Computer Vision
GAP-SAM: A Global Artifact Prior for Generalizable AI-Generated Image Manipulation Localization
Haozhen Yan, Siyuan Shan, Zijian Yu et al. (+4 more)
AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel supervision entangles forensic evidence with
dataset-specific mask geometry and semantic boundaries. Extending image-level distribution
alignment to localization, we construct COCO-ControlNet with source-image Canny edges and depth maps to align semantics and geometry, improving OOD performance across multiple localizers. Yet tighter Mask-VAE Reconstruction
Alignment (Mask-VAE) underperforms COCO-ControlNet, showing that VAE reconstruction artifacts transfer poorly to local
diffusion-inpainting artifacts. We also identify boundary adhesion, where fine-tuned
segmentation models snap predictions to semantic object contours rather than true manipulation boundaries. These findings motivate GAP-SAM, which encodes an image and its frozen VAE reconstruction into a global artifact
token and injects it into SAM3's feature pyramid via zero-gated FiLM before pixel decoding. Without prescribing a spatial region, this
token modulates dense decoding to preserve localization while suppressing semantic-boundary shortcuts. Across six
datasets, GAP-SAM averages 79.8 Pixel-F1, outperforming the strongest prior method by 12.6 points. It also performs best at every tested severity of JPEG compression, Gaussian blur, and resizing.
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All authors Haozhen Yan, Siyuan Shan, Zijian Yu, Youqi Wang, Yan Hong, Jun Lan, Jianfu Zhang
Computer Vision
Semantically Compatible Knowledge Distillation for Cross-Domain Object Detection with Vision Foundation Models
Qifeng Zhang, Ting Xiang, Zeyuan Bai et al. (+1 more)
Vision foundation models (VFMs) offer strong generalization capabilities for domain-adaptive object detection (DAOD). However, existing VFM-based methods overlook the spatial-scale discrepancy between teacher and student feature maps, resulting in semantic incompatibility that weakens both feature
alignment and pseudo-label learning. Moreover, domain shift can cause source-trained VFM teachers to miss target-domain objects, limiting the quality of their pseudo-labels. To address these issues, we propose the Semantic Localization-Enhanced Teacher (SLE-T), a semantically compatible knowledge-
distillation framework built around a lightweight SLE Adapter for DINOv2. SLE Adapter injects pretrained local-texture priors into DINOv2 to improve cross-domain recognition and reformulates its features into dense representations that are spatially and semantically compatible with the student detector. SLE-T transfers the resulting teacher knowledge through either pseudo-label learning or feature
alignment. We instantiate SLE-T with DINOv2-B and DINOv2-L (the ViT-B and ViT-L variants) and compare them with the larger DINOv2-G teacher. Extensive experiments on three DAOD
benchmarks demonstrate that our method achieves state-of-the-art performance, and ablation studies confirm the importance of teacher-student semantic compatibility. Notably, SLE-T with DINOv2-B produces competitive or superior pseudo-labels using approximately one-quarter of the training time of DINOv2-G and substantially less GPU
memory, demonstrating efficient VFM knowledge transfer under limited computational resources.
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All authors Qifeng Zhang, Ting Xiang, Zeyuan Bai, Changjian Chen
Computer Vision
Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization
Zhu Xu, Jiaqi Tang, Pokai Chen et al. (+2 more)
Explainable deepfake detection extends binary classification by requiring models to not only predict authenticity but also provide interpretable justifications. This expanded scope is critical in practice, where users like forensic analysts need insight into the rationale behind the detection. Despite advancements, current approaches suffer from two critical deficiencies: (1)vulnerability to image quality degradation: detection accuracy plummets on low-quality samples, while naive augmentation strategies may induce feature drift and impair performance as diversity expands. (2) factually flawed explanations: explanation models may omit manipulation evidence or hallucinate irrelevant details, undermining interpretability. To address it, we propose a framework with two innovations. For robust deepfake detection, we introduce Feature-robust Augmentation, which comprises diversified degradation-aware augmentation strategies, and a supervised contrastive learning pattern paired with a mean-teacher architecture that stabilizes features against augmentations through consistency constraints. For explanation, we devise an evidence-grounded preference optimization process that guides model to prioritize genuine manipulation traces by learning from chosen-rejected explanation pairs, where rejected samples are constructed via evidence omission or irrelevant information injection. The proposed approach wins the first place in ACM Multimedia 2026 Explainable Deepfake Detection Challenge.The code is available at https://github.com/oceanflowlab/EDD.git.
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All authors Zhu Xu, Jiaqi Tang, Pokai Chen, Yuxin Peng, Yang Liu
Computer Vision
InfinityEdit: Infinite Video Editing with a Lightweight Edit-Ignition Adapter
Yunze Tong, Mushui Liu, Canyu Zhao et al. (+9 more)
With large pretrained models, existing methods have effectively improved instruction-based
video editing. However, most of them rely on an in-place editing assumption. They align the edited
video with the given source clip frame by frame over a fixed time span. This pattern fails for open-ended streams, e.g., restyling a live game or applying a camera move to an ongoing shot. In such cases, edits must extend to future frames as they arrive, rather than be applied to a static input clip. In this paper, we study this setting and name it infinite
video editing: given a preceding segment and an edit request, a model must generate the next segment that continues the stream while applying the requested edit. This process repeats as an unbounded sequence of edit instructions arrives. This task brings two challenges: the edit must be a faithful continuation rather than a frame-wise rewrite, and generation quality must remain stable as edits accumulate. To address them, we first design a data-collection pipeline for infinite
video editing. Based on the collected data, we propose InfinityEdit, a lightweight edit adapter that equips a streaming
video generator with unbounded editing ability. The adapter contains three
attention modules. History cross-
attention guides the denoising frames using the input frames. Temporal causal self-
attention keeps temporal cues flowing only from earlier frames to later ones. Edit cross-
attention injects the edit request into generation. During inference, the adapter is activated only in the chunk where an edit request arrives. Subsequent chunks are generated by the original model with a reset anchor frame. This scheme applies the edit while preserving the original model's infinite generation ability. Extensive experiments show that InfinityEdit faithfully continues the stream under each edit, and stays stable over unbounded edit sequences.
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All authors Yunze Tong, Mushui Liu, Canyu Zhao, Shiyi Zhang, Didi Zhu, Peng Zhang, Wanggui He, Jinlong Liu, Ying Chen, Hao Jiang, Pipei Huang, Bo Zheng
Computer Vision
EmotionDialogCN: A Spontaneous Multimodal Dataset for Mandarin Emotional Dialogue
Yi Zheng, Yifan Xu, Yan Zhou et al. (+8 more)
Face-to-face audiovisual interaction is central to human communication, conveying rich emotional and social cues. However, existing
multimodal dialogue
datasets remain limited by inadequate
emotion annotations, poor emotional diversity, and small scale. We introduce EmotionDialogCN, a large-scale audiovisual-emotional
dataset designed to capture authentic face-to-face communication. It contains 21,880 dialogue sessions performed by 119 professional actors across 20 everyday scenarios, covering 18
emotion categories with over 400 hours of recordings, the largest and most comprehensive
dataset of its kind. A novel data collection framework minimizes equipment interference, enabling natural and nuanced emotional expressions. EmotionDialogCN achieves an
emotion distribution deviation of 0.64 from real human
emotion statistics (versus 5.65 for prior
datasets) and consistent subject framing (52-59% frame occupancy). Together, these properties translate into stable unimodal and
multimodal performance across acoustic, lexical, and visual modalities, with fusion results further underscoring strong
multimodal alignment and cross-modal complementarity.
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All authors Yi Zheng, Yifan Xu, Yan Zhou, Hejia Chen, Chunyu Qiang, Xiaoqiang Liu, Xiaohan Li, Shenze Huang, Yue Zhang, Guoying Zhao, Pengfei Wan
Computer Vision · Cross-listed from cs.RO
IMU-Free Body-Frame State Estimation with Sparse Scene Flow for Quadcopters
Daniel Grønhaug, Sofie Markeset and Mathias Kolberg
We present a
vision-only state estimation system for X-configuration quadcopters equipped with a canonical stereo camera pair and no inertial sensors. The system operates entirely in the body frame, requiring only synchronised stereo images and motor thrust commands. A continuous-discrete extended Kalman filter on a composite
manifold state $\langle SE(3), \mathbb{R}^3, \ldots \rangle$ maintains estimates of body-frame pose, velocity, angular velocity,
gravity, and disturbances, using stationary scene points as implicit inertial references. Feature points are detected (FAST, Shi-Tomasi), tracked temporally (SSD, Lucas-Kanade) and matched across cameras (NCC), with search regions predicted from filter-derived pose and point uncertainty. Chi-squared gating on the normalised innovation admits only stationary points to the filter. The system also produces a sparse 3D point cloud carrying per-point position, velocity and joint covariance. These come from a 4-view (two stereo pairs at two timestamps) full bundle adjustment that jointly estimates position and velocity from stereo disparity and temporal parallax, with the filter-derived relative pose as a prior. Feature points in the EKF do not enter the solver; their information is reflected through the pose prior. Point cloud density is spatially adaptive: an external focus point directs allocation, producing dense coverage in the region of
attention and sparse coverage elsewhere. The output is a body-frame state estimate, a calibrated pose change, and a sparse scene flow. It is intended as a measurement source for a downstream world model anchored in the current body frame, without dependence on GPS, IMU, or any world-frame infrastructure, though the architecture accommodates their future integration.
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Computer Vision
A Collaborative Multi-Modality Interaction for VLA-based End-to-End Autonomous Driving
Jingtao Sun, Xiaohai He, Yike Zhang et al. (+4 more)
Vision-
Language-Action (VLA) models have emerged as a powerful paradigm for end-to-end autonomous driving by jointly integrating perception,
reasoning, and decision making within a unified
multimodal framework. However, most existing VLA models formulate end-to-end autonomous driving as a visual question answering task, leading to unreliable and less interpretable decision
reasoning. In addition, they fail to establish effective multi-modal interaction across heterogeneous sensors, thereby limiting robust scene perception and reliable driving
reasoning in long-tail driving scenarios. To this end, we propose a robust VLA-based end-to-end autonomous driving system that combines multi-modality interaction with multi-trajectory planning and optimization, enabling more reliable, interpretable, and safer driving decisions. Our method comprises three core components: (1) Affinity-Guided Optimal Transport for main-auxiliary modality two-way interaction; (2) Distribution-Consistent Modality Transfer for heterogeneous modality distribution transfer and cross-modal interaction; (3) Multi-modal Multi-Trajectory Planning along with Perception-Oriented Trajectory Refinement for better driving decisions to long-tail driving scenarios. Experimental results in open-loop and closed-loop
datasets demonstrate improvements in safety long-horizon driving
reasoning and road scene perception over existing driving systems, highlighting the ability of our mutli-modality interaction and multi-trajectory planning and optimization for scalable VLA-based systems.
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All authors Jingtao Sun, Xiaohai He, Yike Zhang, Dong Huang, Yaonan Wang, Ajmal Mian, Mike Zheng Shou
Computer Vision
EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking
Enjun Du, Siyi Liu, Zirong Chen et al. (+8 more)
Real-world image search queries are
multimodal and compositional: “find this shirt in pink” specifies an entity to retain, an attribute to modify, and context to ignore. Yet existing re-rankers either compress such multifaceted relevance into an opaque
embedding or rely on free-form chain-of-thought that easily omits or hallucinates fine-grained constraints. Drawing on rubric- and checklist-based evaluation from NLP, we recast
multimodal image re-ranking as a semantic constraint satisfaction problem and propose EviRank, which parses any query - text-only, image-only, or composed - into a unified evidence package: typed criteria across six semantic slots (e.g., entities, attributes, relations), each labelled required, forbidden, or ignorable. Re-ranking then reduces to evidence-conditioned verification, combining deterministic rubric scoring and evidence-grounded listwise comparison in a single training-free procedure. The explicit evidence can further serve as structured supervision for optionally distilling a lightweight student. Across five
benchmarks spanning text-to-image, image-to-image, and composed image
retrieval, EviRank achieves state-of-the-art performance, and the distilled student preserves over 90% of the teacher's capability at substantially lower cost.
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All authors Enjun Du, Siyi Liu, Zirong Chen, Xinyu Zuo, Jinwen Luo, Ruiwen Tao, Lisheng Duan, Haijin Liang, Jin Ma, Junfu Pu, Yongqi Zhang
Computer Vision
Breaking High Confidence: Practical Face Impersonation under High-Security Thresholds
Changjin Kim, Seunghun Paik, Dongsoo Kim et al. (+1 more)
Face recognition systems (FRSs) are increasingly deployed in critical real-world services for authentication, such as banking applications and airport identity checks, necessitating stringent security configurations. Consequently, the security vulnerabilities of FRSs have garnered significant
attention. While existing studies have extensively explored FRS security, prior analyses have primarily focused on medium-security threshold settings, which are not directly applicable to FRSs operating under high-security constraints. In this paper, we propose the first successful impersonation attack against FRSs under high-security threshold settings. Among various threat models, we focus on a practical and challenging scenario: score-based impersonation attacks under strict rate limits. To precisely evaluate the feasibility of such attacks, we provide a principled mathematical analysis characterizing the gaps in each stage of the attack pipeline. Our method significantly enhances impersonation capabilities in score-based attacks, even under elevated decision thresholds. On the LFW
benchmark, with a budget of only 100 confidence score queries per identity, our attack achieves an impersonation success rate exceeding 92% against Amazon Rekognition at a confidence score threshold of 99-recommended setting for law enforcement scenarios. We further observe consistently robust performance across multiple open-source FRSs evaluated at similarly stringent decision thresholds.
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All authors Changjin Kim, Seunghun Paik, Dongsoo Kim, Jae Hong Seo
Computer Vision
LoRC: Detecting AI-Generated Images via Low-Rank Collapse in Semantic Residuals
Haozhen Yan, Ruoxin Chen, Jiahui Zhan et al. (+6 more)
Modern generators faithfully model macroscopic semantics, producing synthetic images that appear highly realistic. Consequently, decisive forensic cues reside in subtle non-semantic visual discrepancies. To reveal these cues, we revisit AIGI detection from a
geometric perspective and identify an architecture-agnostic signature. Specifically, modern generators exhibit low-rank collapse (i.e., rank degeneracy) in the semantic-residual orthogonal subspace while largely preserving the dominant semantic direction. This structural flattening consistently emerges during the final decoding stage, forming a shared bottleneck across diverse generator architectures. Motivated by this signature, we propose LoRC, a framework that decouples semantic dominance to capture the collapsed residual geometry induced by the generative decoding bottleneck. Our method improves accuracy by an average of 7.0% across multiple
benchmarks and achieves 97.0% accuracy on 39 unseen generators. These results demonstrate strong cross-model generalization and robustness, making LoRC a reliable approach for AIGI detection in complex real-world environments.
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All authors Haozhen Yan, Ruoxin Chen, Jiahui Zhan, Bo Wang, Youchang Xiao, Shouhong Ding, Liqing Zhang, Taiping Yao, Jianfu Zhang
Computer Vision
Multi-Modal Traffic Sign Detection with Semantic Attributes for Autonomous Driving
Meda Lazar, Sourab Sridhar, Shashwata Gupta et al. (+3 more)
Reliable traffic sign detection is a prerequisite for the global deployment of autonomous driving systems, where regulatory compliance and road safety depend on perceiving signs correctly across regions, ranges, and weather conditions. Despite recent progress,
vision-based methods continue to face three fundamental limitations: poor cross-regional generalization due to high diversity across countries, degraded performance on small-object detection at long ranges (traffic signs occupy as little as $10{\times}10$ pixels at 200m), and fragile temporal tracking under the strongly non-linear perspective distortion that occurs as a vehicle approaches a sign. In this paper, we address the problem of robust, long-range, region-agnostic traffic sign perception by combining camera and Light Detection and Ranging (
LiDAR) sensing. We present a multi-modal detection framework whose Intensity-Aware Deformable Fusion module aligns retro-reflective
LiDAR cues with camera features, anchoring detection on
geometric invariants rather than region-specific visual appearance. We further introduce a dual motion-model tracker that explicitly accounts for non-linear perspective transformations during vehicle approach, substantially improving temporal consistency over linear motion assumptions. Additionally, we develop a semantic attribute classification pipeline that estimates occlusion level, readability, sign embeddedness, and road relevance, providing actionable context to downstream planning. Extensive evaluation on our
dataset, spanning 60+ countries and 2,500+ hours of driving data, shows that the proposed pipeline achieves an Object Miss Ratio (OMR) of 0.49% across 221,068 evaluation sequences, demonstrating globally generalizable traffic sign perception in commercial-grade autonomous driving systems.
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All authors Meda Lazar, Sourab Sridhar, Shashwata Gupta, Alexandra Tripcea, Varun Ravi, Senthil Yogamani
Computer Vision
RDANet: Relative Degradation Aware Network for Infrared Small Target Detection
Rui Liu, Jing Nie and Ying Fu
Infrared small target detection is still challenging in remote sensing imagery, because the targets are extremely small, exhibit weak local contrast, and are often embedded in complex and highly variable backgrounds. In addition to these inherent difficulties, we observe that existing detectors often show unstable performance when the target scale changes or when the scene background varies. This scale- and scene-sensitive degradation indicates that current methods are insufficient in simultaneously preserving target structure during feature downsampling and maintaining discriminative local contrast under background shifts, which finally results in unbalanced detection performance across different conditions. To improve detection robustness, this paper proposes a Relative Degradation Aware Network (RDANet) for infrared small target detection. RDANet consists of two dedicated modules: Multi-Scale Anti-Alias Downsampling (MSAD) and Prototype-Guided Skip
Memory (PGSM). MSAD introduces multi-scale anti-alias filtering together with pixel-fold aggregation to reduce aliasing effects during resolution reduction, so that target shape information can be better preserved while irrelevant background responses are suppressed. PGSM further enhances the skip features by retrieving patch-level prototypes from a shared
memory and adaptively integrating them into the current representation, which helps maintain stable local contrast cues under diverse scene backgrounds. Experiments on three public
benchmarks show that RDANet achieves the best performance on most evaluation metrics, while scale- and background-stratified evaluations indicate more stable behavior across target sizes and scene complexity. The code is available at https://github.com/BIT-RuiLiu/RDANet.
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Computer Vision
Identify, Locate, Link: End-to-End Key-Value Extraction from Document Images
A. Said Gurbuz, Ahmed Nassar, Christoph Auer et al. (+8 more)
Document processing pipelines traditionally cascade optical character recognition (OCR) engines with downstream models for structured information extraction, leading to multi-stage error propagation. We fine-tune SmolDocling, a compact 256M-parameter
vision-
language model (VLM), to perform end-to-end key-value extraction directly from document images, jointly solving identification, localization, and association in a single pass without OCR preprocessing. We extend DocTags with specialized key, value, region, and link tags, enabling many-to-many relationships in a unified output sequence. To address data limitations, we design an augmentation pipeline combining synthetic form filling and graph-based crops that preserve complete key-value subgraphs. We further introduce a layout-aware evaluation framework extending text matching with spatial bounding box verification. On FUNSD, XFUND, and a large-scale private
dataset, our model outperforms larger zero-shot VLM baselines under layout-aware evaluation, while being 27 times smaller than Qwen2.5-VL (7B) and over 5 times faster at inference. The model weights will be released publicly after publication.
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All authors A. Said Gurbuz, Ahmed Nassar, Christoph Auer, Maksym Lysak, Lucas Morin, Matteo Omenetti, Tim Strohmeyer, Panagiotis Vagenas, Nikolaos Livathinos, Michele Dolfi, Peter Staar
Computer Vision · Cross-listed from cs.IR
KoViDoRe: A Benchmark for Korean Visual Document Retrieval
Yongbin Choi, Yongwoo Song and Mujeen Sung
Recent advances in
multimodal retrieval have improved the ability to retrieve information from visually rich documents such as PDFs and reports. However, existing
benchmarks remain largely centered on English and provide limited coverage of Korean visual documents with complex structures. Furthermore, most existing Korean resources primarily evaluate single-page
retrieval, failing to capture realistic scenarios that require evidence aggregation across multiple pages. To address these gaps, we introduce KoViDoRe, a
benchmark for Korean visual document
retrieval. The
dataset is constructed from publicly available Korean documents with diverse layouts, including tables, figures, and multi-column structures. We develop a multi-stage data curation pipeline consisting of structured document parsing, synthetic query generation using both summary-based and context-based strategies, and relevance mapping with human verification. Using KoViDoRe, we evaluate a wide range of
multimodal retrieval models and observe that current models struggle to effectively handle Korean visual document
retrieval, particularly in settings involving structured content and diverse query types. Motivated by this finding, we further curate a large-scale training
dataset, Ko-VDR Train Public, to support the development of
retrieval models tailored to Korean visual documents. Together, KoViDoRe and Ko-VDR Train Public provide a unified
benchmark and training resource for Korean visual document
retrieval.
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Computer Vision · Cross-listed from cs.LG
Chenghao Li, Xiao Han, Xinxin Huang et al. (+22 more)
The
matrix-aware optimizer
Muon improves large model training by balancing updates across singular directions, yet its scaling behavior and end-to-end efficiency on large
Diffusion Transformers (DiTs) remain unclear. We first establish
Muon's scaling behavior on DiTs from 1.3B to 15B parameters, showing that its optimization and generative quality advantages over AdamW persist across model scales. However, at scale, the 5-step Newton–Schulz iteration (NS5) performed at every optimization step, together with full-momentum materialization, introduces substantial computation and communication overhead that can offset
Muon's step-efficiency advantage. We introduce Periodic Row-wise
Muon, which performs a full NS5 spectral update once every \(K\) steps and applies a low compute and communication cost row-wise constrained update based on the current momentum at the remaining steps. We further co-design a distributed implementation that operates directly on sharded momentum during non-refresh steps and accelerates spectral refreshes through bucketed all-gather and communication–computation overlap. Across all scales,
Muon improves the best observed generative quality over AdamW by 12.9–19.1%. Compared with vanilla
Muon, Periodic Row-wise
Muon remains within 0.5% in best generative quality on the 1.3B–4B models and improves it by 4.5% at 9B. It reduces optimizer time by 46.9–54.3%, end-to-end step time by 15.7–24.3%, and logical communication volume by 66.7%, while reaching its respective best generative quality with 33.7–64.8% less active training time. These results show that Periodic Row-wise
Muon preserves
Muon's generative quality advantage while translating it into end-to-end training efficiency for large DiTs.
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All authors Chenghao Li, Xiao Han, Xinxin Huang, Wei Liu, Boyang Li, Bing Xiao, Heran Zhang, Juanma Perez Rua, Ke Xu, Kangning Liu, Linjun Kuang, Na Li, Tan Wang, Tian Xie, Wei Peng, Yang Pei, Yifan Xu, Yuanhao Zhai, Yuwei Lin, Zhe Wang, Zihao He, Daniel Li, Junbiao Tang, Ziyang Jiang, Dake Chen
Computer Vision
Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision
Beibei Zhang, Chao Xu, Jun Lan et al. (+4 more)
Though
Multimodal Large
Language Models (MLLMs) have shown impressive potential in
video understanding, long
video understanding (LVU) remains challenging since distracting noise in complex and lengthy contexts can obscure localized details, misleading MLLMs to produce incorrect answers. Recent works mitigate these issues by incentivizing deep
reasoning to include relevant evidence. However, these methods have two main problems: First, the reinforcement fine-tuning framework (RFT) they leveraged incurs substantial training overheads, including high annotation costs and complicated reward designs. Second, the self-reflective and iterative-perception mechanism in some methods causes lengthy outputs and high inference latency. To alleviate these problems, we propose a novel Segment-to-
Video Supervision} method (S2V) to efficiently enhance fine-grained
reasoning in LVU. Specifically, we generate question answer pairs (VQA) based on localized segments, and then transfer these segment-based VQA back to the whole
video for training. Due to focusing on short segments, segment-based VQA can naturally notice details which tend to be overlooked from a whole-
video perspective. Training on such data can enforce MLLMs to correctly associate fine-grained details with QA while avoiding distracting noise in the whole
video. The S2V training involves just reinforcement learning (RL) with a simple accuracy reward based on only 10K VQA samples and the resulting S2V model predicts answer using a single forward pass with limited output
tokens. Experimental results demonstrate that S2V can consistently improve LVU performance across multiple LVU
benchmarks, outperforming both general MLLMs and
reasoning-based methods not only in LVU accuracy but also in training and inference efficiency.
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All authors Beibei Zhang, Chao Xu, Jun Lan, Zongyi Li, Lai Wei, Huijia Zhu, Tongwei Ren
Computer Vision · Cross-listed from cs.MM
When Images Look Right and Retrieve Wrong: Coverage-Guided Cross-Scale Re-Indexing for Knowledge-Faithful Generative Perception
Guangyuan Dong, Chuang Liu, Haoyu Wang et al. (+10 more)
Multimodal information systems increasingly route generated visual content back through the same
vision-
language index that informed its production, so the output must remain retrievable by the queries it was meant to serve. When the scene contains entities at vastly different scales, existing
language-guided generators condition on a single, globally pooled text
embedding and quietly drop scale-specific concepts, breaking concept-query
retrieval even when pixel fidelity is high. We formalise this failure as semantic collapse and propose CERES, a closed-loop
multimodal indexing framework that builds a three-level semantic pyramid, mines implicit concepts via a co-occurrence-aware router, performs scale-routed cross-
attention into a lightweight U-Net generator, and verifies coverage by re-indexing the generated image with the same frozen VLM. A continuously differentiable soft-Jaccard coverage objective returns dense gradients to the 0.39 M-parameter generator under explicit non-degeneracy conditions, and coverage is verified by an independent DINOv2 linear probe trained only on external scene and object labels. On four pansharpening
benchmarks across seven settings, CERES delivers the new state of the art with the largest gains where scale variation is most extreme (+4.64% relative Q2n and +9.7 mAP for DOTA detection). It also improves concept-query
retrieval Recall@5 by +14.0 points and image-text mean reciprocal rank by 0.19 over the strongest baseline, showing that the closed loop preserves queryable content rather than self-referential feature consistency.
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All authors Guangyuan Dong, Chuang Liu, Haoyu Wang, Yangchen Zeng, Jiaqi Zhang, Li Jiuxing, Xiaoyang Yu, Pinlong Zhao, Yuchao Hou, Ziwei Li, Zheng Lin, Alexander Lim Han Yang, Yusen Wu
Computer Vision
TRACE: Training-time Report-guided and Clinically Ordered Concept Editing
Wentao Yue, Tianyou Lai, Jiayu Luo et al. (+4 more)
Breast ultrasound diagnosis relies on clinically meaningful semantic concepts, yet most deep learning methods adopt end-to-end image-to-label paradigms that lack interpretability and robustness. While concept-based approaches offer a promising alternative, they often assume complete annotations or require
multimodal inputs at inference, which significantly limits their real-world applicability. To tackle these issues, we propose Training-time Report-guided and Clinically Ordered Concept Editing (TRACE), a training-time report-guided framework that leverages structured radiology reports as privileged concept supervision while enabling image-only diagnosis at test time. TRACE refines image-derived concepts through a teacher-guided editing mechanism within a malignancy-aware ordered concept space. To address incomplete annotations, we introduce Strategic Concept Missing Training (SCMT) and train an image-only self-editor via edit
distillation for autonomous concept refinement. Besides, we introduce BUSC, a concept-enriched
benchmark linking images, labels, and structured attributes. Experiments across multiple
datasets demonstrate that TRACE achieves superior performance and improved cross-domain robustness compared to existing methods.
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All authors Wentao Yue, Tianyou Lai, Jiayu Luo, Qingyu Mao, Ziying Wang, Zhenyuan Ning, Qilei Li
Computer Vision
Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding
Tianyue Wang, Xuying Wu, Yuxiang Ma et al. (+7 more)
Long-form
video understanding remains challenging for
video agents due to the mismatch between query demands and evidence acquisition strategies. Although recent planning-before-perception methods outperform query-agnostic pipelines, they often rely on a single dominant strategy, either generation-based strategy or
retrieval-based strategy, limiting their ability to handle diverse query demands. We propose Route2Look, a lightweight and model-agnostic framework for query-adaptive evidence acquisition in long-form
video understanding. Route2Look operates in a Route-Look-Memorize loop with three tools: Global Browse for holistic context, Temporal Ground for explicit temporal cues, and Semantic Retrieve for semantic search. The core component is a routing policy that dynamically selects evidence acquisition tools based on the query. To build this policy, Route2Look adopts a two-stage design: first distilling the routing skill from differential contrastive analysis between generation-based and
retrieval-based trajectories, and then applying the distilled skill with hard routing rules and continue-or-stop criteria during inference. Experiments on challenging long-
video benchmarks show that Route2Look achieves state-of-the-art performance while maintaining strong frame efficiency across
datasets and query types. Oracle routing analysis further reveals the potential of query-adaptive evidence acquisition for future long-form
video understanding.
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All authors Tianyue Wang, Xuying Wu, Yuxiang Ma, Ruiming Liang, Jiaxuan Kang, Yanchao Hao, Zheng Wei, Leigang Qu, Haiyun Guo, Jinqiao Wang
Computer Vision · Cross-listed from cs.GR
CubicSplat: Differentiable Vector Graphics via Error-Bounded Forward Relaxation
Chenglong Liu, Xin Zhang, Yimeng Zhu et al. (+5 more)
Vector graphics are prized for their resolution independence, compact storage, and direct editability, making differentiable optimization of their parametric primitives an attractive goal. Yet classical rasterization is discontinuous with respect to geometry, and existing remedies that smooth the forward pass demand increasingly elaborate heuristics as scene complexity grows. We trace this fragility to a gradient seesaw: design choices that improve forward
geometric exactness can systematically degrade the induced gradient signal, and vice versa. To navigate this tension we introduce CubicSplat, a differentiable vector rasterizer that replaces Bézier closest-point solvers with uniform polyline surrogates whose
geometric error is bounded at $O(S^{-2})$. The resulting static computation graph yields well-conditioned gradients by construction, while a compositing-derived visibility mechanism prunes degenerate primitives without auxiliary regularization. On DIV2K and Kodak
benchmarks CubicSplat achieves state-of-the-art reconstruction quality with over 2 dB PSNR gain in the closed-fill setting, while training up to 4x faster than prior methods. The code is available at https://github.com/CubicSplat/repo
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All authors Chenglong Liu, Xin Zhang, Yimeng Zhu, Liyang He, Yixiao Ma, Yu Su, Zhenya Huang, Qi Liu
Computer Vision
CertVLA: Certified Defense against Physical Visual Attacks for Vision-Language-Action Models
Hui Lu, Zhijie Peng, Yuqi Lin et al. (+8 more)
Vision-
Language-Action (VLA) policies are vulnerable to localized physical perturbations, yet existing certified patch defenses target discrete labels and cannot directly certify continuous, temporally correlated actions. We introduce CertVLA, a certified defense for closed-loop VLA control under bounded patch and texture attacks. CertVLA proposes a calibrated region of behaviorally consistent actions, while deterministic covering masks ensure that at least one checked prediction is attack-free. Specifically, CertVLA normalizes action disagreement by the benign variation of each mask pair and accepts a single-mask anchor only when it remains consistent under every second mask. It then calibrates the resulting max-min-max episode score to provide finite-sample clean coverage. Conjoining query-level decisions extends the action certificate to the complete closed-loop rollout. Furthermore, we prove that against any adaptive attacker satisfying the bounded-support threat model, every rollout certified by CertVLA executes only action chunks consistent with attack-erased clean predictions. Under dual-mask rollout correctness, this consistency certificate further guarantees task success. The certificate is independent of patch content, generation method, and physical transformation. Experiments in simulation and the real world demonstrate the empirical and certified effectiveness of CertVLA against patch attacks, with additional simulation validation on texture attacks.
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All authors Hui Lu, Zhijie Peng, Yuqi Lin, Zaijia Yang, Jiaming He, Shuhan Ye, Yi Yu, Hanwei Zhu, Bingquan Shen, Alex Kot, Xudong Jiang
Computer Vision
M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo
Byeonggwon Lee, Sanggi Lee, Siwoo Lee et al. (+2 more)
Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes, particularly in occluded areas or regions with limited view overlap. To mitigate this, recent approaches integrate Depth Foundation Models (DFMs) into MVS pipelines to provide
monocular depth priors. However, existing methods typically rely on a static, one-way fusion scheme, which fails to fully exploit the complementary strengths of both modalities. We propose a novel framework that overcomes this limitation by tightly coupling a DFM with a cascade MVS pipeline through a bidirectional mutual refinement strategy. Our method leverages MVS depth to resolve the scale ambiguity in
monocular predictions, while the
monocular depth, in turn, enhances the structural completeness and fine-grained detail of the MVS estimate. Furthermore, we introduce a prior-guided cost volume refinement mechanism that effectively integrates multi-view and
monocular information via
attention-based fusion and discretized depth bins, thereby promoting local
geometric consistency. Extensive experiments demonstrate that our method outperforms state-of-the-art MVS approaches on standard
benchmarks, producing more complete and generalizable depth maps with sharp boundaries. Furthermore, although not explicitly designed for sparse-view settings, our framework generalizes remarkably well, competing favorably with even dedicated sparse-view methods while maintaining a superior accuracy-efficiency trade-off.
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All authors Byeonggwon Lee, Sanggi Lee, Siwoo Lee, Khang Truong Giang, Soohwan Song
Computer Vision
MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories
Haojin He, Hao Tan, Zichang Tan et al. (+2 more)
Modern AI
video generation models can produce
videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does not necessarily imply physical motion consistency. Existing generative models mainly optimize distribution matching in pixel or latent spaces, without explicitly enforcing real-world constraints such as inertia, continuous forces, and trajectory geometry. Our experiments show that AI-generated
videos remain visually plausible over short sequences of consecutive frames, yet fail to preserve physical motion consistency throughout a complete object action, resulting in systematic statistical discrepancies in their motion trajectories. Based on this observation, we introduce MotionPhys, a lightweight and interpretable framework that treats sparse motion trajectories as physical evidence rather than relying on appearance artifacts or generator-specific traces. By modeling the
geometric evolution of trajectories across multiple temporal scales, MotionPhys reveals subtle motion inconsistencies that are difficult to capture with conventional visual cues and transforms them into a compact representation for efficient detection. Experiments on multiple
datasets show that MotionPhys can effectively detect physical inconsistencies in generated
videos and generalizes well across different
video generators.
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All authors Haojin He, Hao Tan, Zichang Tan, Ajian Liu, Jun Wan
Computer Vision
CARD: Diagnosing Belief to Action Routing Failures in Vision Language Models
Souptik Kumar Majumdar, Fabian Kögel and Andreas Bulling
Linear probes and activation steering have uncovered that
vision-
language models (VLMs) internally represent mental states such as
agents' beliefs, knowledge, and intentions. However, it is unclear whether and how these representations are used by downstream predictions along these axes. To close this gap, we introduce Cross-Axis Routing Diagnostic (CARD), which steers activations along one axis while measuring the response of a different axis's prediction. Applied to open-weight VLMs on Relay Chain – a new cooperative grid-world
benchmark we propose – we diagnose a critical routing failure: models fail to incorporate belief representations into their next action prediction, effectively leaving valuable information about their partners unused.
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Computer Vision
DiGS-Avatar: Single-Image Animatable 3D Human Reconstruction via UV-Space Diffusion
Jiakun Li, Li Fang, Hao Zhu et al. (+4 more)
Single-image 3D human reconstruction often suffers from over-smoothed textures and
geometric inconsistencies. While
diffusion models improve generative quality, their reliance on multi-view synthesis prior to 3D reconstruction is computationally expensive and prone to view inconsistency. We propose DiGS-Avatar, which reformulates this task as an efficient,
diffusion-based UV-latent completion task, ensuring 3D consistency by design. To capture accurate spatial structure, we introduce a teacher-student framework where a multi-view teacher provides geometrically aligned pseudo-ground-truth latents to supervise a single-view
diffusion student. Treating this inferred latent as a robust structural skeleton, our method injects high-level semantic features to accurately recover fine textural details without disrupting spatial integrity. The refined representation is then decoded into 3D Gaussian primitives. Extensive experiments demonstrate that DiGS-Avatar achieves state-of-the-art or highly competitive visual fidelity and zero-shot generalization, while reconstructing a fully animatable 3D avatar in just 0.71 seconds. Code is available at https://github.com/KLMAV-CUC/DiGS-Avatar.
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All authors Jiakun Li, Li Fang, Hao Zhu, Fei Hu, Long Ye, Yuan Zhang, Jinyao Yan
Computer Vision
Vis-Poison: Poisoning Visual Knowledge in Multimodal Retrieval-Augmented Generation
Rujin Liang, Zhongpu Chen, Yuhao Lei et al. (+1 more)
While
multimodal retrieval-augmented generation (RAG) systems increasingly rely on images as external knowledge sources, the introduction of poisoned visual evidence can severely compromise
multimodal large
language model (MLLM) generation. Unlike prior attacks that rely on altering textual metadata, we introduce Vis-Poison, a novel visual knowledge poisoning attack where the poisoned image itself is the attacker-controlled payload, without manipulating captions, summaries, metadata, or other associated text. Specifically, this attack is instantiated through an automated multi-
agent method that constructs visually plausible poisoned images. To assess its impact, we evaluate Vis-Poison across two representative
multimodal RAG pipelines, four
embedding models, and six generation models. Empirically, Vis-Poison achieves an end-to-end attack success rate of 40.16% to 65.40% against 30k-entry
multimodal knowledge bases in black-box settings. Moreover, Vis-Poison remains effective against various MLLMs that can answer correctly from parametric knowledge alone, with an average success rate above 60%. Code and data are available at https://github.com/SWUFE-DB-Group/Vis-Poison.
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All authors Rujin Liang, Zhongpu Chen, Yuhao Lei, Xin Miao
Computer Vision
SPARK-SAM: Learning How to Prompt and Respond for Infrared Small Target Segmentation
Aji Mao, Zhenming Peng, Bailin Mu et al. (+1 more)
Promptable
segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target
segmentation (IRSTD) exposes a mismatch between spatial
prompts and target-domain mask responses. In a diagnostic using target-covering loose-box
prompts deterministically derived from test reference masks, the best official SAM2.1 results are only 4.69%, 1.64%, and 2.28% IoU on NUAA-SIRST, NUDT-SIRST, and IRSTD-1K. We introduce SPARK-SAM (Self-
Prompt Adaptation with Response Knowledge for SAM), which learns target-domain response knowledge and conditions the decoder through an image-conditioned joint self-
prompt state. Training combines
benchmark-mask supervision with reliability-aware response guidance. SPARK-SAM achieves 75.78%, 86.49%, and 68.34% IoU with 0.726M additional parameters, ranking first on two
benchmarks among 14 retrained SAM variants and adaptations evaluated as automatic image-to-mask methods. The staged IRSTD-1K diagnostic shows that response adaptation reaches most of the final IoU before the predicted points acquire reliable target grounding.
Prompt supervision aligns the predicted
prompt candidates with target locations, and frozen-weight interventions measure output sensitivity to the joint self-
prompt state. Matched ablations show consistent accuracy gains from response guidance and high-resolution
prompt refinement across all three
datasets. Code is available at https://github.com/Sakauma/SPARK-SAM.
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All authors Aji Mao, Zhenming Peng, Bailin Mu, Tian Pu
Computer Vision
Identity-Preserving Text-to-Video Generation via Agentic Enhancement and Semantic Repair
Jiayi Gao, Changcheng Hua, Jiaqi Tang et al. (+2 more)
Identity-preserving
video generation aims to synthesize
videos that follow natural-
language instructions while maintaining the visual identity of a given subject. Recent commercial
video generation models have achieved strong visual quality and motion realism, but they still suffer from identity drift, incomplete instruction following, and missing visual details under complex
prompts. Since these models are usually closed-source black boxes, directly improving them through parameter optimization is often infeasible. We therefore propose Agentic Enhancement and Semantic Repair (AESR), a lightweight enhancement framework for identity-preserving
video generation. To improve
prompt construction before generation and mitigate the above failures, AESR introduces a global agentic
prompt enhancement module. This module learns model-specific prompting formats from official documentation, acquires human-centered
video generation priors from human-interaction data, and accumulates test-domain identity-preserving generation experience into a reusable playbook through an agentic loop. To further repair errors in
videos generated with enhanced
prompts, AESR introduces a sample-level visual semantic repair module, which uses a VLM to locate erroneous
video segments and design repair instructions, edits selected frames into explicit visual references, and guides a
video editing model to fix local semantic or identity-related errors. We also adopt a lightweight Mixture-of-Experts selection strategy to choose reliable outputs from different generation and refinement paths. Under the official evaluation protocol of the ACM MM 2026 Identity-Preserving
Video Generation Challenge, our system MIPL\_
Video ranked first in Track 1, demonstrating the effectiveness of AESR for practical identity-preserving
video generation. The code is available at https://github.com/oceanflowlab/AESR.
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All authors Jiayi Gao, Changcheng Hua, Jiaqi Tang, Yuxin Peng, Yang Liu
Computer Vision
Generating Multi-view Adversarial Examples for Visual Geometry Grounded Transformer
Qi Song, Ziyuan Luo, Haoliang Han et al. (+1 more)
The Visual Geometry Grounded
Transformer (VGGT) enables unified feed-forward 3D reconstruction from multi-view images. However, deploying such a high-performance model may expose critical security vulnerabilities. Traditional
adversarial perturbations require costly per-scene optimization, while Universal
Adversarial Perturbations (UAPs) rely on a single static pattern and fail to effectively attack VGGT. To address these limitations, we propose MVAP-G, a multi-view
adversarial perturbation generator that produces imperceptible consistent perturbations across multiple views in a single feed-forward pass. To ensure perturbation consistency across diverse scenes, we design a cross-view
adversarial alignment mechanism to process multi-view images. Experiments demonstrate that MVAP-G significantly degrades VGGT performance without iterative optimization during inference. This work pioneers multi-view
adversarial attacks on 3D foundation models, uncovering severe vulnerabilities and underscoring the urgent need for robust 3D
vision systems. The code is available at https://github.com/qsong2001/mvap-g.
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All authors Qi Song, Ziyuan Luo, Haoliang Han, Renjie Wan
Computer Vision
VisTa3D: A Dataset and Benchmark for Thin Object Reconstruction from Vision, Tactile, and 3D Point Clouds
Shania Guo, Yeongsik Seo, Andrew Fu et al. (+7 more)
State-of-the-art 3D reconstruction models, whether from visual, range, or both, tend to underperform on thin objects. This is partially due to the small amount of space such objects occupy in RGB images and in 3D point clouds. To test the extent of their errors, we collected the first thin object
dataset comprising of synchronized RGB images, depth maps, and tactile response maps, where each frame is associated with inertial measurements, camera pose and calibration, and groundtruth depth and
segmentation maps obtained from
laser scanning of thin objects. We hypothesize that tactile data can aid in the reconstruction of thin objects as their response maps provide local shape and deformation information. Our
dataset, termed VisTa3D, comprises of 387 scenes covering 70 thin objects over 17 environments. We benchmarked current 3D reconstruction models on VisTa3D and found that, indeed, they exhibit low fidelity on thin objects. To test if tactile data can help, we introduce the first visual-range-tactile 3D reconstruction model as a baseline. Code and data: https://huggingface.co/
datasets/shaniaguo/VisTa3D.
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All authors Shania Guo, Yeongsik Seo, Andrew Fu, Mei Hao, Iris Xia, Jiwon Jenny Lee, Xinyi Mary Xie, Hyoungseob Park, Aaron Dollar, Alex Wong