Computer Vision · Cross-listed from cs.GR
MeshTailor: Cutting Seams via Generative Mesh Traversal
Xueqi Ma, Xingguang Yan, Congyue Zhang et al. (+1 more)
We present MeshTailor, the first mesh-native generative framework for synthesizing edge-aligned seams on 3D surfaces. Unlike prior optimization-based or extrinsic learning-based methods, MeshTailor operates directly on the mesh graph, eliminating projection artifacts and fragile snapping heuristics. We introduce ChainingSeams, a hierarchical serialization of the seam graph that orders chains from global structural cuts down to local details in a coarse-to-fine manner, and a dual-stream encoder that fuses topological and
geometric context. Leveraging this hierarchical representation and dual-stream vertex
embeddings, our MeshTailor
Transformer utilizes an autoregressive pointer layer to trace seams vertex-by-vertex within local neighborhoods. Extensive evaluations show that MeshTailor produces more coherent and structurally regular seam layouts compared to recent optimization-based and learning-based baselines.
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All authors Xueqi Ma, Xingguang Yan, Congyue Zhang, Hui Huang
Computer Vision
Dual-Path Learning based on Frequency Structural Decoupling and Regional-Aware Fusion for Low-Light Image Super-Resolution
Ji-Xuan He, Jia-Cheng Zhao, Feng-Qi Cui et al. (+5 more)
Low-light image super-resolution (LLISR) is essential for restoring fine visual details and perceptual quality under insufficient illumination conditions with ubiquitous low-resolution devices. Although pioneer methods achieve high performance on single tasks, they solve both tasks in a serial manner, which inevitably leads to artifact amplification, texture suppression, and structural degradation. To address this, we propose Decoupling then Perceive (DTP), a novel frequency-aware framework that explicitly separates luminance and texture into semantically independent components, enabling specialized modeling and coherent reconstruction. Specifically, to adaptively separate the input into low-frequency luminance and high-frequency texture subspaces, we propose a Frequency-aware Structural Decoupling (FSD) mechanism, which lays a solid foundation for targeted representation learning and reconstruction. Based on the decoupled representation, a Semantics-specific Dual-path Representation (SDR) learning strategy that performs targeted enhancement and reconstruction for each frequency component is further designed, facilitating robust luminance adjustment and fine-grained texture recovery. To promote structural consistency and perceptual
alignment in the reconstructed output, building upon this dual-path modeling, we further introduce a Cross-frequency Semantic Recomposition (CSR) module that selectively integrates the decoupled representations. Extensive experiments on the most widely used LLISR
benchmarks demonstrate the superiority of our DTP framework, improving $+$1.6% PSNR, $+$9.6% SSIM, and $-$48% LPIPS compared to the most state-of-the-art (SOTA)
algorithm. Codes are released at https://github.com/JXVision/DTP.
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All authors Ji-Xuan He, Jia-Cheng Zhao, Feng-Qi Cui, Jinyang Huang, Yang Liu, Sirui Zhao, Meng Li, Zhi Liu
Computer Vision
Complet4R: Geometric Complete 4D Reconstruction
Weibang Wang, Kenan Li, Zhuoguang Chen et al. (+2 more)
We introduce Complet4R, a novel end-to-end framework for
Geometric Complete 4D Reconstruction, which aims to recover temporally coherent and geometrically complete reconstruction for dynamic scenes. Our method formalizes the task of
Geometric Complete 4D Reconstruction as a unified framework of reconstruction and completion, by directly accumulating full contexts onto each frame. Unlike previous approaches that rely on pairwise reconstruction or local motion estimation, Complet4R utilizes a decoder-only
transformer to operate all context globally directly from sequential
video input, reconstructing a complete geometry for every single timestamp, including occluded regions visible in other frames. Our method demonstrates the state-of-the-art performance on our proposed
benchmark for
Geometric Complete 4D Reconstruction and the 3D Point Tracking task. Code will be released to support future research.
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All authors Weibang Wang, Kenan Li, Zhuoguang Chen, Yijun Yuan, Hang Zhao
Computer Vision
Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving
Wonjune Kim, In-Jae Lee, Sihwan Hwang et al. (+2 more)
3D occupancy prediction provides dense spatial understanding critical for safe autonomous driving. However, this task suffers from a severe class imbalance due to its volumetric representation, where safety-critical objects (bicycles, traffic cones, pedestrians) occupy minimal voxels compared to dominant backgrounds. Additionally, voxel-level annotation is costly, yet dedicating effort to dominant classes is inefficient. To address these challenges, we propose a class-distribution guided active learning framework for selecting training samples to annotate in autonomous driving
datasets. Our approach combines three complementary criteria to select the training samples. Inter-sample diversity prioritizes samples whose predicted class distributions differ from those of the labeled set, intra-set diversity prevents redundant sampling within each acquisition cycle, and frequency-weighted uncertainty emphasizes rare classes by reweighting voxel-level
entropy with inverse per-sample class proportions. We ensure evaluation validity by using a geographically disjoint train/validation split of Occ3D-nuScenes, which reduces train-validation overlap and mitigates potential map memorization. With only 42.4% labeled data, our framework reaches 26.62 mIoU, comparable to full supervision and outperforming active learning baselines at the same budget. We further validate generality on SemanticKITTI using a different architecture, demonstrating consistent effectiveness across
datasets.
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All authors Wonjune Kim, In-Jae Lee, Sihwan Hwang, Sanmin Kim, Dongsuk Kum
Computer Vision
Human-Centric Perception for Child Sexual Abuse Imagery
Camila Laranjeira, João Macedo, Sandra Avila et al. (+2 more)
Law enforcement agencies and non-gonvernmental organizations handling reports of Child Sexual Abuse Imagery (CSAI) are overwhelmed by large volumes of data, requiring the aid of automation tools. However, defining sexual abuse in images of children is inherently challenging, encompassing sexually explicit activities and hints of sexuality conveyed by the individual's pose, or their attire. CSAI classification methods often rely on black-box approaches, targeting broad and abstract concepts such as pornography. Thus, our work is an in-depth exploration of tasks from the literature on Human-Centric Perception, across the domains of safe images, adult pornography, and CSAI, focusing on targets that enable more objective and explainable pipelines for CSAI classification in the future. We introduce the Body-Keypoint-Part
Dataset (BKPD), gathering images of people from varying age groups and sexual explicitness to approximate the domain of CSAI, along with manually curated hierarchically structured labels for skeletal keypoints and bounding boxes for person and body parts, including head, chest, hip, and hands. We propose two methods, namely BKP-Association and YOLO-BKP, for simultaneous pose estimation and detection, with targets associated per individual for a comprehensive decomposed representation of each person. Our methods are benchmarked on COCO-Keypoints and COCO-HumanParts, as well as our human-centric
dataset, achieving competitive results with models that jointly perform all tasks. Cross-domain ablation studies on BKPD and a case study on RCPD highlight the challenges posed by sexually explicit domains. Our study addresses previously unexplored targets in the CSAI domain, paving the way for novel research opportunities.
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All authors Camila Laranjeira, João Macedo, Sandra Avila, Fabrício Benevenuto, Jefersson A. dos Santos
Computer Vision · Cross-listed from cs.RO
Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
Qiqi Liu, Huan Xu, Jingyu Li et al. (+5 more)
Autonomous driving requires
reasoning about how the environment evolves and planning actions accordingly. Existing world-model-based approaches typically predict future scenes first and plan afterwards, resulting in open-loop imagination that may drift from the actual decision process. In this paper, we present Uni-World VLA, a unified
vision-
language-action (VLA) model that tightly interleaves future frame prediction and trajectory planning. Instead of generating a full world rollout before planning, our model alternates between predicting future frames and ego actions step by step, allowing planning decisions to be continuously conditioned on the imagined future observations. This interleaved generation forms a closed-loop interaction between world modeling and control, enabling more adaptive decision-making in dynamic traffic scenarios. In addition, we incorporate
monocular depth information into frames to provide stronger
geometric cues for world modeling, improving long-horizon scene prediction. Experiments on the NAVSIM
benchmark show that our approach achieves competitive closed-loop planning performance while producing high-fidelity future frame predictions. These results demonstrate that tightly coupling world prediction and planning is a promising direction for scalable VLA driving systems.
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All authors Qiqi Liu, Huan Xu, Jingyu Li, Bin Sun, Zhihui Hao, Dangen She, Xiatian Zhu, Li Zhang
Computer Vision
TrackMAE: Video Representation Learning via Track Mask and Predict
Renaud Vandeghen, Fida Mohammad Thoker, Marc Van Droogenbroeck et al. (+1 more)
Masked
video modeling (MVM) has emerged as a simple and scalable self-supervised pretraining paradigm, but only encodes motion information implicitly, limiting the encoding of temporal dynamics in the learned representations. As a result, such models struggle on motion-centric tasks that require fine-grained motion awareness. To address this, we propose TrackMAE, a simple masked
video modeling paradigm that explicitly uses motion information as a reconstruction signal. In TrackMAE, we use an off-the-shelf point tracker to sparsely track points in the input
videos, generating motion trajectories. Furthermore, we exploit the extracted trajectories to improve random tube masking with a motion-aware masking strategy. We enhance
video representations learned in both pixel and feature semantic reconstruction spaces by providing a complementary supervision signal in the form of motion targets. We evaluate on six
datasets across diverse downstream settings and find that TrackMAE consistently outperforms state-of-the-art
video self-supervised learning baselines, learning more discriminative and generalizable representations. Code available at https://github.com/rvandeghen/TrackMAE
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All authors Renaud Vandeghen, Fida Mohammad Thoker, Marc Van Droogenbroeck, Bernard Ghanem
Computer Vision
TrendGen: An Outfit Recommendation and Display System
Theodoros Koukopoulos, Dimos Klimenof and Ioannis Xarchakos
Recent advances in Computer
Vision have significantly improved image understanding and generation, revolutionizing the fashion industry. However, challenges such as inconsistent lighting, non-ideal garment angles, complex backgrounds, and occlusions in raw images hinder their full potential. Overcoming these obstacles is crucial for developing robust fashion AI systems capable of real-world applications. In this paper, we introduce TrendGen, a Fashion AI system designed to enhance online shopping with intelligent outfit recommendations. Deployed on a major e-commerce platform, TrendGen leverages cloth images and product attributes to generate trend-aligned, cohesive outfit suggestions. Additionally, it employs Generative AI to transform raw images into high-quality lay-down views, offering a clear and structured presentation of garments. Our evaluation on production data demonstrates TrendGen's consistent high-quality outfits and lay-down images, marking a significant advancement in AI-driven solutions for fashion retail.
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Computer Vision
MD-RWKV-UNet: Scale-Aware Anatomical Encoding with Cross-Stage Fusion for Multi-Organ Segmentation
Zhuoyi Fang
Multi-organ
segmentation in medical imaging remains challenging due to large anatomical variability, complex inter-organ dependencies, and diverse organ scales and shapes. Conventional encoder-decoder architectures often struggle to capture both fine-grained local details and long-range context, which are crucial for accurate delineation - especially for small or deformable organs. To address these limitations, we propose MD-RWKV-UNet, a dynamic encoder network that enables scale-aware representation and spatially adaptive context modeling. At its core is the MD-RWKV block, a dual-path module that integrates deformable spatial shifts with the Receptance Weighted Key Value mechanism, allowing the receptive field to adapt dynamically to local structural cues. We further incorporate Selective Kernel
Attention to enable adaptive selection of convolutional kernels with varying receptive fields, enhancing multi-scale interaction and improving robustness to organ size and shape variation. In parallel, a cross-stage dual-
attention fusion strategy aggregates multi-level features across the encoder, preserving low-level structure while enhancing semantic consistency. Unlike methods that stack static convolutions or rely heavily on global
attention, our approach provides a lightweight yet expressive solution for dynamic organ modeling. Experiments on Synapse and ACDC demonstrate state-of-the-art performance, particularly in boundary precision and small-organ
segmentation.
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Computer Vision
Seeing the Scene Matters: Revealing Forgetting in Video Understanding Models with a Scene-Aware Long-Video Benchmark
Seng Nam Chen, Hao Chen, Chenglam Ho et al. (+4 more)
Long
video understanding (LVU) remains a core challenge in
multimodal learning. Although recent
vision-
language models (VLMs) have made notable progress, existing
benchmarks mainly focus on either fine-grained perception or coarse summarization, offering limited insight into temporal understanding over long contexts. In this work, we define a scene as a coherent segment of a
video in which both visual and semantic contexts remain consistent, aligning with human perception. This leads us to a key question: can current VLMs reason effectively over long, scene-level contexts? To answer this, we introduce a new
benchmark, SceneBench, designed to provide scene-level challenges. Our evaluation reveals a sharp drop in accuracy when VLMs attempt to answer scene-level questions, indicating significant forgetting of long-range context. To further validate these findings, we propose Scene
Retrieval-Augmented Generation (Scene-RAG), which constructs a dynamic scene
memory by retrieving and integrating relevant context across scenes. This Scene-RAG improves VLM performance by +2.50%, confirming that current models still struggle with long-context retention. We hope SceneBench will encourage future research toward VLMs with more robust, human-like
video comprehension.
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All authors Seng Nam Chen, Hao Chen, Chenglam Ho, Xinyu Mao, Jinping Wang, Yu Zhang, Chao Li
Computer Vision
Zero-shot Vision-Language Reranking for Cross-View Geolocalization
Yunus Talha Erzurumlu, John E. Anderson, William J. Shuart et al. (+2 more)
Cross-view geolocalization (CVGL) systems, while effective at retrieving a list of relevant candidates (high Recall@k), often fail to identify the single best match (low Top-1 accuracy). This work investigates the use of zero-shot
Vision-
Language Models (VLMs) as rerankers to address this gap. We propose a two-stage framework: state-of-the-art (SOTA)
retrieval followed by VLM reranking. We systematically compare two strategies: (1) Pointwise (scoring candidates individually) and (2) Pairwise (comparing candidates relatively). Experiments on the VIGOR
dataset show a clear divergence: all pointwise methods cause a catastrophic drop in performance or no change at all. In contrast, a pairwise comparison strategy using LLaVA improves Top-1 accuracy over the strong
retrieval baseline. Our analysis concludes that, these VLMs are poorly calibrated for absolute relevance scoring but are effective at fine-grained relative visual judgment, making pairwise reranking a promising direction for enhancing CVGL precision.
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All authors Yunus Talha Erzurumlu, John E. Anderson, William J. Shuart, Charles Toth, Alper Yilmaz
Computer Vision
IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment
Huiyao Zhang, Jin Bai, Rui Guo et al. (+3 more)
Prompt-conditioned foundation segmenters have emerged as a dominant paradigm for image
segmentation, where explicit spatial
prompts(e.g., points, boxes, masks) guide mask decoding. However, many real-world deployments require fully automatic
segmentation, creating a structural mismatch: the decoder expects
prompts that are unavailable at inference. Existing adaptations typically modify intermediate features, inadvertently bypassing the model's native
prompt interface and weakening
prompt-conditioned decoding. We propose IP-SAM, which revisits adaptation from a
prompt-space perspective through
prompt-space conditioning. Specifically, a Self-
Prompt Generator (SPG) distills image context into complementary intrinsic
prompts that serve as coarse regional anchors. These cues are projected through SAM2's frozen
prompt encoder, restoring
prompt-guided decoding without external intervention. To suppress background-induced false positives,
Prompt-Space Gating (PSG) leverages the intrinsic background
prompt as an asymmetric suppressive constraint prior to decoding. Under a deterministic no-external-
prompt protocol, IP-SAM achieves state-of-the-art performance across four camouflaged object detection
benchmarks with only 21.26M trainable parameters. Furthermore, the proposed conditioning strategy generalizes beyond COD to medical polyp
segmentation.
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All authors Huiyao Zhang, Jin Bai, Rui Guo, JianWen Tan, HongFei Wang, Ye Li
Computer Vision
SaSaSaSa2VA: 2nd Place of the 5th PVUW MeViS-Text Track
Dengxian Gong, Quanzhu Niu, Shihao Chen et al. (+6 more)
Referring
video object
segmentation (RVOS) commonly grounds targets in
videos based on static textual cues. MeViS
benchmark extends this by incorporating motion-centric expressions (referring &
reasoning motion expressions) and introducing no-target queries. Extending SaSaSa2VA, where increased input frames and [SEG]
tokens already strengthen the Sa2VA backbone, we adopt a simple yet effective target existence-aware verification mechanism, leading to Still Awesome SaSaSa2VA (SaSaSaSa2VA). Despite its simplicity, the method achieves a final score of 89.19 in the 5th PVUW Challenge (MeViS-Text Track), securing 2nd place. Both quantitative results and ablations suggest that this existence-aware verification strategy is sufficient to unlock strong performance on motion-centric referring tasks.
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All authors Dengxian Gong, Quanzhu Niu, Shihao Chen, Yuanzheng Wu, Yikang Zhou, Tao Zhang, Haobo Yuan, Lu Qi, Shunping Ji
Computer Vision
Diagnosing and Repairing Unsafe Channels in Vision-Language Models via Causal Discovery and Dual-Modal Safety Subspace Projection
Jinhu Fu, Yihang Lou, Qingyi Si et al. (+3 more)
Large
Vision-
Language Models (LVLMs) have achieved impressive performance across
multimodal understanding and
reasoning tasks, yet their internal safety mechanisms remain opaque and poorly controlled. In this work, we present a comprehensive framework for diagnosing and repairing unsafe channels within LVLMs (CARE). We first perform causal mediation analysis to identify
neurons and layers that are causally responsible for unsafe behaviors. Based on these findings, we introduce a dual-modal safety subspace projection method that learns generalized safety subspaces for both visual and textual modalities through generalized eigen-decomposition between benign and malicious activations. During inference, activations are dynamically projected toward these safety subspaces via a hybrid fusion mechanism that adaptively balances visual and textual corrections, effectively suppressing unsafe features while preserving semantic fidelity. Extensive experiments on multiple safety
benchmarks demonstrate that our causal-subspace repair framework significantly enhances safety robustness without degrading general
multimodal capabilities, outperforming prior activation steering and
alignment-based baselines. Additionally, our method exhibits good transferability, defending against unseen attacks.
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All authors Jinhu Fu, Yihang Lou, Qingyi Si, Shudong Zhang, Yan Bai, Sen Su
Computer Vision
An Instance-Centric Panoptic Occupancy Prediction Benchmark for Autonomous Driving
Yi Feng, Junwu E, Zizhan Guo et al. (+3 more)
Panoptic occupancy prediction aims to jointly infer voxel-wise semantics and instance identities within a unified 3D scene representation. Nevertheless, progress in this field remains constrained by the absence of high-quality 3D mesh resources, instance-level annotations, and physically consistent occupancy
datasets. Existing
benchmarks typically provide incomplete and low-resolution geometry without instance-level annotations, limiting the development of models capable of achieving precise
geometric reconstruction, reliable occlusion
reasoning, and holistic 3D understanding. To address these challenges, this paper presents an instance-centric
benchmark for the 3D panoptic occupancy prediction task. Specifically, we introduce ADMesh, the first unified 3D mesh library tailored for autonomous driving, which integrates over 15K high-quality 3D models with diverse textures and rich semantic annotations. Building upon ADMesh, we further construct CarlaOcc, a large-scale, physically consistent panoptic occupancy
dataset generated using the CARLA simulator. This
dataset contains over 100K frames with fine-grained, instance-level occupancy ground truth at voxel resolutions as fine as 0.05 m. Furthermore, standardized evaluation metrics are introduced to quantify the quality of existing occupancy
datasets. Finally, a systematic
benchmark of representative models is established on the proposed
dataset, which provides a unified platform for fair comparison and reproducible research in the field of 3D panoptic perception. Code and
dataset are available at https://mias.group/CarlaOcc.
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All authors Yi Feng, Junwu E, Zizhan Guo, Yu Ma, Hanli Wang, Rui Fan
Computer Vision
NimbusGS: Unified 3D Scene Reconstruction under Hybrid Weather
Yanying Li, Jinyang Li, Shengfeng He et al. (+3 more)
We present NimbusGS, a unified framework for reconstructing high-quality 3D scenes from degraded multi-view inputs captured under diverse and mixed adverse weather conditions. Unlike existing methods that target specific weather types, NimbusGS addresses the broader challenge of generalization by modeling the dual nature of weather: a continuous, view-consistent medium that attenuates light, and dynamic, view-dependent particles that cause scattering and occlusion. To capture this structure, we decompose degradations into a global transmission field and per-view particulate residuals. The transmission field represents static atmospheric effects shared across views, while the residuals model transient disturbances unique to each input. To enable stable geometry learning under severe visibility degradation, we introduce a geometry-guided gradient scaling mechanism that mitigates gradient imbalance during the self-supervised optimization of 3D Gaussian representations. This physically grounded formulation allows NimbusGS to disentangle complex degradations while preserving scene structure, yielding superior geometry reconstruction and outperforming task-specific methods across diverse and challenging weather conditions. Code is available at https://github.com/lyy-ovo/NimbusGS.
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All authors Yanying Li, Jinyang Li, Shengfeng He, Yangyang Xu, Junyu Dong, Yong Du
Computer Vision
Jaeseong Kim, Chaehwan Lim, Sang Hyun Gil et al. (+1 more)
We present EuraGovExam, a
multilingual and
multimodal benchmark sourced from real-world civil service examinations across five representative Eurasian regions: South Korea, Japan, Taiwan, India, and the European Union. Designed to reflect the authentic complexity of public-sector assessments, the
dataset contains over 8,000 high-resolution scanned multiple-choice questions covering 17 diverse academic and administrative domains. Unlike existing
benchmarks, EuraGovExam embeds all question content–including problem statements, answer choices, and visual elements–within a single image, providing only a minimal standardized instruction for answer formatting. This design demands that models perform layout-aware, cross-lingual
reasoning directly from visual input. All items are drawn from real exam documents, preserving rich visual structures such as tables,
multilingual typography, and form-like layouts. Evaluation results show that even state-of-the-art
vision-
language models (VLMs) achieve only 86% accuracy, underscoring the
benchmark's difficulty and its power to diagnose the limitations of current models. By emphasizing cultural realism, visual complexity, and linguistic diversity, EuraGovExam establishes a new standard for evaluating VLMs in high-stakes,
multilingual, image-grounded settings. It also supports practical applications in e-governance, public-sector document analysis, and equitable exam preparation.
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All authors Jaeseong Kim, Chaehwan Lim, Sang Hyun Gil, Suan Lee
Computer Vision
HD-VGGT: High-Resolution Visual Geometry Transformer
Tianrun Chen, Yuanqi Hu, Yidong Han et al. (+11 more)
High-resolution imagery is essential for accurate 3D reconstruction, as many
geometric details only emerge at fine spatial scales. Recent feed-forward approaches, such as the Visual Geometry Grounded
Transformer (VGGT), have demonstrated the ability to infer scene geometry from large collections of images in a single forward pass. However, scaling these models to high-resolution inputs remains challenging: the number of
tokens in
transformer architectures grows rapidly with both image resolution and the number of views, leading to prohibitive computational and
memory costs. Moreover, we observe that visually ambiguous regions, such as repetitive patterns, weak textures, or specular surfaces, often produce unstable feature
tokens that degrade
geometric inference, especially at higher resolutions. We introduce HD-VGGT, a dual-branch architecture for efficient and robust high-resolution 3D reconstruction. A low-resolution branch predicts a coarse, globally consistent geometry, while a high-resolution branch refines details via a learned feature upsampling module. To handle unstable
tokens, we propose Feature Modulation, which suppresses unreliable features early in the
transformer. HD-VGGT leverages high-resolution images and supervision without full-resolution
transformer costs, achieving state-of-the-art reconstruction quality.
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All authors Tianrun Chen, Yuanqi Hu, Yidong Han, Hanjie Xu, Deyi Ji, Qi Zhu, Chunan Yu, Xin Zhang, Cheng Chen, Chaotao Ding, Ying Zang, Xuanfu Li, Jin Ma, Lanyun Zhu
Computer Vision
LightMover: Generative Light Movement with Color and Intensity Controls
Gengze Zhou, Tianyu Wang, Soo Ye Kim et al. (+7 more)
We present LightMover, a framework for controllable light manipulation in single images that leverages
video diffusion priors to produce physically plausible illumination changes without re-rendering the scene. We formulate light editing as a sequence-to-sequence prediction problem in visual
token space: given an image and light-control
tokens, the model adjusts light position, color, and intensity together with resulting reflections, shadows, and falloff from a single view. This unified treatment of spatial (movement) and appearance (color, intensity) controls improves both manipulation and illumination understanding. We further introduce an adaptive
token-
pruning mechanism that preserves spatially informative
tokens while compactly encoding non-spatial attributes, reducing control sequence length by 41% while maintaining editing fidelity. To train our framework, we construct a scalable rendering pipeline that generates large numbers of image pairs across varied light positions, colors, and intensities while keeping the scene content consistent with the original image. LightMover enables precise, independent control over light position, color, and intensity, and achieves high PSNR and strong semantic consistency (DINO, CLIP) across different tasks.
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All authors Gengze Zhou, Tianyu Wang, Soo Ye Kim, Zhixin Shu, Xin Yu, Yannick Hold-Geoffroy, Sumit Chaturvedi, Qi Wu, Zhe Lin, Scott Cohen
Computer Vision
Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation
Guohuan Xie, Xin He, Dingying Fan et al. (+3 more)
Generalized few-shot semantic
segmentation (GFSS) is fundamentally limited by the coverage of novel-class appearances under scarce annotations. While
diffusion models can synthesize novel-class images at scale, practical gains are often hindered by insufficient coverage and noisy supervision when masks are unavailable or unreliable. We propose Syn4Seg, a generation-enhanced GFSS framework designed to expand novel-class coverage while improving pseudo-label quality. Syn4Seg first maximizes
prompt-space coverage by constructing an
embedding-deduplicated
prompt bank for each novel class, yielding diverse yet class-consistent synthetic images. It then performs support-guided pseudo-label estimation via a two-stage refinement that i) filters low-consistency regions to obtain high-precision seeds and ii) relabels uncertain pixels with image-adaptive prototypes that combine global (support) and local (image) statistics. Finally, we refine only boundary-band and unlabeled pixels using a constrained SAM-based update to improve contour fidelity without overwriting high-confidence interiors. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ demonstrate consistent improvements in both 1-shot and 5-shot settings, highlighting synthetic data as a scalable path for GFSS with reliable masks and precise boundaries.
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All authors Guohuan Xie, Xin He, Dingying Fan, Le Zhang, Ming-Ming Cheng, Yun Liu
Computer Vision
Understanding and Mitigating Hallucinations in Multimodal Chain-of-Thought Models
Ji Ma, Wei Suo, Peng Wang et al. (+1 more)
Multimodal Chain-of-Thought (MCoT) models have demonstrated impressive capability in complex visual
reasoning tasks. Unfortunately, recent studies reveal that they suffer from severe
hallucination problems due to diminished visual
attention during the generation process. However, visual
attention decay is a well-studied problem in Large
Vision-
Language Models (LVLMs). Considering the fundamental differences in
reasoning processes between MCoT models and traditional LVLMs, we raise a basic question: Whether MCoT models have unique causes of
hallucinations? To answer this question, we systematically investigate the
hallucination patterns of MCoT models and find that fabricated texts are primarily generated in associative
reasoning steps, which we term divergent thinking. Leveraging these insights, we introduce a simple yet effective strategy that can effectively localize divergent thinking steps and intervene in the decoding process to mitigate
hallucinations. Extensive experiments show that our method outperforms existing methods by a large margin. More importantly, our proposed method can be conveniently integrated with other
hallucination mitigation methods and further boost their performance. The code is publicly available at https://github.com/ASGO-MM/MCoT-
hallucination.
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All authors Ji Ma, Wei Suo, Peng Wang, Yanning Zhang
Computer Vision
Let Triggers Control: Frequency-Aware Dropout for Effective Token Control
Junyoung Koh, Hoyeon Moon, Dongha Kim et al. (+3 more)
Text-to-image models such as Stable
Diffusion have achieved unprecedented levels of high-fidelity visual synthesis. As these models advance, personalization of generative models – commonly facilitated through Low-Rank Adaptation (LoRA) with a dedicated trigger
token – has become a significant area of research. Previous works have naively assumed that fine-tuning with a single trigger
token to represent new concepts. However, this often results in poor controllability, where the trigger
token alone fails to reliably evoke the intended concept. We attribute this issue to the frequent co-occurrence of the trigger
token with the surrounding context during fine-tuning, which entangles their representations and compromises the
token's semantic distinctiveness. To disentangle this, we propose Frequency-Aware Dropout (FAD) – a novel regularization technique that improves
prompt controllability without adding new parameters. FAD consists of two key components: co-occurrence analysis and curriculum-inspired scheduling. Qualitative and quantitative analyses across
token-based
diffusion models (SD~1.5 and SDXL) and natural
language–driven backbones (FLUX and Qwen-Image) demonstrate consistent gains in
prompt fidelity, stylistic precision, and user-perceived quality. Our method provides a simple yet effective dropout strategy that enhances controllability and personalization in text-to-image generation. Notably, it achieves these improvements without introducing additional parameters or architectural modifications, making it readily applicable to existing models with minimal computational overhead.
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All authors Junyoung Koh, Hoyeon Moon, Dongha Kim, Seungmin Lee, Sanghyun Park, Min Song
Computer Vision
K$\alpha$LOS finds Consensus: A Meta-Algorithm for Evaluating Inter-Annotator Agreement in Complex Vision Tasks
David Tschirschwitz and Volker Rodehorst
Progress in object detection
benchmarks is stagnating. It is limited not by architectures but by the inability to distinguish model improvements from label noise. To restore trust in benchmarking the field requires rigorous quantification of annotation consistency to ensure the reliability of evaluation data. However, standard statistical metrics fail to handle the instance correspondence problem inherent to
vision tasks. Furthermore, validating new agreement metrics remains circular because no objective ground truth for agreement exists. This forces reliance on unverifiable heuristics. We propose K$\alpha$LOS (KALOS), a unified meta-
algorithm that generalizes the "Localization First" principle to standardize
dataset quality evaluation. By resolving spatial correspondence before assessing agreement, our framework transforms complex spatio-categorical problems into nominal reliability
matrices. Unlike prior heuristic implementations, K$\alpha$LOS employs a principled, data-driven configuration; by statistically calibrating the localization parameters to the inherent agreement distribution, it generalizes to diverse tasks ranging from bounding boxes to volumetric
segmentation or pose estimation. This standardization enables granular diagnostics beyond a single score. These include annotator vitality, collaboration clustering, and localization sensitivity. To validate this approach, we introduce a novel and empirically derived noise generator. Where prior validations relied on uniform error assumptions, our controllable testbed models complex and non-isotropic human variability. This provides evidence of the metric's properties and establishes K$\alpha$LOS as a robust standard for distinguishing signal from noise in modern computer
vision benchmarks.
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Computer Vision
MotionRFT: Unified Reinforcement Fine-Tuning for Text-to-Motion Generation
Xiaofeng Tan, Wanjiang Weng, Hongsong Wang et al. (+3 more)
Text-to-motion generation has advanced with
diffusion- and flow-based generative models, yet supervised pre-training remains insufficient to align models with high-level objectives such as semantic consistency, realism, and human preference. Existing post-training methods have key limitations: they (1) target a specific motion representation, such as joints, (2) optimize a particular aspect, such as text-motion
alignment, and may compromise other factors; and (3) incur substantial computational overhead, data dependence, and coarse-grained optimization. We present a reinforcement fine-tuning framework that comprises a heterogeneous-representation, multi-dimensional reward model, MotionReward, and an efficient, fine-grained fine-tuning method, EasyTune. To obtain a unified semantics representation, MotionReward maps heterogeneous motions into a shared semantic space anchored by text, enabling multi-dimensional reward learning; Self-refinement Preference Learning further enhances semantics without additional annotations. For efficient and effective fine-tuning, we identify the recursive gradient dependence across denoising steps as the key bottleneck, and propose EasyTune, which optimizes step-wise rather than over the full trajectory, yielding dense, fine-grained, and
memory-efficient updates. Extensive experiments validate the effectiveness of our framework, achieving FID 0.132 at 22.10 GB peak
memory for MLD model and saving up to 15.22 GB over DRaFT. It reduces FID by 22.9% on joint-based ACMDM, and achieves a 12.1% R-Precision gain and 21.7% FID improvement on rotation-based HY-Motion. Our project page with code is publicly available.
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All authors Xiaofeng Tan, Wanjiang Weng, Hongsong Wang, Fang Zhao, Xin Geng, Liang Wang
Computer Vision
Incentivizing Temporal-Awareness in Egocentric Video Understanding Models
Zhiyang Xu, Tian Qin, Bowen Jin et al. (+4 more)
Multimodal large
language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in
egocentric settings where
reasoning depends on the correct ordering and
evolution of events. This deficiency stems in part from training objectives that fail to explicitly reward temporal
reasoning and instead rely on frame-level spatial shortcuts. To address this limitation, we propose Temporal Global Policy Optimization (TGPO), a reinforcement learning with verifiable rewards (RLVR)
algorithm designed to incentivize temporal awareness in MLLMs. TGPO contrasts model outputs generated from temporally ordered versus shuffled
video frames to derive calibrated, globally normalized reward signals that explicitly favor temporally coherent
reasoning. Integrated with GRPO and GSPO, TGPO supports cold-start RL training and effectively suppresses spatial shortcut behaviors learned by existing MLLMs. Experiments across five
egocentric video benchmarks demonstrate that TGPO consistently improves temporal grounding and causal coherence, outperforming prior RL-based
video reasoning approaches. Our results suggest that TGPO offers a simple and scalable pathway toward temporally robust MLLMs for
egocentric video understanding.
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All authors Zhiyang Xu, Tian Qin, Bowen Jin, Zhengfeng Lai, Meng Cao, Lifu Huang, Peng Zhang
Computer Vision
Communicating about Space: Language-Mediated Spatial Integration Across Partial Views
Ankur Sikarwar, Debangan Mishra, Sudarshan Nikhil et al. (+2 more)
Humans build shared spatial understanding by communicating partial, viewpoint-dependent observations. We ask whether
Multimodal Large
Language Models (MLLMs) can do the same, aligning distinct
egocentric views through dialogue to form a coherent, allocentric mental model of a shared environment. To study this systematically, we introduce
COSMIC, a
benchmark for Collaborative Spatial Communication. In this setting, two static MLLM
agents observe a 3D indoor environment from different viewpoints and exchange natural-
language messages to solve spatial queries.
COSMIC contains 899 diverse scenes and 1250 question-answer pairs spanning five tasks. We find a capability hierarchy, MLLMs are most reliable at identifying shared anchor objects across views, perform worse on relational
reasoning, and largely fail at building globally consistent maps, performing near chance, even for frontier models. Moreover, we find thinking capability yields gains in anchor grounding, but is insufficient for higher-level spatial communication. To contextualize model behavior, we collect 250 human-human dialogues. Humans achieve 95% aggregate accuracy, while the best model, Gemini-3-Pro-Thinking, reaches 72%, leaving substantial room for improvement. Moreover, human conversations grow more precise as partners align on a shared spatial understanding, whereas MLLMs keep exploring without converging, suggesting limited capacity to form and sustain a robust shared mental model throughout the dialogue. Our code and data is available at https://github.com/ankursikarwar/
Cosmic.
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All authors Ankur Sikarwar, Debangan Mishra, Sudarshan Nikhil, Ponnurangam Kumaraguru, Aishwarya Agrawal
Computer Vision
Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision
Yizhou Jin, Yuezhu Feng, Jinjin Zhang et al. (+3 more)
Multimodal large
language models (MLLMs) have recently demonstrated remarkable
reasoning and perceptual abilities for anomaly detection. However, most approaches remain confined to image-level anomaly detection and textual
reasoning, while pixel-level localization still relies on external
vision modules and dense annotations. In this work, we activate the intrinsic
reasoning potential of MLLMs to perform anomaly detection, pixel-level localization, and interpretable
reasoning solely from image-level supervision, without any auxiliary components or pixel-wise labels. Specifically, we propose
Reasoning-Driven Anomaly Localization (ReAL), which extracts anomaly-related
tokens from the autoregressive
reasoning process and aggregates their
attention responses to produce pixel-level anomaly maps. We further introduce a Consistency-Guided
Reasoning Optimization (CGRO) module that leverages reinforcement learning to align
reasoning tokens with visual attentions, resulting in more coherent
reasoning and accurate anomaly localization. Extensive experiments on four public
benchmarks demonstrate that our method significantly improves anomaly detection, localization, and interpretability. Remarkably, despite relying solely on image-level supervision, our approach achieves performance competitive with MLLM-based methods trained under dense pixel-level supervision. Code is available at https://github.com/YizhouJin313/ReADL.
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All authors Yizhou Jin, Yuezhu Feng, Jinjin Zhang, Peng Wang, Qingjie Liu, Yunhong Wang
Computer Vision
MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence
Woohyeon Park, Jaeik Kim, Sunghwan Steve Cho et al. (+7 more)
Lesion detection, symptom tracking, and visual explainability are central to real-world medical image analysis, yet current medical
Vision-
Language Models (VLMs) still lack mechanisms that translate their broad knowledge into clinically actionable outputs. To bridge this gap, we present MEDIC-AD, a clinically oriented VLM that strengthens these three capabilities through a stage-wise framework. First, learnable anomaly-aware
tokens (<Ano>) encourage the model to focus on abnormal regions and build more discriminative lesion centered representations. Second, inter image difference
tokens (<Diff>) explicitly encode temporal changes between studies, allowing the model to distinguish worsening, improvement, and stability in disease burden. Finally, a dedicated explainability stage trains the model to generate heatmaps that highlight lesion-related regions, offering clear visual evidence that is consistent with the model's
reasoning. Through our staged design, MEDIC-AD steadily boosts performance across anomaly detection, symptom tracking, and anomaly
segmentation, achieving state-of-the-art results compared with both closed source and medical-specialized baselines. Evaluations on real longitudinal clinical data collected from real hospital workflows further show that MEDIC-AD delivers stable predictions and clinically faithful explanations in practical patient-monitoring and decision-support workflows
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All authors Woohyeon Park, Jaeik Kim, Sunghwan Steve Cho, Pa Hong, Wookyoung Jeong, Yoojin Nam, Namjoon Kim, Ginny Y. Wong, Ka Chun Cheung, Jaeyoung Do
Computer Vision
MultiLoc: Look Around As You Localize for Fast And Robust Visual Re-localization
Nobel Dang and Bing Li
Relative camera pose is a basic
geometric cue for visual re-localization and scene understanding. When using images alone, visual evidence may not sufficiently support reliable estimation of 3D-consistent motion. The challenge is especially acute in unfamiliar and ever-changing environments for real-time applications, where a system must infer spatial structure under tight time constraints from what it sees rather than rely on scene-specific reconstruction or training. This raises a fundamental question: how can live 3D camera motion be estimated accurately for re-localization in unseen environments while drawing on enough scene context to resolve pose ambiguity? We introduce MultiLoc, a multi-view-guided relative pose regressor trained at scale to achieve spatial and
geometric consistent representations for robust visual re-localization in unseen scenarios with high inference speed. Specifically, MultiLoc creates a minimal 3D-sub-scene representation of the environment and efficiently transforms it into 3D spatially and geometrically consistent features in a single forward pass, enabling precise pose estimates with high inference speed gains. Across diverse indoor, outdoor, and in-the-wild visual re-localization
benchmarks—Indoor6, Cambridge Landmarks and WaySpots—MultiLoc consistently outperforms state-of-the-art relative pose regression methods. We also highlight that MultiLoc, a pose regressor, also performs competitively with inference-heavy and structure-based approaches while retaining sub-second inference and generalizing to unseen scenes. Given a small posed support set, it also surpasses relative pose regression, feature-matching, and non-regression methods on several relative camera pose
benchmarks. Code will be released.
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Computer Vision
RiskProp: Collision-Anchored Self-Supervised Risk Propagation for Early Accident Anticipation
Yiyang Zou, Tianhao Zhao, Peilun Xiao et al. (+9 more)
Accident anticipation aims to predict impending collisions from dashcam
videos and trigger early alerts. Existing methods rely on binary supervision with manually annotated "anomaly onset" frames, which are subjective and inconsistent, leading to inaccurate risk estimation. In contrast, we propose RiskProp, a novel collision-anchored self-supervised risk propagation paradigm for early accident anticipation, which removes the need for anomaly onset annotations and leverages only the reliably annotated collision frame. RiskProp models temporal risk
evolution through two observation-driven losses: first, since future frames contain more definitive evidence of an impending accident, we introduce a future-frame regularization loss that uses the model's next-frame prediction as a soft target to supervise the current frame, enabling backward propagation of risk signals; second, inspired by the empirical trend of rising risk before accidents, we design an adaptive monotonic constraint to encourage a non-decreasing progression over time. Experiments on CAP and Nexar demonstrate that RiskProp achieves state-of-the-art performance and produces smoother, more discriminative risk curves, improving both early anticipation and interpretability.
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All authors Yiyang Zou, Tianhao Zhao, Peilun Xiao, Hongyu Jin, Longyu Qi, Yuxuan Li, Liyin Liang, Yifeng Qian, Chunbo Lai, Yutian Lin, Zhihui Li, Yu Wu