Computer Vision
FedVAR: Prototype-Aligned Federated Framework for Video Anomaly Recognition
Ghani Haider, Majid Kundroo, Boyun Eom et al. (+3 more)
In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS),
Federated Learning (FL) offers a promising decentralized intelligence paradigm for
Video Anomaly Recognition (VAR). This task is vital for maintaining high-fidelity Digital Twins and ensuring safety in mission-critical environments. However, the inherent data heterogeneity across distributed edge clients leads to a fundamental challenge known as semantic misalignment, where clients learn divergent feature representations of "normal" and "abnormal" events. The problem becomes particularly pronounced in VAR, where the presence of diverse and fine-grained anomaly categories leads each client to develop distinct semantic interpretations of abnormality. Existing
federated methods primarily focus on binary anomaly detection and fail to address this misalignment, preventing effective fine-grained recognition. In this paper, we introduce FedVAR, a weakly-supervised FL framework explicitly designed for VAR. Leveraging the rich representations of
Vision-
Language Models (VLMs), FedVAR employs a prototype-based
alignment mechanism that creates a shared semantic anchor for all clients to re-center and align their visual and textual feature spaces. This process enforces a consistent representation of "normality" across the decentralized network, directly mitigating semantic misalignment and enabling robust
prompt-learning of anomaly direction vectors with minimal communication overhead. We conduct extensive experiments on challenging
benchmarks under various non-IID data partitioning schemes, unseen domains, and novel anomaly classes. The results demonstrate that FedVAR consistently outperforms state-of-the-art
federated baselines, establishing a robust framework for distributed intelligence in
video-based CPS.
Read the full abstract ▾
All authors Ghani Haider, Majid Kundroo, Boyun Eom, Dong Hwan Park, Chen Chen, Taehong Kim
Computer Vision
DAEP: Difficulty-Aware Evidence Planning for Medical Video Corpus Temporal Answer Grounding
Tianjian He, Yujie Liu, Zhiping Huang et al. (+1 more)
We describe DAEP, team BIGC's submission to NLPCC 2026 Shared Task 1 Track 3: Difficulty-Aware Temporal Answer Grounding in
Video Corpus (DA-TAGVC). The task requires retrieving the target
video from 50 candidates and localizing the answer-supporting span. DAEP ranks
videos with subtitle, visual, and procedural-context evidence, expands high-scoring anchors into temporal spans, and reranks spans for final output. Its main design is to convert the task-provided simple/complex input label into an inference-time evidence plan controlling modality weights, Top-K aggregation, boundary threshold, expansion length, and reranking strength. In the official evaluation, BIGC ranks first among ten systems with an Average score of 0.2728. Validation ablations show that visual evidence, procedural context, and difficulty-aware planning improve ranking quality, with the largest gain on complex questions.
Read the full abstract ▾
All authors Tianjian He, Yujie Liu, Zhiping Huang, Changbo Xu
Computer Vision
Multi-Agent Forensic Reasoning for Generalizable Deepfake Video Detection
Xuechao Zou, Shun Zhang, Kai Li et al. (+6 more)
The malicious use of generative artificial intelligence to create highly realistic deepfake
videos raises serious ethical concerns and poses substantial challenges to AI safety. However, existing deepfake
video benchmarks provide limited coverage of recent synthesis methods and generally lack reliable fine-grained textual annotations. Meanwhile, conventional detectors and
multimodal large
language models (MLLMs), whether operating as a single model or relying on a single analytical perspective, often fail to capture subtle forgery artifacts, limiting their generalization to emerging AI-generated methods. To address these limitations, we introduce FaceVid-Forensics-100K, a large-scale deepfake
video dataset comprising 100,000
videos and spanning 33 synthesis methods across face swapping, face reenactment, and entire-face synthesis, including recent generators such as Seedance 2.0. The
dataset provides fine-grained textual annotations of visual observations and verdict-consistent forensic explanations, automatically synthesized through a multi-model aggregation and conflict-resolution pipeline powered by advanced MLLMs. Building on this
benchmark, we propose a multi-
agent forensic
reasoning framework that employs four specialized domain-expert
agents to independently analyze forgery cues from four perspectives: texture, lighting, motion, and physics. A judge
agent then reconciles their reports to produce a final prediction together with an explanation. Extensive evaluations on out-of-domain test sets show that, despite being composed entirely of small open-source MLLMs, our framework outperforms all methods including closed-source GPT and Gemini models and ranks first across all reported metrics on this
benchmark. The project page is available at https://xavierjiezou.github.io/ARGUS/.
Read the full abstract ▾
All authors Xuechao Zou, Shun Zhang, Kai Li, Yi Zhou, Xinyu Sun, Yuhui Chen, Zhe Wu, Congyan Lang, Junliang Xing
Computer Vision
RegionDet: A Benchmark for Region Detection Beyond Object Instances
Liang Wan, Yuhan Wang, Yupeng Zhang et al. (+4 more)
Object detection is a fundamental task in computer
vision and has achieved remarkable progress on standard
benchmarks by localizing discrete and well-bounded object instances. However, many visual targets in real-world scenarios are not individual objects, but regions defined by visual states, scene context, object relations, and human activities, such as construction areas, damaged road regions, queues, group conversations, and vendor regions. Existing detection
benchmarks are mainly built around object instances, providing limited support for systematically evaluating such region targets. To address this gap, we introduce Region Detection, a task that extends conventional object detection beyond object instances, and construct RegionDet, a
benchmark for region target localization. RegionDet contains eight region categories, including Construction, Crossing, Damage, Queuing, Talking, Vendor, Waiting, and Walking, with COCO-style bounding-box annotations and evaluation protocols. We systematically evaluate representative closed-set and zero-shot/open-vocabulary detectors on RegionDet. Results show that closed-set detectors can partially learn region-level patterns under supervision, while zero-shot/open-vocabulary detectors struggle severely, revealing the strong object-centric bias of current
vision-
language detectors. Further analyses highlight key challenges in Region Detection, including weak boundary cues, strong context dependency, and insufficient relation-level region understanding. The RegionDet will be released.
Read the full abstract ▾
All authors Liang Wan, Yuhan Wang, Yupeng Zhang, Zhen Xu, Han Wang, Fangjie Fu, Sirui Zhu
Computer Vision · Cross-listed from cs.RO
Are Visual Place Recognition Models Recognizing Places or Conditions? Distractor-Augmented Evaluation and Condition Suppression
Beomsu Kim, Minwoo Jung and Giseop Kim
Long-term Visual Place Recognition (VPR) is typically evaluated by matching queries from one condition against a database from another. Crowdsourced map databases, however, may mix conditions and include images that resemble the query in condition but depict different places. In the presence of these distractors, a method may retrieve by condition similarity rather than place identity. We argue that this susceptibility arises because the discriminability of VPR methods allows them to encode information such as illumination, weather, and seasonal appearance in their descriptors. We therefore introduce Distractor-Augmented Recall (DAR) to isolate and quantify the effect of distractors, and propose condition suppression to remove condition information from VPR descriptors. Across eleven methods and six
datasets, method rankings under DAR@1 differ from those under Recall@1 (R@1), while applying INLP and LEACE as condition suppression methods generally improves DAR@1 without reducing R@1. Thus, distractor robustness is distinct from standard
retrieval performance and can be improved by suppressing condition information.
Read the full abstract ▾
Computer Vision
ECAD: Expanding Class-Agnostic Detection Beyond Thing-Centric Objectness
Liang Wan, Zixin Ren, Yupeng Zhang et al. (+2 more)
Object detection is a fundamental task in visual perception, providing structured region representations for recognition, grounding,
reasoning, and interaction. However, existing detection paradigms largely inherit a thing-centric notion of objectness, where detectors are mainly trained to localize discrete and countable object instances. Consequently, many semantically meaningful visual elements, such as sky, road, grassland, water, and sports courts, are often absorbed into the background despite their importance for scene understanding and spatial
reasoning. In this paper, we formulate Expanded Class-Agnostic Detection (ECAD), a new setting that aims to discover category-agnostic visual candidates beyond conventional thing-centric objects. To support this setting, we construct BTCO-Bench, a Beyond Thing-Centric Objectness
benchmark with category-agnostic box annotations covering both real-world and cross-domain scenarios. We further propose ECADet, a lightweight DETR-based detector built upon a frozen DINOv3 encoder, and introduce Geometry-Aware Expert Regression (GAER) and Prototype-Guided Query Modulation (PGQM) to improve localization and objectness estimation for diverse visual elements, respectively. Extensive experiments show that ECADet consistently outperforms representative class-agnostic and proposal-based detectors on BTCO-Bench, demonstrating the effectiveness of expanded objectness discovery. Code and
benchmark will be released.
Read the full abstract ▾
All authors Liang Wan, Zixin Ren, Yupeng Zhang, Yuhan Wang, Fangzhuo Gao
Computer Vision
GOPI: Generation-Oriented 3D Pose Inference for Furniture Insertion from Single-View RGB-D Indoor Scenes
Ruifeng Zhai, Renjie Liu, Guangrun Wang et al. (+1 more)
We study the problem of inserting new furniture into indoor scene images. Under masked single-view 2D image-plane conditioning, however, the physical scale of the inserted furniture relative to the scene cannot be uniquely determined, making physically grounded furniture placement underdetermined from image evidence alone. We therefore reformulate the task as a combination of 3D pose inference and geometry-guided image generation, where estimating a geometrically plausible 3D placement is essential for reliable synthesis. To this end, we propose a two-stage framework. For 3D placement, we introduce GOPI, a generation-oriented 3D pose inference framework that addresses the underdetermined nature of single-view furniture insertion through data-driven iterative inference, producing geometrically plausible object placements. For image generation, we develop a geometry-guided conditioning strategy that projects the inferred 3D pose into the image plane as a pixel-aligned constraint, enforcing consistency between the synthesized image and the underlying 3D geometry. Experimental results validate the proposed framework from both 3D pose estimation and image synthesis perspectives. For 3D placement, GOPI produces poses with stronger
geometric feasibility and better consistency with reference layouts than direct regression and vanilla baselines. For image synthesis, our method preserves
alignment with the projected 3D geometry across different furniture scales, showing stable projection-generation
alignment across the tested furniture scales.
Read the full abstract ▾
All authors Ruifeng Zhai, Renjie Liu, Guangrun Wang, Liang Lin
Computer Vision
Bend the Basics: Degradation-Aware Deformable Tokenization for All-in-One Image Restoration
Zihao He, Yunfeng Wu, Xinchao Wang et al. (+1 more)
All-in-one image restoration seeks a single model that can recover images degraded by diverse and spatially non-uniform corruptions. However, many unified
Transformers rely on fixed patch partitioning: task/degradation condition is injected only into the backbone blocks after tokenization, leaving the
embedding and reconstruction stages insensitive to local degradation variations. In contrast to previous approaches, we present Flexible Image
Transformer (FIT) that explicitly models degradation awareness across the entire pipeline, from patch sampling to pixel reconstruction. Specifically, FIT employs a lightweight Degradation Encoder to predict a global degradation vector $\mathbf{g}$ and a spatial degradation map $\mathbf{M}$ from local degradation severity, which jointly condition the patch
embedding and unembedding through adaptive deformation. Moreover, to improve robustness across degradation types, we introduce a task-
token dropout strategy that regularizes task conditioning during training. On five standard
benchmarks (BSD68, Rain100L, SOTS, GoPro, and LOLv1), FIT achieves state-of-the-art performance with 30.72 dB average PSNR on the five-degradation setting and 32.83 dB on the three-degradation setting, outperforming recent unified restoration methods by +0.5$\sim$1.1 dB. Moreover, the learned offsets provide a direct handle for visualizing degradation-aware spatial adaptation.
Read the full abstract ▾
All authors Zihao He, Yunfeng Wu, Xinchao Wang, Songhua Liu
Computer Vision · Cross-listed from cs.RO
R2S-EGO: Dual-Proxy Refinement for Sparse-Capture Real-to-Sim
Shuai Fang, Xin Deng, Yuchen Kang et al. (+2 more)
Real-to-sim (R2S) depends on scene representations that render observations along
robot ego trajectories, yet dense multi-view capture limits per-environment real-image capture-count efficiency, and sparse human capture can leave behavior-scoped
robot views under-supported. Camera-controlled synthesis can fill missing views, but its use in R2S requires behavior-admissible queries and capture-anchored structural conditioning. We present R2S-EGO, which couples a simulator-derived
robot proxy that represents the behavior-scoped executable query domain with a capture-anchored geometry proxy that supplies scene-specific structural conditions. Within this domain, fixed- budget selection targets current support deficits for which geometry support is available. The generated observations are assimilated as pseudo-observations to refine the visual asset, while real captures remain anchors. The fused geometry proxy also supplies the scene collision surface, which is refreshed between rounds. Together, these updates refine the existing simulation scene while its
robot dynamics and control stack stay fixed. Across 48 frozen Unitree G1 ego views in three Replica scenes, six-view R2S-EGO reaches 19.062 dB PSNR, compared with 14.226 dB for the strongest reported R2S baseline. Across five paired policy-training seeds, R2S-EGO achieves 82.5% +/- 6.8% real-G1 sitting success, compared with 10.0% +/- 10.5% for GaussGym.
Read the full abstract ▾
All authors Shuai Fang, Xin Deng, Yuchen Kang, Zhenjiang Li, Jie Chen
Computer Vision
AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles
Jinlei Wang, Jiahuan Long, Mingkai Sun et al. (+9 more)
Physical
adversarial attacks against person detectors have evolved from localized patches to full-body textures. However, achieving both visual naturalness and strong attack effectiveness remains challenging. Existing natural-looking methods typically optimize camouflage textures as a whole, limiting the flexibility to refine local
adversarial patterns and their spatial arrangement. To address this issue, we propose AdvTiles, a physical
adversarial camouflage framework built from learnable tiles, enabling strong attack performance while preserving a natural camouflage appearance. Specifically, we use a Straight-through (ST) Gumbel-Softmax
estimator for differentiable tile selection, enabling joint optimization of tile patterns and spatial layouts. This design provides fine-grained control over
adversarial texture generation. To improve robustness in diverse physical conditions, we further optimize the camouflage through differentiable 3D Gaussian Splatting rendering with variations in viewpoints, scales, illuminations and backgrounds. Extensive experiments across multiple detectors demonstrate that AdvTiles achieves an average ASR of 86.2%, outperforming existing state-of-the-art attack methods. We further fabricate the optimized camouflage into wearable
adversarial clothing, validating its effectiveness in real-world scenarios across diverse distances, angles and backgrounds.
Read the full abstract ▾
All authors Jinlei Wang, Jiahuan Long, Mingkai Sun, Yafei Guo, Yuanhao Huang, Ming Wang, Junqi Wu, Jiacheng Hou, Hongbo Chen, Xingxing Wei, Tingsong Jiang, Wen Yao
Computer Vision · Cross-listed from cs.RO
Is Forward Prediction Enough? Physical State Grounding for JEPA World Models
Haodong Yan, Jiaguan Zhu, Mingyuan Jia et al. (+12 more)
Learning structured and control-relevant latent representations remains a key challenge for world models. Recent JEPA-based world models learn action-conditioned predictive latent dynamics from observation sequences. However, their forward-prediction objectives do not explicitly enforce reliable identifiability of
robot-centric physical state from individual latents or state changes from latent pairs, which can limit downstream planning and policy performance. We propose PSG-JEPA, a physically grounded JEPA world model that shapes its latent space with two complementary grounding objectives beyond forward prediction: grounding individual latents in
robot proprioceptive state, and grounding latent pairs in multi-horizon joint-angle changes. Both objectives are applied only during training, leaving the inference architecture and computational cost unchanged. To comprehensively evaluate PSG-JEPA, we conduct experiments at three levels: (1) latent identifiability via probing, (2) goal-conditioned planning on frozen latents, and (3) policy learning in simulation and on a real
robot. Experiments demonstrate that our PSG-JEPA consistently outperforms state-of-the-art latent world-model baselines at all three levels.
Read the full abstract ▾
All authors Haodong Yan, Jiaguan Zhu, Mingyuan Jia, Ruiqing Yin, Junjie He, Zhide Zhong, Junfeng Li, Jinxuan Lu, Hengtao Li, Tianran Zhang, Jiayi Chen, Wenxuan Song, Wen Chen, Yuxiang Gao, Haoang Li
Computer Vision
PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model
Renye Yan, Jikang Cheng, You Wu et al. (+4 more)
While
diffusion models have made significant progress in text-to-image tasks, they still exhibit limitations when directly optimizing downstream objectives. Although Reinforcement Learning (RL) enables targeted optimization, existing methods are generally constrained by low-efficiency fine-tuning and sparse rewards. To address these challenges, we propose PAST, which provides differentiated rewards while adaptively regulating training episode length by jointly perceiving denoising progress and
prompt difficulty. Specifically, we design an intrinsic reward paradigm to compensate for sparse extrinsic rewards and guide the model to explore paths that diverge more efficiently from noise patterns. We further provide theoretical justification for intrinsic rewards. Then, PAST dynamically monitors denoising completion and semantic
alignment between image structures and
prompt semantics. When both metrics satisfy generation requirements, the system adaptively terminates training. This enables appropriate allocation of episode lengths based on
prompt difficulty and the current generation process. Finally, based on the predicted residual noise level, we establish a dual adaptive coordination mechanism. Specifically, it not only balances the extrinsic and intrinsic rewards but also balances the exploration and convergence. Experimental results demonstrate that PAST enhances computational efficiency of existing RL fine-tuning methods by up to 66.7%, while improving preference optimization quality by up to 29.5% through its dual adaptive regulation mechanism.
Read the full abstract ▾
All authors Renye Yan, Jikang Cheng, You Wu, Wei Peng, Zongwei Wang, Ling Liang, Yimao Cai
Computer Vision
UniCycleFlow: Bidirectional Unpaired Image Translation with a Shared Rectified Flow
Xianhao Zhou, Jianghao Wu, Shaoting Zhang et al. (+1 more)
Bidirectional unpaired image translation must preserve source-specific structure while learning coherent transformations in both directions without paired supervision. Existing methods typically employ two direction-specific generators or train separate one-way models. Even when linked by cycle consistency, such models constrain only the round-trip endpoint reconstruction, without requiring the two directions to obey a common local transformation rule. We propose UniCycleFlow, a rectified-flow framework that represents bidirectional translation as forward and reverse integration of a single time-conditioned velocity field. This formulation organizes both directions within the same continuous dynamics, rather than coupling otherwise separate endpoint mappings. A key challenge is that unpaired data provide no meaningful source–target coupling from which rectified-flow trajectories can be constructed. UniCycleFlow addresses this challenge by learning deterministic source-conditioned endpoints whose marginal distributions are adversarially matched to the opposite domains. The resulting paths are regularized by stop-gradient self-flow matching for intermediate velocity supervision, discrete cycle closure for forward–reverse consistency, and representation path-velocity regularization for controlling localized feature changes along the trajectory. Across ten translation directions, UniCycleFlow achieves the lowest FID on 7 of 10 tasks using a single Euler evaluation and obtains the best average FID of 55.1.
Read the full abstract ▾
All authors Xianhao Zhou, Jianghao Wu, Shaoting Zhang, Guotai Wang
Computer Vision
AnyTrack: Unifying Visual Object Tracking with Any Modalities
Hao Li, Yunzhi Zhuge, Wenning Hao et al. (+4 more)
Visual object tracking aims to continuously locate specific targets within sequential frames, evolving from single-modal methods to multi-modal ones. However, existing multi-modal trackers are typically designed for fixed modality combinations, requiring separate models for different inputs. This leads to a poor adaptability to missing or imperfect modalities, and limited generalization. To address these issues, we propose a novel unified framework called AnyTrack for object tracking with any modalities. Specifically, we design a Modality-aware Interaction Module (MIM) to facilitate dynamic interaction across diverse modalities. This module bridges modality discrepancies and aggregates temporal cues to maintain spatio-temporal consistency during cross-modal interaction. Furthermore, we introduce a Context Understanding Module (CUM) to establish spatial correspondence between visual features and target locations via global-local
prompts. This module employs target-aware context modeling to enhance foreground-background discrimination for precise localization. Finally, to support the training and evaluation under diverse modalities, we extend existing multi-modal object tracking
benchmarks by incorporating grayscale images,
language descriptions, and audio clips. Extensive experiments with both complete and missing modality settings demonstrate that our AnyTrack achieves state-of-the-art performance, validating its effectiveness and flexibility. The source code is available at https://github.com/IdolLab/AnyTrack.
Read the full abstract ▾
All authors Hao Li, Yunzhi Zhuge, Wenning Hao, Pingping Zhang, Xiaoxiong Zhang, Dong Wang, Huchuan Lu
Computer Vision · Cross-listed from cs.AI
Surg-UniWorld: A Unified Surgical World Model with Multimodal Control Experts
Rulin Zhou, Wanhao Liu, Guoheng Ma et al. (+8 more)
Controllable
surgical world models can provide a generative foundation for
surgical artificial intelligence and simulation by synthesizing realistic instrument–tissue interactions. However, existing methods lack a unified
multimodal control paradigm, while direct fusion of heterogeneous visual conditions often causes anatomical distortion, instrument appearance drift, and temporally inconsistent interactions. In this work, we propose {Surg-UniWorld}, a unified
surgical world model with
multimodal control experts. Surg-UniWorld first constructs a {Hierarchical
Surgical Anchor} from first-frame appearance and hierarchical semantic masks to preserve persistent scene identity, anatomical organization, and interaction boundaries. {Anchor-Relative Modality Experts} then interpret edge, depth, and optical-flow evidence relative to the shared anchor, capturing complementary boundary,
geometric, and motion information. A {
Multimodal Control Expert} further performs contribution-preserving stage-wise composition of the activated modality increments and generates control hints for the Wan2.2
video diffusion backbone. To support
multimodal surgical world modeling, we further construct Cholec80-SurgWAM, a
benchmark for controllable
surgical video generation. Extensive experiments demonstrate that Surg-UniWorld consistently outperforms existing controllable
video generation methods and
surgical world-model baselines in generation quality, temporal consistency, and
multimodal controllability.
Read the full abstract ▾
All authors Rulin Zhou, Wanhao Liu, Guoheng Ma, Liangjin Shao, Qiujie Song, Yidu Wang, Guankun Wang, Tong Chen, Long Bai, Luping Zhou, Hongliang Ren
Computer Vision
Yuanfu Sun, Yuanhang Ren, Kang Li et al. (+5 more)
Recent
Multimodal Large
Language Models (MLLMs) have achieved remarkable progress across diverse
vision-
language tasks, creating an urgent need for more challenging
benchmarks. Yet existing evaluations still provide limited insight into whether these models can truly reason over structured visual information. Visual Graph
Reasoning (VGR) offers a compelling testbed for this challenge, requiring models to integrate perception, structural understanding, and multi-step
reasoning over graph-based visual inputs. However, prior VGR
benchmarks often reduce the task to visual perception followed by text-based
reasoning, restrict evaluation to single-image settings, rely on answer-only metrics, and underrepresent realistic graph-centric scenarios. To bridge the gap, we introduce GraphVerse, a unified
benchmark that jointly evaluates perception, visual
reasoning, and text-based graph
reasoning in MLLMs under both single-image and paired-image settings. At its core is a suite of Graph-centric Image Editing (GIE) strategies that modify graph images while preserving their semantics, turning them into active tests of visual
reasoning. We further propose VGR-Score, a process-sensitive metric that evaluates
reasoning quality beyond final-answer accuracy. Extensive experiments reveal several key limitations of current MLLMs in VGR, while also validating the effectiveness of GIE strategies and the transferability of GraphVerse to broader
multimodal reasoning capabilities. The code is available at https://github.com/sunyuanfu/GraphVerse.
Read the full abstract ▾
All authors Yuanfu Sun, Yuanhang Ren, Kang Li, Chuanhao Ji, Jiaxi Li, Jiajin Liu, Ninghao Liu, Qiaoyu Tan
Computer Vision
Explore or Converge? Stage-Guided Per-Step Optimization for Diffusion Models
Renye Yan, Jikang Cheng, You Wu et al. (+4 more)
Diffusion models have strong generative capabilities. However, their maximum likelihood training objective only focuses on reconstructing the data distribution, making it difficult to align with specific preferences. Reinforcement learning (RL) for preference
alignment in
diffusion models is promising but limited by reward sparsity. Since a single reward cannot support optimization, existing RL methods usually backpropagate the final reward to all previous steps. However, denoising is stage-wise, with distinct semantics and controllability. Repeating the final reward across all steps creates a temporal objective mismatch, encouraging reward shortcuts that lead to reward hacking. At the same time, due to reward backfilling, each time step receives the same reward, making it impossible to distinguish between actions, thereby weakening the optimization process. To resolve this issue, we propose Stage-Guided Per-Step Optimization (SGPO) for
diffusion models, which jointly leverages signal-to-noise ratio and semantic changes to identify generation stages and adaptively assign stage-specific objectives. Early denoising is chaotic and far from the final reward, resulting in weak reward-behavior correlation. This stage should prioritize exiting the chaotic state. In the mid stage, the latent transitions to a stable structure, where the final reward better corresponds to generative behavior. Therefore, this stage optimizes the final reward while exploring diversity to avoid early convergence to a single mode. In the late stage, the latent's core structure is largely fixed, and preference optimization mainly amplifies local details, risking overfitting. Therefore, stable convergence is preferred to avoid quality degradation. Results from 16 comparative experiments validate SGPO. Our method achieves 26.7% average gains in generative quality and 36.7% higher convergence speed.
Read the full abstract ▾
All authors Renye Yan, Jikang Cheng, You Wu, Wei Peng, Zongwei Wang, Ling Liang, Yimao Cai
Computer Vision
Beyond Starry Night: Shortcut-Aware Control-State Planning for Artist-Grounded Text to Image Generation
Kuan Xing, Ye Wang, Changyi Gan et al. (+4 more)
Artist-grounded image generation requires more than appending an artist name to a
prompt. Image models often respond to artist names through canonical shortcuts, such as recurring motifs, generic palettes, or overrepresented period signatures, rather than preserving the user's intended scene. We introduce Atelier, a shortcut-aware control-state planning framework for artist-grounded image generation. Atelier translates underspecified artistic intent into an explicit control state that separates scene anchors, preserve/transform decisions, style-regime hypotheses, role-bound artist evidence, and shortcut-avoidance constraints. It grounds this state using artist-level knowledge and local patch references, compiles backend-aware generation plans, and iteratively refines candidates through global and local authenticity feedback. We further introduce ArtIntentBench, a
benchmark covering Van Gogh and Qi Baishi across artwork re-rendering, period/style-controlled generation, historically unseen subjects, shortcut
auditing, and human preference evaluation. Across open-weight and closed-source generators, Atelier improves artist-level style fidelity, preserves source structure more faithfully, and substantially reduces shortcut substitution compared with
prompt-engineered,
retrieval-augmented, and general-purpose
agent baselines. These results suggest that artist-grounded generation is bottlenecked not only by image synthesis, but by the upstream inference of explicit, evidence-grounded artistic controls.
Read the full abstract ▾
All authors Kuan Xing, Ye Wang, Changyi Gan, Yuheng Li, Thao Nguyen, Yi Chang, Yilin Wang
Computer Vision · Cross-listed from cs.AI
From Cheap Fakes to Pure Synthesis: Addressing the New Era of T2V Fake News Videos
Yifeng Luo, Yupeng Li, Liang Lan et al. (+1 more)
Recent text-to-
video (T2V) generation models enable fake news
videos to be synthesized from scratch, shifting the threat beyond cheap fakes assembled from existing footage. Such news
videos can closely match fabricated narratives, creating a modality
alignment trap for existing detectors. Existing
datasets lack pure synthesis fake news
videos. Although directly prompting T2V models with descriptions of fake news
videos can yield perfectly aligned samples, it reduces the fake news
video detection (FNVD) to unimodal shortcuts and causes semantic-visual degeneration. To counter this, we formulate T2V-FNVD as a novel ternary classification task with three labels (real, cheap fake, and pure synthesis fake) and construct the first pure synthesis fake news
video dataset (PS-FNVD). PS-FNVD includes fabricated events with aligned deception (Type 1) and true events with false visual provenance (Type 2), preventing models from exploiting unimodal shortcuts. Furthermore, we propose the
Reasoning-guided T2V-FNVD (R-T2V) framework. Trained through conditioned rationale generation and supervised fine-tuning, R-T2V integrates high-level semantic logic with low-level physical generative traces to predict the ternary veracity label. Extensive experiments across 10 prevailing baselines show that R-T2V achieves the state-of-the-art performance, outperforming the second-best baseline by 12.20 percentage points in accuracy and 8.46 percentage points in macro $F_1$.
Read the full abstract ▾
All authors Yifeng Luo, Yupeng Li, Liang Lan, Tian Wang
Computer Vision · Cross-listed from cs.RO
AtlasVLA: Persistent World-Ego State Modeling for Vision-Language-Action Models
Guiyu Zhao, Longteng Guo, Yanghong Mei et al. (+7 more)
While
Vision-
Language-Action (VLA) models have advanced
embodied AI, their fundamentally reactive paradigm severely limits performance in partially observable and long-horizon tasks. When restricted to a single wrist-mounted camera, they inevitably suffer from perception forgetting as objects exit the field of view, and temporal task-progress forgetting} during multi-step execution. To overcome these bottlenecks, we propose AtlasVLA, a novel framework that transitions from direct reactive manipulation to proactive
reasoning through a persistent world-ego state. AtlasVLA features a dual-
memory architecture: a 4D Persistent World State
Memory that lifts transient 2D observations into a globally updated, voxel-hashed spatial state to resolve visual blind spots, and an Ego-Working State
Memory that tracks historical ego state and task progress. By conditioning a
diffusion transformer (DiT) on this joint World-Ego state, AtlasVLA enables robust spatial
reasoning. Extensive evaluations across LIBERO, RLBench, and real-world
benchmarks demonstrate that AtlasVLA achieves state-of-the-art performance using solely a wrist camera. Remarkably, it decisively outperforms multi-view baselines, yielding absolute success rate improvements of 9.4% on LIBERO-Long and 17.5% in real-world long-horizon tasks.
Read the full abstract ▾
All authors Guiyu Zhao, Longteng Guo, Yanghong Mei, Zilin Zhu, Yu Zhang, Bin Cao, Mingming Yu, Xingjian He, Jie Jiang, Jing Liu
Computer Vision
WaveFreqAnchor: Wave-Structural Anchoring and Frequency Correction Diffusion for Training-Free Face Restoration
Zelin Du, Wenjie Li, Zhengxue Wang et al. (+3 more)
Diffusion-based face restoration that adjusts the sampling trajectory of pre-trained
diffusion models has achieved remarkable progress. However, existing approaches provide insufficient constraints during reverse
diffusion, causing identity-related structural drift and degraded fidelity under severe degradations. To address this, we propose WaveFreqAnchor, a training-free framework based on Wave-Structural Anchoring and Frequency Correction
Diffusion. Specifically, Anchor-Space Wave-Structural Guidance (ASWG) constrains facial structures through anisotropic wave-response consistency, while Multi-scale Wavelet-
Fourier Injection (MWFI) aligns the predicted low-frequency subband with the observation by replacing its phase, correcting inconsistencies accumulated during reverse
diffusion. For real-world scenes, we further introduce Subband High-Frequency Enhancement (SHE), which performs bounded, spatially masked refinement on the predicted high-frequency subbands to recover fine facial details under unknown compound degradations. Together, these designs effectively preserve facial identity while restoring sharp and realistic facial details. Extensive experiments show that our method consistently outperforms existing methods, achieving high-quality and high-fidelity face restoration.
Read the full abstract ▾
All authors Zelin Du, Wenjie Li, Zhengxue Wang, Juncheng Li, Cailing Wang, Guangwei Gao
Computer Vision
Suppress and Diversify: Refining Robust Pathways for Corruption Robustness
Jiangang Yang, Wenhui Shi, Xiaoran Xu et al. (+4 more)
Model robustness against natural image corruptions is essential for safety-critical applications. While existing methods primarily focus on implicit representation learning, we provide the first systematic exploration of computational pathways to explicitly characterize internal robustness. We identify a progressive decay of robust features across network layers and establish a functional dependency between the prevalence of these features and model performance. To exploit these insights, we propose Suppress and Diversify (S&D), a non-intrusive refinement approach that enhances robustness by dynamically selecting robust pathways and diversifying them through
symmetry-preserving transformations. S&D is architecture-agnostic, parameter-free, and incurs zero test-time overhead. Extensive evaluations across eight
benchmarks demonstrate that S&D consistently improves performance across multiple
vision tasks, diverse backbones, and complex real-world scenarios, highlighting its broad efficacy and scalability.
Read the full abstract ▾
All authors Jiangang Yang, Wenhui Shi, Xiaoran Xu, Wenyue Chong, Luqing Luo, Jing Xing, Jian Liu
Computer Vision · Cross-listed from cs.AI
AgentPatch: Coarse-to-Fine Weak-Task Repair for Merging Agentic Multimodal Large Language Models
Zibo Shao, Baochen Xiong, Chengdong Xu et al. (+6 more)
Agentic
multimodal large
language models (MLLMs) extend
multimodal perception and
reasoning with planning, tool use, and interaction in dynamic environments. Yet current models are specialized for particular tools or environments, complicating consolidation into a single generalist. We formulate Agentic MLLM Merging and identify two challenges: asymmetric capability preservation, whereby capabilities with different interaction complexity are retained unevenly, producing weak tasks after merging, and behavior-critical forgetting, whereby losing decisive actions can derail long-horizon execution. We propose AgentPatch, a training-free coarse-to-fine repair framework. It selects a stable merged backbone, restores diluted weak-task-specific signals through Weak-Task Unique Residual Recovery, and applies an
Agent-Guided Behavior-Critical Patch that recovers decisive behaviors under explicit capability protection. AgentPatch produces a single static checkpoint without routing or ensembles. Experiments across six agentic and
multimodal benchmarks show that AgentPatch improves diverse merged backbones, alleviates weak-task degradation, and better balances weak-task recovery with the preservation of complementary search and agentic visual processing capabilities. Code is available at https://github.com/ziboshao/AgentPatch.
Read the full abstract ▾
All authors Zibo Shao, Baochen Xiong, Chengdong Xu, Linhui Xiao, Kaichen Li, Haoran Gong, Yan Li, Yaguang Song, Xiaoshan Yang
Computer Vision
CoDAT: Collaborative Dual-Attention Transformer with Low-Cost Temporal Modeling for Efficient Edge Action Recognition
Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu et al. (+3 more)
Real-time human action recognition on Internet-of-Things (IoT) edge devices requires models that capture rich spatio-temporal cues within strict latency,
memory, and power envelopes. Current 3D CNNs,
video transformers, and shift-based ViT deliver high accuracy but come at computational costs that preclude edge IoT deployment. This paper proposes CoDAT, a Collaborative Dual-
Attention Transformer that replaces conventional multi-head
attention with a lightweight dual-branch module: Spatial Convolutional
Attention (SCA) for local aggregation and Strided Single-Head
Attention (SSHA) for global context. SSHA jointly compresses the spatial resolution and channel dimensions of the query, key, and value tensors via stride-based sparse projection, then fuses the resulting global and local features at a markedly reduced cost. To enable temporal communication across frames, a parameter-free TShift module is embedded in each block. Extensive experiments on Jetson AGX Orin and Raspberry Pi 5 demonstrate that CoDAT achieves an energy-accuracy balance in both image and action recognition. On ImageNet-1K, CoDAT-M runs 2x faster than EfficientViT384 and FastViT-S12 at comparable accuracy, and CoDAT-L matches ViT-S with 3x fewer parameters at 2x higher throughput. On Kinetics-400 and MA-52, CoDAT achieves competitive Top-1 accuracy against state-of-the-art CNN,
transformer, and hybrid baselines while running up to 2.9x faster than VSwin-T, 2x faster than ViT-Temporal-Shift variants, and 5x faster than UniFormer-B. On UCF-101, CoDAT-S384 matches TokShift and LAPS while being 6x faster and requiring up to 13x fewer FLOPs, establishing an efficiency-accuracy balance for real-time action recognition in edge IoT perception systems. Code is available at https://github.com/novendrastywn/CoDAT .
Read the full abstract ▾
All authors Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu, Wen-Kai Kuo, Jing-Ming Guo, Jun-Wei Hsieh
Computer Vision
Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando et al. (+6 more)
Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in many safety-critical systems. Detection
transformers have emerged as leading object detectors, yet their
adversarial robustness remains comparatively underexplored. Most existing attacks target the detection output rather than the
attention mechanism that makes these models distinctive. In this paper, we introduce the first attack that directly optimizes an encoder-
attention objective under an imperceptible, bounded $\ell_\infty$ perturbation. Rather than introducing an attacker-owned sink
token through a visible patch, it drives the model's own
attention toward a corrupted target. We argue that encoder
attention concentrates the model's spatial
reasoning, so corrupting it propagates through the detection pipeline more disruptively than perturbing the detection output alone. Our attack reduces DETR-R50 mAP on COCO from 42.1 to 0.97, a $\sim 4\times$ reduction in resulting mAP over the strongest existing attack under an identical perturbation budget and iteration count. We further show that this vulnerability is not specific to a particular corruption objective: across four qualitatively distinct targets, dispersion, re-ranking, permutation, and peak-suppression, detection consistently drops below 3 mAP, suggesting that the weakness arises from disrupting the
attention structure itself rather than from any single target. Finally, we demonstrate that the attack generalizes across
attention formulations, reducing DINO-Swin-L from 56.8 to 1.44 mAP against 7.3 for the strongest prior attack, establishing state-of-the-art on both dense and deformable
attention.
Read the full abstract ▾
All authors Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando, Harshala Gammulle, Basura Fernando, Sanka Rasnayake, A V Subramanyam, Sridha Sridharan, Clinton Fookes
Computer Vision
When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning
Wei Liu, Xing Deng and Haijian Shao
Language descriptions in source-free cross-domain few-shot learning (SF-CDFSL) are often selected according to zero-shot accuracy obtained with a frozen
vision–
language model. This paper asks whether that ranking remains valid after target-domain visual adaptation. Under a strictly paired protocol, we compare a generic class-name template with fixed detailed class descriptions before and after visual Low-Rank Adaptation (LoRA) on EuroSAT, CropDisease, ISIC, and ChestX. Let $\deltazero$ and $\deltalora$ denote the Detailed-minus-Base accuracy before and after adaptation, respectively. Two recurring regimes emerge. In semantic saturation, $\deltazero>0$ but $0<\deltalora\ll\deltazero$: on EuroSAT and CropDisease, initial gains of 8.13–21.54 percentage points contract to 0.69–2.96 points after LoRA. In semantic emergence, $\deltazero\leq0$ but $\deltalora>0$: on ISIC and ChestX, detailed descriptions become more useful only after the visual representation is updated. Training trajectories and sample-level decomposition show that saturation is driven mainly by Base-LoRA recovering errors already solved by detailed semantics, whereas emergence is associated with prediction turnover and newly formed Detailed-only correct decisions. Fixed-point-free shuffled-semantic controls, a second CLIP backbone, and multiple random seeds support the broad pattern while identifying ChestX 1-shot as a weak boundary case. These findings establish that zero-shot
prompt quality is an incomplete proxy for adaptation-anchor quality and motivate evaluating
language on both sides of the adaptation boundary.
Read the full abstract ▾