Computer Vision · Cross-listed from cs.RO
From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs
Jason Stanley, Zhirui Dai, Qihao Qian et al. (+6 more)
Autonomous flight in cluttered environments requires a
robot to build a
geometric map of its surroundings and plan safe, dynamically feasible trajectories, all onboard and in real time. Conventional approaches treat mapping and planning as separate stages and often rely on binary occupancy for collision checking. We argue that these two stages should be co-designed around a single representation: a signed distance function (SDF). By encoding distance to the nearest obstacle, an SDF provides richer information for planning and trajectory optimization than occupancy alone. We develop an Octree REsidual Network (OREN) that pairs an explicit octree prior with an implicit neural residual to reconstruct SDFs online from point cloud observations with the efficiency of volumetric methods and the accuracy and differentiability of neural methods. In tandem, we develop Bubble$^\star$, a search-based planner that exploits the distance information to grow maximal collision-free balls, which we call bubbles, with formal guarantees of termination, completeness, and failure detection. Planning over a graph of bubbles significantly reduces collision checks compared to a grid-based A$^\star$ search and returns a bubble sequence that forms a safe corridor for trajectory optimization. We demonstrate the integrated OREN-Bubble$^\star$ approach onboard a quadrotor, navigating unseen indoor environments in real time under tight compute constraints. OREN improves SDF estimation by $22$% compared to baselines, while Bubble$^\star$ finds trajectories spanning $\approx 90$ m through a cluttered environment in $1$-$3$ sec., whereas baselines take up to $10$ sec. in the same environment.
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All authors Jason Stanley, Zhirui Dai, Qihao Qian, Tzu-Chin Ho, Tianxing Fan, Siddharth Saha, Christopher Barngrover, Ki Myung Brian Lee, Nikolay Atanasov
Computer Vision
No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation
Feinan Cheng, Dongliang Xu, Wenli Nong et al. (+4 more)
Test-time scaling offers a promising method to improve the inference performance of
Vision-
Language Models (VLMs) without additional training. Existing approaches to
vision-
language navigation (VLN) for Unmanned Aerial Vehicle (
UAV) typically relies on a single inference pass, which can falter in complex environments by producing suboptimal or unsafe trajectories. In this paper, we explore a simple and effective approach to apply test-time scaling to VLN for
UAV. We enhance
navigation reasoning through an iterative refinement process that requires no extra model training, guiding the model to re-evaluate its initial
navigation plan for better accuracy and safety. Our method first
prompts the model to generate multiple parallel candidates and then performs a self-correction step, achieving deeper and more robust planning without changing the underlying model. To further strengthen decision-making, we design a multi-criteria scoring function to evaluate the refined candidates based on safety, goal
alignment, and forward-progress. This simple yet powerful combination enables a frozen
UAV navigation VLMs to self-correct and generate more accurate and reliable flight plans, achieving SOTA performance in this task.
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All authors Feinan Cheng, Dongliang Xu, Wenli Nong, Zhiheng Zhang, Ang Liu, Tianyu Wang, Yue Yao
Computer Vision
EviPathBench: Benchmarking Evidence Acquisition and Reasoning in Vision-Language Models for Whole-Slide Pathology
Dankai Liao, Tianyi Zhang, Yufeng Wu et al. (+6 more)
Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence. However, most pathology
benchmarks evaluate models on pre-cropped patches or pre-extracted slide features, leaving their ability to acquire evidence from gigapixel WSIs largely untested. We introduce EviPathBench, a
benchmark for evaluating evidence acquisition and
reasoning in
vision-
language models (VLMs) for whole-slide pathology. It evaluates four capabilities: image-to-text matching for evidence interpretation, text-to-image
retrieval for evidence verification, diagnostic-region localization for evidence acquisition, and multi-scale
reasoning for evidence integration. The
benchmark is organized as a diagnostic tree linking nested regions across magnifications with scale-specific findings and path-level diagnoses. It contains 1,822 TCGA WSIs and 17,135 diagnostic paths annotated by ten board-certified pathologists. A private cohort of 190 breast cancer WSIs with detailed annotations further evaluates autonomous whole-slide exploration. We evaluate 19 VLMs spanning general-purpose, medical, and pathology-specialized families, plus one text-only reference model. Leading open-weight models achieve over 93% accuracy in multi-scale
reasoning and over 50% in both cross-modal matching tasks. In contrast, diagnostic-region localization remains challenging: the best text-guided mean intersection-over-union is below 0.09, underperforming a center-based heuristic. During autonomous exploration, the unconditional hit rate drops from 0.522 at low magnification to 0.185 at intermediate magnification and 0.020 at high magnification. These results reveal a pronounced gap between
reasoning over curated evidence and acquiring it from WSIs. EviPathBench provides a unified framework for measuring and improving both capabilities.
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All authors Dankai Liao, Tianyi Zhang, Yufeng Wu, Xinyue Zhang, Qiaochu Xue, Zeyu Liu, Dachun Zhao, Linghan Cai, Yueming Jin
Computer Vision · Cross-listed from cs.CL
MeetingToM: Evaluating Multimodal LLMs on Theory-of-Mind Reasoning in Multi-Party Meetings
Ziyi Wang, Yuhang Wu, Dongxu Piao et al. (+3 more)
Theory of Mind (ToM), the ability to infer other's beliefs, intentions, and states of knowledge, is central to social interaction, yet remains challenging for current
Multimodal Large
Language Models (MLLMs), especially in multi-party meetings where cues are distributed across
speech and behavior. Existing
multimodal ToM
benchmarks mainly focus on
video-grounded question answering over overt, externally verifiable signals, and provide limited coverage of latent social states and group dynamics. We introduce MeetingToM, a
benchmark for complex social behavior
reasoning in naturalistic multi-party meetings. MeetingToM targets meeting-specific phenomena such as pseudo-consensus, where apparent agreement masks private dissent under social pressure. The
benchmark is hierarchically organized to evaluate ToM at increasing levels of social granularity, including (i) subject-level mental state prediction, (ii) dyadic-level addressee understanding, and (iii) group-level consensus
reasoning. We provide a unified evaluation protocol and conduct systematic analyses of representative MLLMs, revealing persistent limitations in integrating non-verbal cues, inferring hidden attitudes, and distinguishing genuine consensus from pseudo-consensus. Our results highlight key challenges and establish MeetingToM as a testbed for advancing meeting-grounded ToM in
multimodal models.
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All authors Ziyi Wang, Yuhang Wu, Dongxu Piao, Xingyu Liu, Tianhui Zhou, Miao Liu
Computer Vision
IGGT4D: Streaming 4D Instance-Grounded Geometry Transformer
Zhengyu Zou, Hao Li, Kuixuan Jiao et al. (+7 more)
Real-world spatial intelligence requires
agents to understand scenes from continuous
video streams, where objects move, persist, disappear, and reappear over time. While recent spatial foundation models have enabled generalizable feed-forward 3D reconstruction, most streaming methods remain geometry-centric and lack temporally consistent object-level understanding. Meanwhile, existing semantic reconstruction and 3D-aware
vision-
language methods largely rely on externally extracted 2D semantic cues or loosely coupled geometry inputs, limiting unified geometry-instance learning in long dynamic scenes. In this paper, we propose IGGT4D, a streaming instance-grounded geometry
Transformer for online 4D scene understanding. IGGT4D processes
video frames sequentially, reuses historical context through causal spatial-temporal modeling, and incrementally updates a unified representation of camera motion, geometry, and object identity. This enables long-sequence feed-forward reconstruction with geometry-instance consistency in dynamic environments. To address the lack of high-quality 4D supervision, we further construct InsScene4D-147K, a large-scale
dataset spanning real/synthetic and static/dynamic scenes, with RGB images, depth, poses, and temporally consistent instance masks generated by an automated geometry-guided annotation pipeline. Experiments on 3D reconstruction, pose estimation, instance spatial tracking, and open-vocabulary
segmentation demonstrate that IGGT4D outperforms existing streaming baselines while maintaining scalable online inference for long dynamic sequences.
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All authors Zhengyu Zou, Hao Li, Kuixuan Jiao, Liu Liu, Tingyang Xiao, Xiaolin Zhou, Fangzhou Hong, Zhizhong Su, Dingwen Zhang, Ziwei Liu
Computer Vision
Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography
Andreas W. Aspe, Jonas Jalili Loft, Michael Huy Cuong Pham et al. (+6 more)
Accurate delineation of the pericardium in a cardiac CT scan is essential for quantifying epicardial adipose tissue, yet it remains one of the most challenging structures to segment due to its poor contrast boundaries. Instead of solely relying on image gradients, our framework leverages the anatomical context of surrounding anatomical structures to guide the
segmentation. This work introduces a novel 3D iterative mesh refinement framework that balances anatomical and
geometric forces derived from inherent anatomical rules to refine an initial, possibly ambiguous,
segmentation into a high-precision, anatomically plausible result. Designed as a model-agnostic post-processing step, our method uses a 3D vector field to iteratively push the vertices to the correct anatomical locations. Evaluating the refinement on both a high-resolution in-house
dataset and a coarse, sparsely annotated open-source
dataset, our method consistently improves all volumetric, surface, and anatomical metrics. The framework demonstrates greater improvement when applied to weaker initial
segmentations, highlighting its potential for improving
segmentations for out-of-domain models and in limited-training-data scenarios. The method is formulated as a gradient-based, GPU-accelerated framework that can be easily extended to other anatomical use cases.
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All authors Andreas W. Aspe, Jonas Jalili Loft, Michael Huy Cuong Pham, Andreas Ohrt Johansen, Jørgen Tobias Kühl, Klaus Fuglsang Kofoed, Kristine Aavild Sørensen, Rasmus R. Paulsen, Josefine Vilsbøll Sundgaard
Computer Vision · Cross-listed from cs.RO
Cognitive Dual-Process Planning for Autonomous Driving with Structured Scene Knowledge and Verifiable Reasoning-Action Consistency
Zhongyao Yang, Haoyu Li, Yu Yan et al. (+3 more)
High-level planning for autonomous driving is a knowledge-intensive engineering decision task that requires accurate scene understanding, timely inference, and internally consistent action selection.
Vision-
language models (VLMs) can make intermediate
reasoning explicit, but their use in deployed planners is constrained by costly structured supervision, unnecessary
reasoning in routine scenes, and possible inconsistencies between generated rationales and driving actions. We present a cognitive dual-process planning framework that represents planning-relevant scene knowledge in a machine-parsable structured chain-of-thought (S-CoT) schema. An automated data engine integrates perception foundation models, critical-path filtering, and an expert VLM to generate S-CoT supervision without manual annotation of individual rationales. A lightweight visual Arbiter estimates scene complexity from multilevel
vision-encoder features before
language decoding and routes each input to either fast meta-action prediction or slow structured
reasoning. For slow-path outputs, a deterministic rule-based validator checks whether the parsed S-CoT fields are consistent with the final meta-action and provides verifiable rewards for Group Relative Policy Optimization (GRPO). In a 195-scene manual audit, the generated annotations achieve 91.8% CoT accuracy and a 98.5% Logical Consistency Score (LCS). On 574 manually verified NAVSIM test samples, the planner achieves 80.14% planning accuracy and 97.20% LCS while reducing average latency by 17.39% relative to applying slow
reasoning to every scene. Evaluation on external long-tail subsets further identifies conditions under which routing and planning performance degrade. Together, these results show how explicit scene knowledge can be operationalized through adaptive
reasoning and rule-based verification to support high-level VLM planning decisions.
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All authors Zhongyao Yang, Haoyu Li, Yu Yan, Zhuangxuan Yu, Jiangfeng Nan, Jinrui Nan
Computer Vision
ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU
Fan Jiang, Zhaoxu Sun, Mengchao Wang et al. (+38 more)
We present ABot-World-0, an action-conditioned
video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA games, simulation engines, and internet
videos to learn controllable world dynamics. WorldExplorer performs
agent-driven collection guided by training feedback, while a unified pipeline applies 14 deterministic quality checks, VLM-based assessment, and synchronized action and text annotation. We progressively distill a bidirectional action-conditioned teacher into a causal student through teacher forcing and ODE
distillation, and introduce LongForcing to align long student self-rollouts with an extended-horizon teacher, mitigating accumulated distribution shift and autoregressive drift. Raw keyboard actions provide a unified control interface for scene roaming and third-person character interaction, while reference-character
memory provides persistent appearance cues for identity consistency during third-person rollouts. For deployment, we co-design a streaming inference stack with a lightweight VAE decoder, efficient
attention,
memory-aware scheduling, and low-bit DiT inference. Across optimized low-bit configurations, ABot-World-0 streams 720P
video at up to 16 FPS on a single NVIDIA RTX 5090 desktop GPU, with 1.2s action-to-first-frame latency and approximately 19GiB peak VRAM. Experiments on WorldRoamBench and extended interactive rollouts demonstrate competitive controllability and coherent long-horizon world
evolution.
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All authors Fan Jiang, Zhaoxu Sun, Mengchao Wang, Ziyu Zhu, Chiyu Wang, Yunpeng Zhang, Wenlin Liu, Yun Wang, Xue Zheng, Rui Sun, Junfeng Ni, Hongyu Pan, Zhongxu Sun, Fei Yu, Zengye Ge, Mengmeng Du, Nianfei Fan, Mingchao Sun, Yu Liu, Yongchang, Yanqing Zhu, Jiahang Wang, Ning Ying, Yuze Xuan, Di Yang, Zhicheng Liu, Zhe Gao, Tingbing Xu, Jiacheng Sui, Wenjin Yang, Junnan Lai, Shufeng Liu, Yuan Liu, Zheng Zhou, Yingliang Peng, Dawei Cao, Kaifeng Sheng, Yuxiang Cai, Fei Lu, Mu Xu, Ning Guo
Computer Vision
Point Ladder Tuning: Parameter-Efficient Hierarchical Adaptation for 3D Point Cloud Understanding
Junlin Chang, Longhao Zou and Rui Li
Fine-tuning pre-trained point-cloud backbones typically updates all parameters, resulting in substantial computation and
memory overhead. More importantly, modern point backbones rely on aggressive tokenization and downsampling, which yields compact global
tokens but irreversibly discards fine-grained local geometry, an inherent bottleneck for parameter-efficient adaptation. Consequently, existing PEFT methods that operate only on these coarsened
tokens can modulate global semantics but struggle to recover the missing multi-scale locality. We present Point Ladder Tuning (PLT), a locality-aware PEFT framework that performs hierarchical, instance-conditioned adaptation while keeping the backbone frozen. PLT forms a lightweight closed loop: (i) a Hierarchical Ladder Network (HLN) constructs a multi-resolution local feature pyramid directly from raw points; (ii) a Local-Global Fusion (LGF) aligns and fuses local pyramids with intermediate backbone semantics; and (iii) a Dynamic
Prompt Generator produces instance-aware multi-scale
prompts to modulate the frozen backbone effectively. For dense prediction, we further introduce a lightweight
segmentation head that progressively upsamples fused features and leverages backbone priors to refine fine structures. Extensive experiments on classification and dense prediction show that PLT consistently surpasses prior PEFT baselines with minimal tunable parameters. PLT achieves state-of-the-art performance using only 2.71% trainable parameters for classification and 7.69% for dense prediction, and scales favorably to larger backbones, requiring merely 0.36% parameters on PointGPT-L. The code is released at https://github.com/JunLinChang/ECCV2026-PLT.
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Computer Vision · Cross-listed from cs.CR
Sarus: Privacy-Preserving Multi-Vendor Perception Fusion via Homomorphic Encryption
Munawar Hasan and Apostol Vassilev
Cooperative perception enables autonomous vehicles (AVs) to improve situational awareness by aggregating detection outputs from multiple
agents and sensing platforms, often via a shared fusion service in multi-vendor deployments. However, sharing such outputs at inference time exposes proprietary model behavior and sensitive environmental information, creating significant
privacy and security concerns. In this paper, we present Sarus, a
privacy-preserving framework for multi-vendor perception fusion via homomorphic encryption (HE), enabling aggregation without revealing individual vendor outputs. Each vendor encodes detections as compact Gaussian moment vectors over a shared spatial
lattice and transmits encrypted payloads to a fusion server, which aggregates them directly in the encrypted domain. The fused result is then decrypted and reconstructed into final detections through class-wise bin merging. We analyze the computational complexity, showing linear scaling for vendor payload construction and $O(BV)$ server-side fusion with the number of occupied bins $B$ and vendors $V$, while postprocessing scales as $O(B + \sum_{c\in \mathcal{C}} B_c^2)$, where $\mathcal{C}$ denotes the set of object classes and $B_c$ is the number of occupied bins for class $c$. Experiments demonstrate linear scaling in practice with only a bounded constant-factor overhead from HE, with decryption dominating postprocessing cost. Experiments on the KITTI
dataset using camera (YOLOv8) and
LiDAR (PointPillars, PV-RCNN) detectors show that Sarus improves scene-level coverage by effectively aggregating complementary detections, particularly in distance-dependent regimes where individual modalities degrade. These results indicate that
privacy-preserving multi-vendor perception fusion is feasible for real-time deployment when statistical compression and spatial sparsity are jointly exploited.
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Computer Vision
Maohua Li, Qirui Li, Yanke Zhou et al. (+10 more)
Modern text-to-image
diffusion transformers (DiTs) generate images through joint
attention, in which text and image
tokens interact directly within a single sequence. In large-scale DiTs, the conditioning input contains not only the user
prompt but also chat-template
tokens introduced by LLM-based text encoders. Yet how these
tokens participate in the denoising computation remains poorly understood. To probe this, we introduce a causal interpretability framework. Using it to separate
prompt-content
tokens from chat-template
tokens, we find that the template
tokens carry little
prompt-specific information at the encoder output. Yet surprisingly, they emerge as dominant image-to-text
attention sinks and causally maintain object identity inside the DiT, acting as implicit semantic registers. We show that they acquire this identity indirectly. Rather than reading the
prompt tokens, they draw the identity from the image latents into which the
prompt semantics have already been injected at the very first layer. We further reveal a division of labor across heads and depth in DiTs, where distinct heads route semantics or render visual structure, and identity is committed in early blocks, carried by middle blocks, and refined in late ones. As a practical payoff, this analysis yields a training-free
pruning rule that removes the causally inert
prompt-reading heads and cuts $20\%$ of joint-
attention FLOPs at a $1.4$-point cost in GenEval accuracy. Overall, our work not only reveals that the
tokens encoding semantics at the input need not be those that maintain them during generation, but also provides a causal view of internal mechanisms in
diffusion transformers.
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All authors Maohua Li, Qirui Li, Yanke Zhou, Yiduo Li, Zhaosheng Chi, Chao Xu, Cuifeng Shen, Yixuan Xu, Hanlin Tang, Kan Liu, Tao Lan, Lin Qu, Shao-Qun Zhang
Computer Vision
Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models
Lisa Weijler, Irene Ballester, Guofeng Mei et al. (+2 more)
Geometric foundation models, such as the Visual Geometry Grounded
Transformer (VGGT), provide strong 3D priors from unposed images. However, such models operate purely in a feed-forward, deterministic regime, \ie~they cannot generate plausible geometry beyond what the input views directly support. Generative models for 3D scenes, on the other hand, must rely on strong
geometric priors to produce coherent outputs from sparse inputs. We bridge these two paradigms by performing flow matching directly in VGGT's latent space, leveraging its learned 3D priors without committing to any explicit downstream representation such as Gaussians, meshes, or
video-VAE latents. This requires respecting the latent geometry: VGGT
tokens occupy a product of high-dimensional hyperspheres on which standard Euclidean flow matching fails. We address this with a Riemannian Flow Matching framework defined on a product
manifold of four hyperspheres, aligned with VGGT's multi-scale encoder, which keeps generated
tokens on the valid data
manifold required by the frozen decoding heads. On RealEstate10K, ScanNet++ and ETH3D, our method achieves strong performance against recent scene generation baselines in both per-view appearance and aggregated 3D geometry, establishing latent-space flow matching on
geometric foundation models as a viable paradigm for 3D generation. The project page can be found $\href{https://lisaweijler.github.io/geometry-grounded-rfm/}{\text{here}}$.
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All authors Lisa Weijler, Irene Ballester, Guofeng Mei, Tolga Birdal, Pedro Hermosilla
Computer Vision
CR-Refiner: An Object-Centric Optimal Transport Reranker for Edit-Conditioned 3D Scene Retrieval
Hao Wu, Jinjing Zhu, Nanyu Wu et al. (+4 more)
Edit-conditioned 3D scene
retrieval pairs a reference 3D room with a natural-
language modification and retrieves rooms from a corpus that satisfy the edit. Three lines of prior work each fall short on this task. 2D composed image
retrieval reasons over pixel-level edits and has no primitive for 3D object sets. 3D foundation encoders embed individual objects but cannot compose at the scene level. 3D scene-grounding methods localize references inside a static scene rather than rank modified rooms across a corpus. We present CR-Refiner, a training-free reranker that wraps any base retriever's top-K candidates with three components. A frozen LLM parses the edit into a structured query entity, and each candidate is scored by an unbalanced optimal-transport problem over a 1xG cost
matrix coupling category, style, material, and geometry. The unbalanced solver lets the single-entity query drop mass on irrelevant objects, modelling the asymmetry directly. An axis-conditional structural prior adds size-keyword cues for
geometric edits and subject-anchor direction cues for spatial edits. An LLM verifier refines the top three candidates with continuous confidence. Because no
benchmark evaluates compositional matching over 3D object sets, we additionally release 3D-CER, 4,963 edit-conditioned queries over a 23,381-room indoor corpus across five edit axes, with multi-positive ground truth, CIRR-style hard subsets, and zero-target adversarials. Across three qualitatively distinct base retrievers, CR-Refiner consistently improves hard-subset R@1 and mAP@10 on every edit axis.
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All authors Hao Wu, Jinjing Zhu, Nanyu Wu, Qianyi Cai, Heyi Lin, Hao Wang, Hui Xiong
Computer Vision
GATE-3D: Geometry-Aware Test-time Adaptive Reranking for Open-Set 3D Shape Retrieval
Hao Wu, Heyi Lin, Zilin Wang et al. (+3 more)
Large pretrained
vision models have substantially improved appearance-based 3D shape
retrieval, but they still confuse shapes that look similar while differing in geometry. Although geometry-aware features can reduce these errors, naive fusion of geometry and appearance may hurt
retrieval when the two modalities are already well aligned. We propose GATE-3D, a lightweight query-adaptive reranking method that incorporates geometry without retraining the
retrieval backbone. For each query, GATE-3D predicts how much a geometry-aware score should adjust the appearance-based ranking using features that capture disagreement between the two modalities. This selective design lets geometry contribute where it helps and stay silent where it would hurt. Experiments on three open-set 3D
retrieval benchmarks show that GATE-3D improves over appearance-only
retrieval and is more robust than always-on fusion. On the primary
benchmark, it improves mAP@10 by 2.00 points over appearance-only
retrieval (p=0.041); it also improves leave-one-category-out generalization and reduces
geometric false positives by 10.8%. GATE-3D achieves competitive zero-shot results against DAC-based baselines. We further find that simple linear routing is more effective than a small MLP in the low-data regime, suggesting that cross-modal disagreement features matter more than model capacity for adaptive routing.
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All authors Hao Wu, Heyi Lin, Zilin Wang, Huizai Yao, Hao Wang, Hui Xiong
Computer Vision
FlexiAvatar: Unified 3D Gaussian Human Avatars Under Arbitrary Body Visibility
Yihalem Yimolal Tiruneh, Muhammad Salman Ali, Uyoung Jeong et al. (+5 more)
Reconstructing animatable 3D human avatars from
monocular video is a fundamental problem in computer
vision with broad applications in AR/VR and digital content creation. Existing approaches typically couple parametric body models with neural rendering or 3D Gaussian splatting and optimize all body regions jointly from short
videos, which often degrades fidelity in the visible areas. To overcome this limitation, we introduce FlexiAvatar, a unified framework that explicitly optimizes only the visible body regions, effectively eliminating artifacts arising from unobserved limbs. Our method integrates occlusion-robust SMPL-X tracking with part-specific residual refinement to capture high-frequency
geometric and appearance details. To complete entirely unseen regions (e.g., back views), we leverage a
diffusion-based approach to generate texture consistent with the observed appearance. Experiments on full-body (NeuMan, ZJU-MoCap, WildAvatar), upper/half-body (talk-show clips), and head-only (INSTA) inputs show that FlexiAvatar delivers consistently higher reconstruction quality, outperforming state-of-the-art methods by an average PSNR improvement of approximately 3% across
datasets. Finally, by restricting optimization to observed regions, our method reduces the effective number of Gaussians that must be optimized and rendered, leading to reduced runtime and
memory overhead in partial-visibility scenarios.
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All authors Yihalem Yimolal Tiruneh, Muhammad Salman Ali, Uyoung Jeong, Muneeb A. Khan, MD Khalequzzaman Chowdhury Sayem, Allanur Bayramgeldiyev, Binod Bhattarai, Seungryul Baek
Computer Vision
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Joy Dhar, Manish Kumar Pandey, Nayyar Zaidi et al. (+4 more)
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges. First, they struggle to capture complex cross-modal interactions effectively, which in turn limits performance improvements. Second, they incur high computational costs, restricting their applicability in resource-constrained healthcare AI applications. Finally, they are often designed and evaluated for narrow, fixed modality configurations (e.g., imaging-only, or specific pairs such as image and omics), which limits evidence of their adaptability and generalizability to broader collections of heterogeneous medical modalities. To address these challenges, we propose a novel MFL framework - Cascaded Unified Representation Learning for Efficient Fusion Network (CURE) - a lightweight and scalable framework that progressively integrates various modalities through a novel efficient Hybrid Geometry Aware Fusion layer (HyFuse), where each HyFuse layer is sequentially learned for each modality, making the framework adaptable and generalizable. Within HyFuse, an efficient residual convolution module captures rich multi-scale features to ensure cost-effective learning, while a hybrid-space aware
attention mixer learns coarse-to-fine structural cues to better preserve cross-modal relationships. Complementary learnable late-fusion and shared information refinement modules are then employed to learn robust modality-order-invariant shared representations, which in turn yields consistent performance improvements. Extensive evaluations on 16 public
datasets show that CURE outperforms leading
multimodal fusion methods, boosting performance by up to 3.97% and lowering computational costs by up to 87.8%, ensuring more effective and reliable predictions.
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All authors Joy Dhar, Manish Kumar Pandey, Nayyar Zaidi, Chen Chen, Maryam Haghighat, Ferdous Sohel, Puneet Goyal
Computer Vision · Cross-listed from eess.IV
Wavefront Parallelization for Efficient Learned Image Compression
Shimon Murai, Fangzheng Lin, Kasidis Arunruangsirilert et al. (+1 more)
Autoregressive context models are foundational for learned image compression,but they suffer from slow serial inference. Existing acceleration methods such as checkerboard context require architectural changes and retraining, thus are inapplicable to pre-trained models. We propose a completely training-free inference-time acceleration
algorithm inspired by wavefront parallelism in
video coding standards. Our method reorganizes inference into an optimal “staggered” wavefront order, minimizing sequential steps while maintaining exact autoregressive dependencies. Experimental results show our approach accelerates pre-trained autoregressive models (e.g., Cheng et al.) by more than $13\times$ while preserving the original rate-distortion performance. We also demonstrate that faster decoding is possible by trading off precise context dependencies. Source code will be available at https://github.com/tokkiwa/compressai-wavefront.
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All authors Shimon Murai, Fangzheng Lin, Kasidis Arunruangsirilert, Jiro Katto
Computer Vision
Context-structured Video Anomaly Detection with Large Vision-Language Models
Dongjun Kim, Changjae Oh, Andrea Cavallaro et al. (+1 more)
Training
video anomaly detectors is challenging due to the difficulty and cost of annotating diverse and rare abnormal events. Although recent large
vision-
language models enable training-free inference, existing approaches mostly rely on holistic inference over sampled
video and may miss context-specific anomaly cues. In this paper, we present CSI-VAD, a training-free
video anomaly detector that identifies abnormal events across diverse contexts. The key idea is to decompose each
video into three distinct contexts (environment, objects, time) and perform context-specific inference in separate branches. Because we ground anomaly judgments solely in context-specific visual cues, we do not require predefined text
prompts describing abnormal events or
dataset-specific tuning. Experiments on UCF-Crime and UBnormal show that CSI-VAD consistently improves over the direct holistic baseline and achieves competitive performance against existing methods, showing the advantage of structured context decomposition for training-free
video anomaly detection.
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All authors Dongjun Kim, Changjae Oh, Andrea Cavallaro, Jeonghoon Mo
Computer Vision
Delineate Anything v2: A Global Foundation Model for Field Delineation
Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov et al. (+4 more)
Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounting. While
vision foundation models like SAM show remarkable zero-shot capabilities, they frequently fail in geospatial domains due to topological complexity, cropland texturing patterns, and a lack of physical scale awareness. In this work, we introduce Delineate Anything v2, a globally scalable foundation model designed specifically for wide-area field boundary mapping. We construct FBIS-73M, a 73-million-instance multi-resolution
dataset spanning 61 countries. To address the pervasive issue of multi-field administrative parcel merging, we introduce a resolution-specific data curation pipeline that leverages topological image-space adaptation to homogenize merged parcels and strengthen weak physical boundaries. Furthermore, we establish a novel, manually curated evaluation
benchmark covering 100 countries to assess independent zero-shot generalization. Our results show that Delineate Anything v2 surpasses the current state-of-the-art, including the Delineate Anything framework, by 0.284 mAP@0.5 (+103.3% relative gain), while maintaining execution speeds suitable for rapid national- and global-scale deployment, as demonstrated by nationwide mapping of Ukraine (603,000 km^2) in 5.4 hours on a consumer-grade workstation. Code, pre-trained weights, the FBIS-73M
dataset, and ready-to-use national-scale vector boundary products are publicly available at https://lavreniuk.github.io/Delineate-Anything/.
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All authors Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov, Yevhenii Salii, Volodymyr Kuzin, Charlotte Julia Li-Xing Wang, Zoltan Szantoi
Computer Vision
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
Xinjie Zhang, Peng Zhang, Shicheng Zheng et al. (+21 more)
Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. The stack is built from two co-designed components: Mage-VAE, a lightweight high-fidelity latent tokenizer, and a Native-Resolution
Multimodal Diffusion Transformer trained with rectified flow matching. Mage-VAE uses one-step
diffusion-style encoding and decoding with anchor-latent regularization, preserving the reconstruction quality of strong public VAEs while reducing tokenization cost by more than an order of magnitude. Together with native-resolution packing and stack-level CUDA kernel fusion, the stack supports flexible-resolution training and improves end-to-end training throughput by about $2.5\times$. Built on this foundation, we develop a complete model family with Base, RL-aligned, and Turbo variants for both generation and editing.
Diffusion-NFT improves
prompt following, text rendering, aesthetic quality, and editing fidelity, while few-step
distillation with
adversarial perceptual guidance produces 4-step Turbo models for low-latency inference. Despite its compact scale, Mage-Flow and Mage-Flow-Edit achieves competitive performance across standard generation and editing
benchmarks. More importantly, the Turbo variants make high-resolution generation and editing practical for interactive use: at $1024^2$ resolution on a single NVIDIA A100 GPU, Mage-Flow-Turbo generates an image in 0.59s, and Mage-Flow-Edit-Turbo edits an image in 1.02s, while maintaining a small
memory footprint. These results show that careful tokenizer–backbone–system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family.
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All authors Xinjie Zhang, Peng Zhang, Shicheng Zheng, Jinghao Guo, Zhaoyang Jia, Yifei Shen, Xun Guo, Yuxuan Luo, Jiahao Li, Wenxuan Xie, Fanyi Pu, Xiaoyi Zhang, Kaichen Zhang, Zongyu Guo, Tianci Bi, Dongnan Gui, Zhening Liu, Zimo Wen, Zihan Zheng, Senqiao Yang, Xiao Li, Jinglu Wang, Bin Li, Yan Lu
Computer Vision
Now You See the Hate: Adaptive View Retrieval for Hidden Hateful Illusions
Qianpu Chen and Derya Soydaner
Hateful optical illusions expose a serious gap in current
multimodal safety systems. On original-view hateful illusions, previous work shows that six moderation classifiers achieve at most 20.9 to 24.5% accuracy and nine state-of-the-art VLMs remain at or below 10.2% with illusion-aware prompting, leaving most hidden hate undetected. We formulate hidden hateful illusion detection as a perceptual
retrieval problem and propose Adaptive View
Retrieval. This retrieve-and-calibrate framework assembles a complementary view bank for the image and hidden-message templates, adaptively selects which views to trust, retrieves hidden-message identities, and calibrates whether the recovered evidence is harmful. On HatefulIllusion with a frozen CLIP encoder, Adaptive View
Retrieval reaches 93.2% balanced accuracy on the held-out test split. It substantially outperforms original-view baselines and fixed single-transform filters across hate slangs, hate symbols, and visibility levels. The same design also surpasses official fine-tuned CLIP baselines, matches or exceeds human performance on IllusionMNIST, IllusionFashionMNIST, and IllusionAnimals, and outperforms zoom-out preprocessing on HC-Bench under the SemVink protocol. Together, these results show that robust
multimodal moderation requires recovering hidden meaning before deciding whether it is harmful.
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Computer Vision
Jiacheng Ruan, Jun Tang, Wenzhen Yuan et al. (+5 more)
On-policy
distillation (OPD) trains a student model on trajectories sampled from its own policy, providing dense
token-level supervision by minimizing the divergence between the teacher's and student's output distributions at each
token. Although existing OPD approaches effectively distill strong
reasoning capabilities into student models, they inherently inherit uncurated chain-of-thought traces, exacerbating overthinking and
reasoning redundancy. To address this limitation, we introduce COPD, a contrastive OPD framework. Specifically, for each
token generated by the student, a frozen teacher evaluates the current student state under two contrasting
prompts that induce low and high
reasoning effort, respectively. The resulting difference in log-probabilities serves as a
token-level advantage signal to guide the policy update. Rather than merely mimicking a single teacher distribution, COPD guides the student model toward acquiring concise and efficient
reasoning strategies. We evaluate COPD across 9
multimodal benchmarks covering both
reasoning and understanding tasks. Empirical results show that COPD substantially reduces
reasoning length without hurting task performance, consistently improving efficiency across different tasks and model scales. In addition, this contrastive paradigm extends naturally to on-policy self-
distillation (OPSD), establishing self-contrastive supervisory signals that enable a single model to compress its own
reasoning without an external teacher.
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All authors Jiacheng Ruan, Jun Tang, Wenzhen Yuan, Ting Liu, Shuai Bai, Dayiheng Liu, Zhibo Yang, Yuzhuo Fu
Computer Vision
Gaze-DETR: Top-Down Guidance Through Priority Maps for Infrared Weak-Small UAV Detection with DETR
Nian Liu, Yuxin Yang, Shubo Lin et al. (+6 more)
Infrared small target detection (ISTD) remains challenging because tiny, low-contrast targets are easily overwhelmed by clutter, noise, or occlusion. Conventional single-frame and multi-frame detectors rely on bounding-box supervision, which specifies final target locations but offers little explicit guidance for prioritizing candidate regions or preserving weak-target evidence before localization. Task-driven visual search offers such guidance: top-down goals and visual evidence jointly form a spatial priority map that ranks candidate locations. Building on this principle, we propose Gaze-DETR, a bio-inspired detector that learns an internal priority map before localization. First, a priority head predicts a normalized priority map from image features. Second, Residual Priority-Guided Feature Modulation (RPFM) enhances high-priority responses while retaining multi-scale features. Finally, Priority-Guided Anchor Query Injection (PAQI) converts high-priority locations into decoder anchor queries. We train the priority head using three supervision schemes: box-derived Gaussian maps; real-gaze maps constructed from fixation-density maps; and transferred pseudo-gaze maps learned from gaze–box relations in paired annotations and applied to Anti-
UAV410 training boxes. To support the latter two schemes, we construct TIR-
UAV120-Gaze with paired detection and task-driven eye-tracking annotations. On TIR-
UAV120-Gaze, Gaze-DETR achieves 85.76 mAP$_{50}$ and 88.77 F1 with box-derived supervision, and 86.18 mAP$_{50}$ and 89.00 F1 with real-gaze supervision. On Anti-
UAV410, it achieves 87.06 mAP$_{50}$ and 90.90 F1 with box-derived supervision, and 87.08 mAP$_{50}$ and 90.43 F1 with transferred pseudo-gaze supervision. These results show that explicit spatial-priority learning provides pre-localization guidance complementary to bounding-box supervision across annotation settings and costs.
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All authors Nian Liu, Yuxin Yang, Shubo Lin, Sikui Zhang, Liang Li, Boyu Cai, Yizheng Wang, Weiming Hu, Jin Gao
Computer Vision
FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling
Jialong Zuo, Haotong Zuo, Shiwei Zhang et al. (+5 more)
Translating novels into films poses a grand challenge for generative artificial intelligence, requiring conversion of abstract literary prose into long-form, multi-scene visual narratives. While current
video generation models excel at short, single-scene clips within narrow temporal and spatial contexts, novel-to-film generation operates in a more complex regime, demanding long-duration content across diverse scenes with dynamically evolving entity states. To address this, we formalize novel-to-film generation as dynamic cinematic world modeling, decomposed into two phases: construction, which grounds abstract, underspecified literary narratives into concrete, stateful, and persistent world entities; and
evolution, which governs how these entities dynamically update under plot progression to maintain causal consistency across scenes. We propose FilmWorld, an end-to-end agentic system where two groups of specialized
agents collaborate to instantiate these phases. Construction-side
agents perform narrative structured translation, world entity state modeling with visual anchoring, and state-driven shot planning, progressively projecting literary
language into a cinematic blueprint.
Evolution-side
agents perform state-anchored visual generation, cross-shot dynamic state propagation, and closed-loop state verification to maintain causal consistency and visual coherence. To address the evaluation gap in long-form generation, we introduce FilmEval, a systematic evaluation framework that couples a difficulty-graded
benchmark of 15 representative novels with an automated protocol of nine objective metrics spanning three dimensions: cinematic presentation, film consistency, and novel fidelity. Experiments demonstrate that FilmWorld consistently outperforms state-of-the-art
video generation
agent systems, with particularly pronounced improvements in narrative fidelity and cross-scene consistency.
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All authors Jialong Zuo, Haotong Zuo, Shiwei Zhang, Xiang Wang, Chen Li, Nong Sang, Changxin Gao, Xiang Bai
Computer Vision
CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement
Zhihao Yang, Zhiyu Xiang, Peng Xu et al. (+5 more)
V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from multiple collaborative
agents. However, existing mainstream V2X perception methods mainly focus on 2D BEV object detection. When 3D detection task is concerned, inferior results are obtained because they ignore the 3D spatial misalignment caused by differing height and attitude among the collaborators. In this paper, we propose a novel collaborative 3D object detection framework called CoGoal3D, which extracts and refines the 3D feature gradually in a two-stage pipeline. In the first stage, a multiscale 3D-aware global fusion module is designed to mitigate the 3D spatial misalignment. The resulting proposals are then refined in the second stage with an auxiliary task of 3D point reconstruction. An effective multi-
agent collaborative data augmentation strategy is further proposed to enrich the training data while minimizing information loss. Extensive experiments on public real-world
datasets demonstrate that our CoGoal3D achieves new state-of-the-art performance, with 3D AP@0.7 improvements of 10.86%, 10.34%, and 10.18% on the DAIR-V2X, V2V4Real, and V2X-Real
datasets, respectively. Code is available at https://github.com/Megalo-f/CoGoal3D.
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All authors Zhihao Yang, Zhiyu Xiang, Peng Xu, Tianyu Pu, Kai Wang, Eryun Liu, Dongping Zhang, Yong Ding
Computer Vision
IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion
Lei Hu
Existing Multi-view Anomaly Detection (MAD) methods assume that all views are completely available and model each view separately. However, in real industrial scenarios, information in the view may be missing due to faults such as occlusion, which leads to the performance degradation of existing methods due to the lack of a multi-view consistency prior. To address this, we explored a more challenging task: Incomplete Multi-View Anomaly Detection (IMVAD), in which some areas of each view were masked. We proposed a pipeline for automatically generating the IMVAD
dataset and generated the RIMAD
dataset based on the Real-IAD
dataset through this pipeline. In addition, in order to effectively utilize the information of multiple views in the absence of view information, we propose IMMoE, which consists of two key modules: (1) Multi-View Expert Fusion (MVEF) effectively fuses multi-view information through a multi-view expert network and guides the reconstruction of a single view; (2) Local Anomaly Enhancement Encoder (LAEE) effectively prevents the model from overfitting the mask region by applying dropout to local features. Our method achieves state-of-the-art performance on both the RIMAD and Real-IAD
datasets, especially on RIMAD, we have increased the pixel-level and image-level metrics by 11.8% and 2.8%, respectively. Our source code is available at https://github.com/HULEI7/IMMoE
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Computer Vision
Mitigating Modality and Language-Style Gaps for Zero-Shot Video Moment Retrieval
Jihyun Lee, Cheol-Ho Cho, Woojin Jun et al. (+2 more)
Zero-shot
video moment
retrieval aims to overcome the limitations of traditional approaches that require large-scale
datasets annotated with text and its relevant temporal spans. Despite advances in pre-trained
vision-
language models and
multimodal large
language models, existing ZMR methods still heavily depend on query-to-
video content similarity, making them vulnerable to modality and
language-style gaps. These gaps lead to unreliable span proposals and unstable moment
retrieval results. To address this issue, we propose Self-Similarity-based Moment Proposal and Scoring that instead exploits intrinsic relationships within
videos, enabling robust span generation and scoring. By deriving self-similarity only from the
video content, we circumvent the noisy and mismatched patterns of query-frame or query-caption similarities, thereby mitigating both modality and
language-style gaps. Furthermore, we introduce a query-aware MLLM-based
reasoning stage to further sharpen
alignment between text and
video. Extensive experiments demonstrate that Self-SiMS achieves state-of-the-art performance across ZMR
benchmarks.
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All authors Jihyun Lee, Cheol-Ho Cho, Woojin Jun, Woojin Jeong, Jae-Pil Heo
Computer Vision
Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillation
Jiuhe Qu, Yingping Liang and Ying Fu
Change detection aims to identify semantic changes between remote sensing images. However, features from models are easily disturbed by non-semantic variations, such as illumination, shadows, and atmospheric changes, leading to false alarms and limited generalization in real-world scenarios. In this paper, we propose SCDistill, a framework for learning semantic-robust change detection via semantic-invariant self-
distillation. First, to strengthen semantic consistency, we introduce a semantic-invariant self-
distillation strategy that learns semantic robustness from perturbed yet semantically consistent data, empowering the change detector to extract disturbance-resistant features and achieve more reliable and accurate semantic change identification. Second, to expand paired data with non-semantic variations, we design a
diffusion-based perturbation simulation pipeline that synthesizes complex environmental changes, enabling the model to explicitly learn to distinguish semantic changes from appearance-level fluctuations and reduce false alarms caused by non-semantic disturbances. These components promote robustness from data and representation perspectives, leading to synergistic performance gains. Extensive experiments demonstrate that SCDistill achieves state-of-the-art performance on multiple semantic change detection
benchmarks and exhibits strong generalization to binary change detection and change captioning tasks. Code is accessible at https://github.com/elecreak/SCDistill.
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Computer Vision
Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction
Tianyang Huang, Alessio Lombardi, Ahmed Elnagar et al. (+7 more)
Information Extraction (IE) from Architecture, Engineering, and Construction (AEC) drawings remains hindered by manual inefficiency, while Layout Detection, a vital 'middleware' organizing graphical and textual hierarchies, is underexplored. General document layout models, optimized for text-centric content, lack validation on engineering drawings. This study constructs a custom AEC-specific layouts
dataset and
benchmarks five deep learning architectures. RF-DETR achieves state-of-the-art performance with an $mAP_{50}$ of 0.949, while the
Vision-
Language Model Qwen3-VL attains a leading F1-score of 0.911. Conversely, models pre-trained on general document
datasets suffer from "domain interference", causing performance degradation. This establishes a robust technical foundation for automated IE in AEC.
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All authors Tianyang Huang, Alessio Lombardi, Ahmed Elnagar, Ahmed Zalouk, George Paul, Sepehr Najjarpour, Arvid Sigurdsson, Khalid Ismail, Mohamed Ragab, Edlira Vakaj
Computer Vision
SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling
Federico Carrara, Aman Kukde, Melisande Croft et al. (+2 more)
SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distributions from which solutions are sampled at inference time. Tiled predictions are unavoidable for large image data, and artifacts arise whenever tiles are smaller than a network's receptive field and when tiles are independent posterior samples. SWITi averages overlapping sliding-window predictions, so discrepancies between neighboring samples are spread across shifted tile positions rather than accumulating at fixed seam coordinates. For posterior models, SWITi uses no more tile samples than an MMSE estimate requires and therefore incurs no additional forward passes. Additionally, we introduce two reference-free metrics, the Fraction of Rejected Tests (FRT) and Artifact Severity (ASV), for detecting and quantifying tiling artifacts from a per-tile permutation test that compares the distribution of pixel gradients across tile seams against the surrounding image content. On pre-trained and published image splitting models across three fluorescence microscopy
datasets in 2D and 3D, we show that SWITi substantially attenuates stitching seams while also improving reconstruction fidelity and resolution. Since tiling artifacts in posterior predictions can easily be mistaken for biological structures or for boundaries between biological structures, removing or reducing them using SWITi will improve the downstream processing of large image predictions, which is particularly relevant for biomedical data.
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All authors Federico Carrara, Aman Kukde, Melisande Croft, Joran Deschamps, Florian Jug