Computer Vision
IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation
Yuqi Wu, Tianyu Hu, Wenzhao Zheng et al. (+4 more)
Reconstructing coherent 3D geometry and appearance from unposed multi-view images is a fundamental yet challenging problem in computer
vision. Most existing visual geometry foundation models predict explicit geometry by regressing pixel-aligned pointmaps, often suffering from redundancy and limited
geometric continuity. We propose IVGT, an Implicit Visual Geometry
Transformer that implicitly models continuous and coherent geometry from pose-free multi-view images. This formulation learns a continuous neural scene representation in a canonical coordinate system and supports continuous spatial queries at any 3D positions, retrieving local features to predict signed distance (SDF) values and colors using lightweight decoders. It allows direct extraction of continuous and coherent surface geometry, enabling rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints. We train IVGT via multi-
dataset joint optimization with 2D supervision and 3D
geometric regularization. IVGT demonstrates generalization across scenes and achieves strong performance on various tasks, including mesh and point cloud reconstruction, novel view synthesis, depth and surface normal estimation, and camera pose estimation.
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All authors Yuqi Wu, Tianyu Hu, Wenzhao Zheng, Yuanhui Huang, Haowen Sun, Jie Zhou, Jiwen Lu
Computer Vision
Offline Semantic Guidance for Efficient Vision-Language-Action Policy Distillation
Jin Shi, Brady Zhang and Yishun Lu
Billion-parameter
Vision-
Language-Action (VLA) policies have recently shown impressive performance in robotic manipulation, yet their size and inference cost remain major obstacles for real-time closed-loop control. We introduce VLA-AD, a
distillation framework that uses a
Vision-
Language Model as an offline semantic supervisor to transfer large VLA teachers into lightweight student policies. Instead of relying only on low-level action imitation, VLA-AD augments teacher-provided 7-DoF action targets with high-level semantic guidance, including task phase anchors and multi-frame operating-direction descriptions. These auxiliary signals are used only during training: at test time, the student policy runs independently, with neither the VLA teacher nor the VLM required. We evaluate VLA-AD on three LIBERO
benchmark suites. Using OpenVLA-7B as the teacher, our method produces a 158M-parameter student, yielding a $44\times$ reduction in model size while matching the teacher with only a $0.27\%$ average relative gap. The resulting policy runs at 12.5 Hz on an RTX 4090, achieving a $3.28\times$ inference speedup over OpenVLA-7B. We further show that the same semantic
distillation pipeline generalizes to a different $\pi_{0.5}$-4B teacher, where the student outperforms the teacher on two suites and remains within $0.53\%$ on libero\_goal. Additional analysis indicates that phase-level supervision and multi-frame directional cues make the student less sensitive to noisy teacher actions, such as erroneous high-frequency gripper changes. Overall, VLA-AD demonstrates that offline semantic guidance from VLMs can substantially improve the efficiency, robustness, and deployability of VLA policy
distillation.
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Computer Vision · Cross-listed from cs.GR
Evaluating Design Video Generation: Metrics for Compositional Fidelity
Adrienne Deganutti, Dingning Cao, Jaejung Seol et al. (+2 more)
Generative
video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain. Unlike natural
video generation, design animation imposes structured constraints: specific components shall animate with prescribed motion types, directions, speed and timing, while non-animated regions must remain stable and layout structure must be preserved. This paper provides a fully automated evaluation framework organized across four dimensions: layout fidelity, motion correctness, temporal quality, and content fidelity. This eliminates the reliance on subjective human evaluation and establishes a common basis for benchmarking progress in the field. We release the code and
dataset here: https://github.com/purvanshi/lica-bench.
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All authors Adrienne Deganutti, Dingning Cao, Jaejung Seol, Elad Hirsch, Purvanshi Mehta
Computer Vision
MAgSeg: Segmentation of Agricultural Landscapes in High-Resolution Satellite Imagery using Multimodal Large Language Models
Piyush Tiwary, Utkarsh Ahuja, Depanshu Sani et al. (+5 more)
Agricultural landscape
segmentation in the Global South is challenging as it is characterized by fragmented plots, high intra-class variance, and a scarcity of labeled training data. Recent advances in
segmentation have been made by
Multimodal Large
Language Models (MLLMs). However, current approaches encounter critical context length bottlenecks and a domain
alignment gap in understanding satellite features. We address these limitations through MAgSeg, a novel, decoder-free MLLM
segmentation approach. MAgSeg is an architecturally efficient approach that enables standard MLLMs to perform
segmentation of complex smallholder agricultural landscapes from high-resolution satellite imagery, without requiring auxiliary
vision decoders. We introduce a novel instruction tuning data format designed to enable scalable fine-tuning and post-training on high resolution satellite imagery, which enables MAgSeg to learn from the global context of the image while generating text
tokens for only a patch within the image. Extensive evaluations on
datasets spanning three countries in the Global South demonstrate that MAgSeg significantly outperforms state-of-the-art MLLM baselines, offering a scalable solution to map smallholder agricultural environments.
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All authors Piyush Tiwary, Utkarsh Ahuja, Depanshu Sani, Aishwarya Jayagopal, Sagar Gubbi, Subhashini Venugopalan, Alok Talekar, Vaibhav Rajan
Computer Vision
Res$^2$CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment
Xinyue Liu, Jianyuan Wang, Biao Leng et al. (+1 more)
Few-shot Generalist Anomaly Detection requires models to generalize to novel categories without retraining, posing significant challenges in real-world scenarios with scarce samples and rapidly changing categories. Existing CLIP-based methods face two major challenges: coarse-grained unified text
prompts struggle to adapt to fine-grained foreground-background differences, causing cross-granularity mismatch; and fine-tuning on auxiliary
datasets disrupts CLIP's inherent open-world generalization due to domain shift, leading to cross-category generalization degradation. To address these, we propose to shift
multimodal alignment entirely into a unified residual space, where residual representations naturally eliminate fine-grained normal feature differences across regions and class-specific biases, simultaneously resolving both problems. Based on this insight, Res$^2$CLIP, the first residual-to-residual
alignment framework that symmetrically bridges visual and text modalities within CLIP's residual space, is designed. The framework is developed from a residual perspective into three branches: a text
prompt-based branch, a visual
prompt-based branch, and a novel residual-to-residual
alignment branch. All learnable optimizations are constrained within the residual domain, and the residual
alignment optimization objectives are designed to force the model to focus on relative anomaly deviations rather than optimizing class-specific features. Experiments on multiple
datasets demonstrate the effectiveness of our architecture. The code is available at https://github.com/hito2448/Res2CLIP.
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All authors Xinyue Liu, Jianyuan Wang, Biao Leng, Shuo Zhang
Computer Vision
Second-Order Multi-Level Variance Correction for Modality Competition in Multimodal Models
Yishun Lu and Wes Armour
Autoregressive next-
token training offers a unified formulation for image generation and text understanding, but it also creates strong modality competition that destabilizes optimization and limits large-batch scaling. We show that first-order optimizers such as AdamW are vulnerable to cross-modality gradient heterogeneity, while second-order preconditioning, particularly SOAP, provides a more stable basis for
multimodal alignment. Building on this insight, we propose ML-FOP-SOAP, a second-order optimization framework with Multi-Level Variance Correction. Our Fisher-Orthogonal Projection suppresses variance-induced modality conflicts, reducing the trade-off between visual generation and textual understanding. To make this practical under large gradient accumulation, we introduce a hierarchical folding strategy that captures fine-grained variance with low micro-step overhead. Experiments on Janus and Emu3 show consistent gains across both modalities and stable training at batch size 8192. Compared with AdamW, our method improves sample efficiency by up to $1.4\times$ and accelerates wall-clock training by up to $1.5\times$, offering a robust optimizer for scaling
multimodal foundation models.
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Computer Vision
Nikita Starodubcev, Ilia Sudakov, Ilya Drobyshevskiy et al. (+2 more)
Vision Transformers (ViTs) are known to exhibit high-norm patch-
token outliers that degrade feature map quality, a problem effectively mitigated by register
tokens. As
diffusion models increasingly adopt
transformer architectures and move toward pixel-space training, they become closer in form to ViTs, raising the question of whether register
tokens are also useful for
Diffusion Transformers (DiTs). In this work, we show that DiTs differ from ViTs in a key respect: they do not exhibit patch-
token outliers but still benefit from registers. Interestingly, registers are more effective in pixel-space DiTs than in latent-space DiTs. By analyzing intermediate representations, we find that register
tokens produce cleaner feature maps at high noise levels, which may contribute to their effectiveness in pixel-space generation. We further observe that recent pixel-space DiT architectures implicitly incorporate register-like mechanisms, which may partially account for their strong empirical performance. Motivated by these observations, we propose Register Guidance, a technique that amplifies the contribution of register
tokens responsible for improving visual structure and coherence.
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All authors Nikita Starodubcev, Ilia Sudakov, Ilya Drobyshevskiy, Artem Babenko, Dmitry Baranchuk
Computer Vision
STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics-Physics Dual System
Zhen Luo, Yixuan Yang, Xudong Xu et al. (+5 more)
Generating simulation-ready tabletop scenes from task instructions is an intriguing and promising research direction in the field of
Embodied AI. However, existing task-to-scene generation methods rely exclusively on large
language models (LLMs) to predict scene layouts, inevitably yielding object collisions or floating due to LLMs' inherent limitations in 3D spatial
reasoning. In this paper, we present STABLE, a semantics-physics dual-system tailored for simulation-ready tabletop scene generation. STABLE consists of two complementary modules: (i) a Semantic Reasoner, a fine-tuned LLM trained on a structured tabletop scene
dataset to generate coarse layouts from input task instructions, and (ii) a Physics Corrector, a physics-aware flow-based denoising model that outputs pose updates to refine layouts, which ensures the physical plausibility of scenes while preserves semantic
alignment with task instructions. STABLE adopts a progressive generation paradigm: by alternating between the Semantic Reasoner and Physics Corrector, it incrementally expands the scene from task-critical objects to background objects. Experiments demonstrate that STABLE successfully generates simulation-ready tabletop scenes that strictly conform to task instructions and significantly enhances the physical validity of scenes over prior art.
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All authors Zhen Luo, Yixuan Yang, Xudong Xu, Jinkun Hao, Zhaoyang Lyu, Feng Zheng, Jiangmiao Pang, Yanwei Fu
Computer Vision
WeatherOcc3D: VLM-Assisted Adverse Weather Aware 3D Semantic Occupancy Prediction
A. Enes Doruk, Abdelaziz Hussein and Hasan F. Ates
While multi-modal 3D semantic occupancy prediction typically enhances robustness by fusing camera and
LiDAR inputs, its effectiveness is fundamentally constrained by environmental variability. Specifically, camera sensors suffer from severe low-light degradation, while
LiDAR sensors encounter significant backscatter noise during heavy precipitation. These adverse conditions create a modality trust problem, as static fusion strategies fail to adaptively re-weight inputs when a specific sensor becomes unreliable. To address this, we propose a VLM-assisted framework leveraging the pre-trained CLIP latent space to guide multi-sensor integration via linguistic environmental cues. We utilize a parameter-efficient adapter to align weather-specific text
embeddings with sensor features, coupled with a gating strategy that decomposes environmental uncertainty into two factors: visibility and illumination. This enables the model to dynamically modulate the fusion ratio - prioritizing semantic camera features in clear daylight and shifting to
geometric LiDAR priors during rainy nights. Evaluations on the nuScenes
dataset demonstrate the versatility of our approach, as implementing our proposed framework on the OccMamba and M-CONet architectures achieves mIoU scores of 26.3 and 21.1, respectively, significantly outperforming their traditional baselines.
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Computer Vision
GenShield: Unified Detection and Artifact Correction for AI-Generated Images
Zhipei Xu, Xuanyu Zhang, Youmin Xu et al. (+5 more)
Diffusion-based image synthesis has made AI-generated images (AIGI) increasingly photorealistic, raising urgent concerns about authenticity in applications such as misinformation detection, digital forensics, and content moderation. Despite the substantial advances in AIGI detection, how to correct detected AI-generated images with visible artifacts and restore realistic appearance remains largely underexplored. Moreover, few existing work has established the connection between AIGI detection and artifact correction. To fill this gap, we propose GenShield, a unified autoregressive framework that jointly performs explainable AIGI detection and controllable artifact correction in a closed loop from diagnosis to restoration, revealing a mutually reinforcing relationship between these two tasks. We further introduce a Visual Chain-of-Thought based curriculum learning strategy that enables self-explained, multi-step “diagnose-then-repair” correction with an explicit stopping criterion. A high-quality
dataset with large-scale “artifact-restored” pairs is also constructed alongside a unified evaluation pipeline. Extensive experiments on our correction
benchmark and mainstream AIGI detection
benchmarks demonstrate state-of-the-art performance and strong generalization of our method. The code is available at https://github.com/zhipeixu/GenShield.
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All authors Zhipei Xu, Xuanyu Zhang, Youmin Xu, Qing Huang, Shen Chen, Taiping Yao, Shouhong Ding, Jian Zhang
Computer Vision · Cross-listed from cs.CR
A Cross-Modal Prompt Injection Attack against Large Vision-Language Models with Image-Only Perturbation
Hao Yang, Zhuo Ma, Yang Liu et al. (+3 more)
Large
vision-
language models (LVLMs) have emerged as a powerful paradigm for
multimodal intelligence, but their growing deployment also expands the attack surface of
prompt injection. Despite this growing concern, existing attacks still suffer from a critical limitation: the injected
prompt for one modality only steers the model's interpretation of that singular input. Alternatively, these attacks remain
multimodal but fail to achieve cross-modal
prompt perturbation. To bridge this gap, we introduce a novel cross-modal
prompt injection attack CrossMPI, which can steer the model's interpretation of both textual and visual inputs via image-only
prompt injection. Our design is underpinned by the following key breakthroughs. First, we turn the focus of the injected
prompt perturbation optimization from the visual
embedding space (typically with only $10^5$ parameters) to the model hidden state space (for
multimodal information integration and with $10^7$ parameters). Then, two strategies are adopted to mitigate the optimization challenges posed by the larger parameter space. To constrain the optimized model parameter space, we introduce a layer selection strategy that identifies the layers most critical to
multimodal integration. Interestingly, deviating from the past experience, our analysis reveals that the optimal layers for LVLM
prompt perturbation reside in the middle of the model rather than the last. To constrain the image perturbation space, we propose a new distance-decremental perturbation budget assignment strategy that allocates budgets decrementally as the pixel distance to semantic-critical regions increases. Extensive experiments across multiple LVLMs and
datasets show that our method significantly outperforms baseline approaches.
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All authors Hao Yang, Zhuo Ma, Yang Liu, Yilong Yang, Guancheng Wang, JianFeng Ma
Computer Vision · Cross-listed from cs.LG
MIND: Decoupling Model-Induced Label Noise via Latent Manifold Disentanglement
Dayong Ren
The paradigm of learning from automatic annotations driven by pre-trained experts and Foundation Models dominates data-hungry applications. However, it introduces a critical challenge: model-induced label noise. Unlike
stochastic noise in classical robust learning, this noise stems from annotator inductive biases, manifesting as systematic errors tightly coupled with local feature
manifolds. Existing methods relying on global transition
matrices underfit these structural patterns, while learning instance-specific
matrices remains mathematically intractable. We propose Model-Induced Noise Decoupling (MIND), a theoretically grounded framework addressing this dilemma. We demonstrate that the high-dimensional noise
manifold can be decoupled into tractable, subspace-dependent components via Latent
Manifold Disentanglement. Specifically, our Latent Decoupling
Estimator (LDE) dynamically projects samples into latent structural clusters with consistent error modes, facilitating noise identifiability without ground-truth anchor points. To rigorously evaluate robustness, we adopt a hierarchical protocol: moving from controlled noise on CIFAR-100 to a structural stress test on large-scale real-world 3D
datasets (S3DIS, ScanNet), where error patterns explicitly couple with
geometric manifolds. Empirically, MIND significantly outperforms state-of-the-art methods on these complex
benchmarks and effectively corrects zero-shot
hallucinations from
Vision-
Language Models (e.g., OpenSeg), highlighting its potential as a robust
distillation framework for Foundation Models.
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Computer Vision
ReAlign: Generalizable Image Forgery Detection via Reasoning-Aligned Representation
Qing Huang, Zhipei Xu, Xuanyu Zhang et al. (+2 more)
The rise of AI-generated images (AIGIs) poses growing challenges for digital authenticity, prompting the need for efficient, generalizable image forgery detection systems. Existing methods, whether non-LLM-based or LLM-based, exhibit distinct advantages and limitations. While non-LLM-based models offer efficient low-level artifact detection, they often lack semantic understanding. Conversely, LLM-based methods provide strong semantic
reasoning and explainability but are computationally intensive and less sensitive to subtle visual artifacts. Moreover, the true contribution of explanatory
reasoning texts to forgery detection performance remains unclear. In this work, we investigate the intrinsic value and potential of LLM-generated
reasoning texts, considering it a source of generalization and semantic-error sensitivity. Based on these findings, we propose ReAlign, a novel framework that distills high-quality
reasoning texts generated by a GRPO-optimized LLM into a lightweight AIGI detector via contrastive learning. ReAlign effectively inherits the generalization ability and semantic sensitivity capability of
reasoning textual representations, while remaining efficient and lightweight for deployment. Moreover, ReAlign adopts a tailored joint optimization strategy that integrates contrastive loss for image-text
alignment and classification loss for accurate forgery discrimination. Experimental results on AIGCDetectBenchmark, AIGI-Holmes, and our newly constructed UltraSynth-10k demonstrate that ReAlign consistently outperforms existing state-of-the-art detectors in both accuracy and generalization, particularly when facing complex, high-fidelity forgeries from modern generative models.
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All authors Qing Huang, Zhipei Xu, Xuanyu Zhang, Xiangyu Yu, Jian Zhang
Computer Vision
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation
Yiming Zhao, Yu Zeng, Wenxuan Huang et al. (+11 more)
Large
Vision-
Language Models (LVLMs) have shown significant progress in
video understanding, yet they face substantial challenges in tasks requiring precise spatiotemporal localization at the instance level. Existing methods primarily rely on text
prompts for human-model interaction, but these
prompts struggle to provide precise spatial and temporal references, resulting in poor user experience. Furthermore, current approaches typically decouple visual perception from
language reasoning, centering
reasoning around
language rather than visual content, which limits the model's ability to proactively perceive fine-grained visual evidence. To address these challenges, we propose VideoSeeker, a novel paradigm for instance-level
video understanding through visual
prompts. VideoSeeker seamlessly integrates agentic
reasoning with instance-level
video understanding tasks, enabling the model to proactively perceive and retrieve relevant
video segments on demand. We construct a four-stage fully automated data synthesis pipeline to efficiently generate large-scale, high-quality instance-level
video data. We internalize tool-calling and proactive perception capabilities into the model via cold-start supervision and RL training, building a powerful
video understanding model. Experiments demonstrate that our model achieves an average improvement of +13.7% over baselines on instance-level
video understanding tasks, surpassing powerful closed-source models such as GPT-4o and Gemini-2.5-Pro, while also showing effective transferability on general
video understanding
benchmarks. The relevant
datasets and code will be released publicly.
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All authors Yiming Zhao, Yu Zeng, Wenxuan Huang, Zhen Fang, Qing Miao, Qisheng Su, Jiawei Zhao, Jiayin Cai, Lin Chen, Zehui Chen, Yukun Qi, Yao Hu, Xiaolong Jiang, Feng Zhao
Computer Vision
AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification
Salma Hoque Talukdar Koli and Fahima Haque Talukder Jely
Plant disease detection is still largely manual in Bangladesh, where extension workers eyeball leaf samples across millions of smallholdings. We built AgriMind to automate this: an ensemble of ResNet50, EfficientNet-B0, and DenseNet121 trained on 20,638 PlantVillage images across 15 pepper, potato, and tomato disease classes. Transfer learning with frozen ImageNet backbones and 10 epochs of head-only training keeps the pipeline lightweight. Individual models hit 96–97% on the held-out test set, but averaging their softmax outputs pushes the ensemble to 99.23% – a two-thirds cut in error rate. We tried biasing the average toward the best validation model; it backfired. Dropping any single model also hurt. Pepper and potato classify perfectly; tomato, with ten visually similar classes, still reaches 99.01%. On an NVIDIA T4 GPU the full ensemble runs at 53 FPS. Whether that translates to real-time mobile use depends on TensorFlow Lite optimization – work we have not yet completed.
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Computer Vision
Robust Prior-Guided Segmentation for Editable 3D Gaussian Splatting
Raushan Joshi and Jean-Yves Guillemaut
3D Gaussian Splatting (3D-GS) enables real-time 3D scene reconstruction but lacks robust
segmentation for editing tasks such as object removal, extraction, and recoloring. Existing approaches that lift 2D
segmentations to the 3D domain suffer from view inconsistencies and coarse masks. In this paper, we propose a novel framework that leverages the Segment Anything Model High Quality (SAM-HQ) to generate accurate 2D masks, addressing the limitations of the standard SAM in boundary fidelity and fine-structure preservation. To achieve robust 3D
segmentation of any target object in a given scene, we introduce a prior-guided label reassignment method that assigns labels to 3D Gaussians by enforcing multiview consistency with learned priors. Our approach achieves state-of-the-art
segmentation accuracy and enables interactive, real-time object editing while maintaining high visual fidelity. Qualitative results demonstrate superior boundary preservation and practical utility in Virtual Reality (VR) and robotics, advancing 3D scene editing.
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Computer Vision
EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting
Changjing Liu, Yiming Huang, Long Bai et al. (+2 more)
In
robot-assisted minimally invasive surgery, high-fidelity dynamic endoscopic scene reconstruction and simulation are crucial to enhancing downstream tasks and advancing
surgical outcomes. However, existing methods primarily focus on visual reconstruction, lacking physics-based descriptions of the scene required for realistic simulation. We propose a unified framework that achieves physics-aware reconstruction and physical simulation of endoscopic scenes through Multi-modal Large
Language Models (MLLMs)-guided Gaussian Splatting. Our approach utilizes 4D Gaussian Splatting (4DGS) integrated with pre-trained
segmentation and depth estimation to represent deformable tissues and tools. To achieve automatic inference of physical properties, we introduce an object-wise material field that initializes material parameters via MLLM and refines them through a differentiable Material Point Method (MPM) under joint supervision from rendered images and optical flow. Validated on both open-source and in-house
datasets, our framework achieves superior simulation fidelity and physical accuracy compared to state-of-the-art methods, underscoring its potential to advance
robot-assisted
surgical applications.
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All authors Changjing Liu, Yiming Huang, Long Bai, Beilei Cui, Hongliang Ren
Computer Vision
End-to-end plaque counting and virus titration from laboratory plate images with deep learning
Eugenia Moris, Alicia Costábile, Sebastián Rey et al. (+11 more)
Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-operator variability. We present an end-to-end, computer-aided workflow for cytopathic effect-based virus titration directly from laboratory plaque assay images. The proposed approach combines two models derived from the Segment Anything Model (SAM): a SAM2-based well-
segmentation module that localizes assay wells across heterogeneous imaging conditions, and a SAM-based plaque-
segmentation model that detects and enumerates plaques within each well. The method was evaluated on a mixed
dataset comprising private plaque assay images of Mayaro virus and Coxsackievirus B3, together with public Vaccinia virus images from the VACVPlaque
dataset. The pipeline outputs per-well plaque counts, automatically computes plaque-forming units per milliliter (PFU/mL), and is integrated into a web-based platform that allows users to review results and organize experiments. On held-out plates (17 from MAYV/CVB3 and 22 from VACV), the workflow generalized across two plate formats (6-well and 12-well) and showed strong agreement with manual annotations (Pearson correlation coefficients of 0.92 for MAYV/CVB3 and 0.88 for VACV). Automated plaque counts were further compared with annotations from four independent experts, demonstrating high concordance. The proposed system will be open sourced and publicly released upon acceptance of this manuscript to enable reproducible, scalable, and audit-ready plaque assay analysis while substantially reducing manual annotation effort.
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All authors Eugenia Moris, Alicia Costábile, Sebastián Rey, Irene Ferreiro, Joaquín Hurtado, Lizandra Lissette Luciano, Matías Villagrán, Aisha Espino Vázquez, Jomari Ramos, Isadora Monteiro, María Victoria de Santiago, Pilar Moreno, Gonzalo Moratorio, José Ignacio Orlando
Computer Vision
Echo-Forcing: A Scene Memory Framework for Interactive Long Video Generation
Mingqiang Wu, Weilun Feng, Zhefeng Zhang et al. (+8 more)
Autoregressive
video diffusion models enable open-ended generation through local
attention and KV caching. However, existing training-free long-
video optimization methods mainly focus on stable extension under a single
prompt, making them difficult to handle interactive scenarios involving
prompt switching, old scene forgetting, and historical scene recall. We identify the core bottleneck as the functional
entanglement of historical KV states: stable anchors and recent dynamics are handled by the same cache policy, leading to outdated background contamination, delayed response to new
prompts, and loss of long-range
memory. To address this issue, we propose Echo-Forcing, a training-free scene
memory framework specifically designed for interactive long
video generation with three core mechanisms: (1) Hierarchical Temporal
Memory, which decouples stable anchors, compressed history, and recent windows under relative RoPE; (2) Scene Recall Frames, which compresses historical scenes into spatially structured KV representations to support long-term recall; and (3) Difference-aware
Memory Decay, which adaptively forgets conflicting
tokens according to the discrepancy between old and new scenes. Based on these designs, Echo-Forcing uniformly supports smooth transitions, hard cuts, and long-range scene recall under a bounded cache budget. Extensive evaluations on VBench-Long further demonstrate that Echo-Forcing achieves the best overall performance in both long-
video generation and interactive
video generation settings. Our code is released in https://github.com/mingqiangWu/Echo-Forcing
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All authors Mingqiang Wu, Weilun Feng, Zhefeng Zhang, Haotong Qin, Yuqi Li, Guoxin Fan, Xiaokun Liu, Zhulin An, Libo Huang, Yongjun Xu, Chuanguang Yang
Computer Vision
Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning
Yuyuan Liu, Can Peng, Yingyu Yang et al. (+3 more)
Recent progress in deep learning has significantly advanced CT image analysis, particularly for
segmentation tasks. However, these advances are largely confined to image-level pattern recognition, with most methods lacking explicit anatomical or contextual
reasoning. Large
vision-
language models introduce linguistic context into image analysis, yet most approaches typically focus on a single task, which is insufficient for clinical workflow analysis that requires multiple fine-grained types of analysis, such as anatomy detection and
segmentation. In this paper, we propose a unified autoregressive framework that integrates
language-guided visual
reasoning into CT interpretation. Our method introduces task-routing
tokens that trigger detection and
segmentation heads conditioned on the hidden states of a large
vision-
language model, enabling coherent generation of visual outputs (e.g., masks and bounding boxes) and textual reasonings. To progressively enhance localisation accuracy and semantic clarity, we further design a "closer-look" mechanism that allows the model to perform progressive coarse-to-fine visits to regions of interest under refined fields of view. To support model training and evaluation, we curated a new
multimodal CT
dataset containing pixel-wise masks, bounding boxes, spatial
prompts, and structured descriptions for visual objects constructed through an AI-assisted annotation process with human verification. Experiments on public
benchmarks demonstrate consistent improvements over the SoTA, achieving up to 1.0% Dice on BTCV and 1.7% Dice on MosMed+, while additionally providing appearance
reasoning outputs. The code and
dataset will be available.
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All authors Yuyuan Liu, Can Peng, Yingyu Yang, Qianye Yang, Cheng Ouyang, J. Alison Noble
Computer Vision
Flash-GRPO: Efficient Alignment for Video Diffusion via One-Step Policy Optimization
Xiaoxuan He, Siming Fu, Zeyue Xue et al. (+9 more)
Group Relative Policy Optimization has emerged as essential for aligning
video diffusion models with human preferences, but faces a critical computational bottleneck: training a 14B parametered model typically demands hundreds of GPU days per experiment. Existing efficiency methods reduce costs through sliding window subsampling training timesteps, but fundamentally compromise optimization, exhibiting severe instability and failing to reach full trajectory performance. We present Flash-GRPO, a single-step training framework that outperforms full trajectory training in
alignment quality under low computational budgets while substantially improving training efficiency. Flash-GRPO addresses two critical challenges: iso-temporal grouping eliminates timestep-confounded variance by enforcing
prompt-wise temporal consistency, decoupling policy performance from timestep difficulty; temporal gradient rectification neutralizes the time-dependent scaling factor that causes vastly inconsistent gradient magnitudes across timesteps. Experiments on 1.3B to 14B parameter models validate Flash-GRPO's effectiveness, demonstrating substantial training acceleration with consistent stability and state-of-the-art
alignment quality.
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All authors Xiaoxuan He, Siming Fu, Zeyue Xue, Weijie Wang, Ruizhe He, Yuming Li, Dacheng Yin, Shuai Dong, Haoyang Huang, Hongfa Wang, Nan Duan, Bohan Zhuang
Computer Vision · Cross-listed from cs.AI
Deterministic Event-Graph Substrates as World Models for Counterfactual Reasoning
Fabio Rovai
We study event-graph substrates: a class of world models that represent
agent state as an append-only log of typed RDF triples and answer counterfactual queries by forking the log under a structured intervention vocabulary. Substrates are inspectable at the triple level, support exact counterfactuals, and transfer across domains without learned components. We formalize the class, prove a duality between explanatory and counterfactual queries that reduces both to the same causal-ancestor traversal, and evaluate a 1,400-line CLEVRER-DSL interpreter atop a domain-agnostic substrate runtime at full CLEVRER validation scale (n=75,618). The substrate exceeds the NS-DR symbolic oracle on all four per-question categories (by 9.89, 20.26, 17.65, and 0.80 percentage points), and exceeds the parametric ALOE baseline on descriptive and explanatory while lagging on predictive and counterfactual. We also introduce twin-EventLog, a 500-specification Park-canonical Smallville counterfactual
benchmark on which the substrate exceeds Llama-3.1-8B with full context by 18.80 points joint accuracy.
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Computer Vision · Cross-listed from cs.RO
WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation
Baining Zhao, Jiacheng Xu, Weicheng Feng et al. (+13 more)
Aerial
vision-
language navigation (VLN) requires
agents to follow natural-
language instructions through closed-loop perception and action in 3D environments. We argue that aerial VLN can be formulated as a prediction-driven world-action problem: the
agent should anticipate latent world
evolution and act according to the predicted consequences. To this end, we propose WorldVLN, the first autoregressive world action model for aerial VLN. Unlike full-sequence
video-generation world models that generate an entire visual clip, WorldVLN adapts a latent autoregressive
video backbone to predict short-horizon world-state transitions and directly decodes them into executable waypoint actions. After each action segment is executed, newly received observations are encoded back into the autoregressive context, enabling closed-loop world-action prediction. We further introduce a two-stage training framework that first grounds the
video prior in instruction-conditioned
navigation dynamics and then develops Action-aware GRPO, the first reinforcement learning method tailored to autoregressive WAMs, to optimize waypoint decisions through their downstream rollout consequences. On public outdoor and indoor
benchmarks, WorldVLN consistently outperforms existing
Vision-
Language-Action baselines with 12%+ success-rate gains and larger advantages on challenging cases. It further transfers zero-shot to real drone deployment, suggesting that the proposed WorldVLN offers a promising route for spatial action tasks. Demos and code are available at https://embodiedcity.github.io/WorldVLN/.
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All authors Baining Zhao, Jiacheng Xu, Weicheng Feng, Xin Zhang, Zhaolu Wang, Haoyang Wang, Shilong Ji, Ziyou Wang, Jianjie Fang, Zhiheng Zheng, Weichen Zhang, Yu Shang, Wei Wu, Chen Gao, Xinlei Chen, Yong Li
Computer Vision
Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models
Fabian Morelli, Arnas Uselis, Ankit Sonthalia et al. (+1 more)
Large-scale pre-trained
vision-
language models like CLIP demonstrate remarkable zero-shot performance across diverse tasks. However, fine-tuning these models to improve downstream performance often degrades robustness against distribution shifts. Recent approaches have attempted to mitigate this trade-off, but often rely on computationally expensive text-guidance. We propose a novel method for robust fine-tuning, SAE-FT, which operates only on the model's visual representations. SAE-FT regularizes changes to these representations by penalizing the addition and removal of semantically meaningful features identified by a Sparse Autoencoder trained on the pre-trained model. This constraint prevents catastrophic forgetting and makes the fine-tuning process interpretable, enabling direct analysis of semantic changes. SAE-FT is both mechanistically transparent and computationally efficient, matching or exceeding state-of-the-art performance on ImageNet and its associated distribution shift
benchmarks. Code is publicly available at: https://github.com/Fabian-Mor/sae-ft.
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All authors Fabian Morelli, Arnas Uselis, Ankit Sonthalia, Seong Joon Oh
Computer Vision
From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding
Yuyuan Liu, Yiping Ji, Anjie Le et al. (+6 more)
Finetuning Large
Vision-
Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grounding. However, existing methods, mainly based on GRPO, assign rewards at the response level. Such sparse reward, often criterion-induced, leads to minimal learning signals when all candidate responses fail in challenging scenarios. In this work, we propose a group-revision optimisation paradigm that enhances learning on hard cases. It begins with a sampled initial response and generates a set of revised candidates to explore improved grounding outcomes. Inspired by reward shaping, we introduce a consolidation process that quantifies each candidate's improvement over the initial attempt and converts it into informative shaping signals. These signals are used to both refine the reward and modulate the advantage, amplifying the influence of high-quality revisions. Our method achieves consistent gains across referring and
reasoning segmentation, REC, and counting
benchmarks compared with prior GRPO-based models. Our code is available at https://github.com/yyliu01/GroupRevision.
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All authors Yuyuan Liu, Yiping Ji, Anjie Le, Jiayuan Zhu, Jiazhen Pan, Can Peng, Jiajun Deng, Fengbei Liu, Junde Wu
Computer Vision
Chenhao Wang, Yingrui Ji, Yu Meng et al. (+1 more)
Open-vocabulary
segmentation models often struggle to generalize to unseen combinations of object categories and attributes, because fine-grained descriptions are typically encoded as holistic sentences that entangle multiple semantic units. We propose a Decomposed
Vision-
Language Alignment framework that explicitly factorizes textual
prompts into a concept
token and multiple attribute
tokens, enabling separate cross-modal interactions for each semantic unit. At the feature level, we introduce a Feature-Gated Cross-
Attention module that generates attribute-specific gating maps to fuse information in a multiplicative manner, effectively enforcing compositional semantics. At the scoring level, per-
token similarities are aggregated in log-space, producing a stable and interpretable compositional matching. The method can be seamlessly integrated into existing
transformer-based
segmentation architectures and significantly improves generalization to unseen attribute-category compositions in fine-grained open-vocabulary
segmentation benchmarks.
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All authors Chenhao Wang, Yingrui Ji, Yu Meng, Yao Zhu
Computer Vision
Invaria: Learning Scale and Density Invariance in Point Clouds via Next-Resolution Prediction
Chun-Peng Chang, Shaoxiang Wang, Alain Pagani et al. (+2 more)
Modern image encoders achieve high generalization by decoupling semantic meaning from resolution, an ability yet to be fully realized in the 3D domain. We investigate the failure of 3D point cloud encoders to achieve similar generalization and find that existing models are highly sensitive to sampling resolution and scale changes, leading to significant performance degradation. This sensitivity is a major bottleneck for real-world deployment in robotics, as it suggests models overfit to specific quantization densities and object scales rather than learning invariant semantic features. To mitigate this dependency, we propose Invaria, a point cloud encoder that achieves scale and density invariance through next-resolution prediction and receptive field calibration. While our objective is not the explicit generation of high-resolution point clouds, we find that this training objective encourages the model to learn robust, structural invariants. The resulting encoder achieves significant performance gains during resolution shifts while maintaining high efficiency through a compact model size and reduced
token requirements. Specifically, on ScanNet, Invaria achieves a 56.0% higher mIoU at 3$\times$ lower resolution and a 20% improvement when the objects scale is reduced by a factor of 3. These gains are achieved with a 45% smaller model size and an average reduction of 40% in input
tokens.
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All authors Chun-Peng Chang, Shaoxiang Wang, Alain Pagani, Dariu Gavrila, Holger Caesar
Computer Vision
AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression
Dingming Liu
Object removal aims to eliminate specified objects from images while plausibly inpainting the affected regions with background content. Current training-free methods typically block
attention to object regions within self-
attention layers during the image generation process, leveraging surrounding background information to restore the image. However, indiscriminate suppression of self-
attention in the vacated areas can degrade generation quality, as the model must simultaneously reconstruct background content in these regions. To solve this conflict, we propose AdaEraser, an adaptive framework that dynamically modulates
attention based on the estimated presence of target object concepts. Through analysis of self-
attention map
evolution across denoising timesteps before and during removal, we develop a
token-wise adaptive
attention suppression strategy. This approach enables progressive perception of object removal throughout the denoising process, with the suppression strength in self-
attention layers adjusted adaptively. Extensive experiments demonstrate that AdaEraser achieves superior performance in object removal, outperforming even training-based methods.
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Computer Vision · Cross-listed from cs.LG
LoCO: Low-rank Compositional Rotation Fine-tuning
An Nguyen, Jaesik Choi and Anh Tong
Parameter-efficient fine-tuning (PEFT) has emerged as an critical technique for adapting large-scale foundation models across natural
language processing and computer
vision. While existing methods such as low-rank adaptations achieve parameter efficiency via low-rank weight updates, they are limited in their ability to preserve the
geometric structure of pretrained representations. We introduce Low-rank Compositional Orthogonal fine-tuning (LoCO), a novel PEFT method that constructs orthogonal transformations through low-rank skew-symmetric
matrices and compositional rotation chains. We propose an approximation scheme that enables fully parallel computation of compositional rotations, making the approach practical for high-dimensional feature spaces. Our method maintains low computational complexity while maintaining orthogonality with controlled approximation error. We validate LoCO across diverse domains, including
diffusion transformer fine-tuning,
vision transformer adaptation, and
language model adaptation. Our method demonstrates superior or competitive performance compared to both existing orthogonal and non-orthogonal methods.
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Computer Vision
RaPD: Resolution-Agnostic Pixel Diffusion via Semantics-Enriched Implicit Representations
Yanhao Ge, Shanyan Guan, Weihao Wang et al. (+2 more)
Natural images are continuous, yet most generative models synthesize them on discrete grids, limiting resolution-flexible generation. Continuous neural fields enable resolution-free rendering, but prior methods introduce continuity only at the decoding stage as an interpolation module, leaving the generative latent space discretized and reconstruction-oriented. We propose RaPD (Resolution-agnostic Pixel
Diffusion), which performs
diffusion in a continuous Neural Image Field (NIF) latent space. RaPD bridges this reconstruction generation gap with Semantic Representation Guidance for generation-aware latent learning and a Coordinate-Queried
Attention Renderer for coordinate-conditioned, scale-aware rendering. A single denoised latent can be rendered at arbitrary resolutions by changing only the query coordinates, keeping
diffusion cost fixed. Experiments demonstrate superior generation quality and resolution scalability.
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All authors Yanhao Ge, Shanyan Guan, Weihao Wang, Ying Tai, Mingyu You