Computer Vision
Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weighting
Yue Yu, Haibo Chen, Shuo Chen et al. (+2 more)
Instilling creativity in text-to-image (T2I) generation presents a significant challenge, as it requires synthesized images to exhibit not only visual novelty and surprise, but also artistic value. Current T2I models, however, are largely optimized for literal text-image
alignment with their data distribution, and their noise prediction networks constrain the generation to high-probability regions, consequently generating outputs that lack authentic creativity. To address this, we propose a Self-Creative
Diffusion (SCDiff) model for meaningful T2I generations featuring two core modules: a learnable spatial weighting (LSW) module and a visual-semantic mixing loss (VSML). The LSW module designs a parametric Kaiser-Bessel window to reinforce central image features, fostering novel and surprising generation. The VSML module introduces a dual loss function: a similarity loss constrains that the new images align with its textual description, while a diversity loss maximizes its distinction from the original image, enhancing both semantic value and visual novelty. Extensive experiments demonstrate that our model substantially improves creativity, semantic
alignment, and visual coherence, offering a simple yet powerful framework for generating creative objects.
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All authors Yue Yu, Haibo Chen, Shuo Chen, Jian Yang, Jun Li
Computer Vision · Cross-listed from cs.GR
AnchorFlow: Learning Anchor Placement for Faithful and Editable SVG Reconstruction
Mengnan Jiang, Christian Franke, Michele Franco Adesso et al. (+2 more)
Raster-to-SVG reconstruction requires faithful geometry and a compact control structure for editing. A central challenge is deciding where to place anchors: raster appearance alone does not determine how a contour should be divided into Bézier segments. We present AnchorFlow, which learns anchor placement from designer-authored SVGs to reconstruct accurate curves with sparse controls. Our key idea is a sparse anchor field that jointly encodes contour geometry and reference segment junctions, including those along smooth contours. An anchor decoder predicts explicit anchor proposals from features learned under field supervision. These proposals guide boundary-constrained fitting and local refinement to recover cubic Bézier paths. On clean single-path inputs, AnchorFlow achieves 99.52% mean IoU while using 56.6% fewer anchors on average than AdaVec, with lower boundary error and closer agreement with source-SVG anchor layouts. Under boundary perturbations, it maintains high fidelity with limited anchor growth. Integrated into a component-based pipeline, the same path module also produces compact, faithful full-image reconstructions. On four local-editing tasks, our outputs require less median active time and fewer actions than AdaVec and LIVE while retaining high target-shape accuracy.
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All authors Mengnan Jiang, Christian Franke, Michele Franco Adesso, Antonio Haas, Grace Li Zhang
Computer Vision
Trust It or Not: Evidential Uncertainty for Feed-Forward 3D Reconstruction with Trust3R
Zihao Zhu, Wenyuan Zhao, Nuo Chen et al. (+2 more)
Geometric foundation models hold promise for unconstrained dense geometry prediction from uncalibrated images. However, in current feed-forward designs, their predicted confidence scores are heuristic, lack probabilistic interpretation, and often fail to indicate where and how much the predicted geometry can be trusted. To address this gap, we present Trust3R, a lightweight evidential uncertainty framework for feed-forward 3D reconstruction. Trust3R combines gated residual mean refinement with a Normal-Inverse-Wishart evidential head, yielding a closed-form multivariate Student-t distribution for per-point
geometric uncertainty. This design provides probabilistically grounded pointmap uncertainty estimates while adding moderate inference overhead. We evaluate on diverse indoor and outdoor
benchmarks and compare against MASt3R's built-in confidence map as well as common uncertainty-aware baselines spanning single-pass heteroscedastic regression and sampling-based methods such as MC dropout and deep ensembles. Experimental results show that Trust3R consistently improves risk-coverage and sparsification, and generally improves
geometric accuracy. These gains are reflected in stronger uncertainty ranking across
benchmarks, with 25% lower AURC and 41% lower AUSE on ScanNet++, providing a practical reliability signal for uncertainty-aware weighting in downstream geometry pipelines. The project page and code are available at https://trust3r-z.github.io/.
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All authors Zihao Zhu, Wenyuan Zhao, Nuo Chen, Chao Tian, Zhiwen Fan
Computer Vision
CaptchaMind: Training CAPTCHA Solvers via Reinforcement Learning with Explicit Reasoning Supervision
Pengcheng Wang, Haoxiang Liu, Yang Dai et al. (+5 more)
CAPTCHAs are widely deployed as human verification mechanisms and frequently block intelligent
agents from completing end-to-end automation in real-world web environments. Solving modern CAPTCHAs requires robust multi-step visual
reasoning and interaction capabilities, yet training-based approaches have remained absent due to the lack of large-scale training data and process-level annotations. We introduce CaptchaBench, the first CAPTCHA
benchmark designed to support large-scale training, comprising 16,000 programmatically generated samples across eight task categories with detailed region and process-level annotations. Systematic evaluation on CaptchaBench reveals that existing methods fail consistently on tasks requiring fine-grained visual detail capture and region-level comparison. We therefore present CaptchaMind, an RL-based solver trained with explicit
reasoning process supervision, achieving 82.9% average success rate across eight tasks and 71.0% on real-world instances, substantially outperforming all existing methods without closed-source APIs.
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All authors Pengcheng Wang, Haoxiang Liu, Yang Dai, Xiangxiang Zeng, Guanhua Chen, Baotian Hu, Longyue Wang, Weihua Luo
Computer Vision
Replacement Learning: Training Neural Networks with Fewer Parameters
Yuming Zhang, Peizhe Wang, Tianyang Han et al. (+5 more)
End-to-end training with full-depth backpropagation remains the dominant paradigm for optimizing deep neural networks, but its efficiency deteriorates as models grow deeper. Since every block must be executed and differentiated under a single global objective, full-depth BP introduces substantial parameter redundancy, activation-
memory cost, and training latency, especially when neighboring layers exhibit highly correlated learning patterns. Directly skipping or removing layers can reduce cost, but often weakens representation capacity or requires architecture-specific reuse designs. In this paper, we propose Replacement Learning (RepL), a training-time paradigm that reduces full-depth redundancy by replacing selected blocks rather than simply discarding them. For each removed block, RepL inserts a lightweight computing layer that synthesizes a surrogate operator from the parameters of its adjacent preceding and succeeding blocks through a learnable transformation, and applies the synthesized operator to the preceding activation. In this way, RepL preserves local contextual continuity while avoiding unnecessary full-layer computation. We instantiate RepL for CNNs and ViTs with tailored parameter-fusion blocks that handle convolutional channels, feature resolutions, and
transformer submodules. Extensive experiments on CIFAR-10, SVHN, STL-10, ImageNet, COCO, and CityScapes show that RepL reduces trainable parameters, GPU
memory usage, and training time while matching or surpassing standard end-to-end training across classification, detection, and
segmentation. Additional results on WikiText-2, transfer learning, inference throughput, checkpointing,
stochastic depth, and INT8 quantization further demonstrate its generality and compatibility.
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All authors Yuming Zhang, Peizhe Wang, Tianyang Han, Hengyu Shi, Junhao Su, Dongzhi Guan, Jiabin Liu, Jiaji Wang
Computer Vision
Boosting Text-to-Image Diffusion Models via Core Token Attention-Based Seed Selection
Yunzhe Zhang, Hongfu Liu and Pengyu Hong
Text-to-image
diffusion models can synthesize high-quality images, yet the outcome is notoriously sensitive to the random seed: different initial seeds often yield large variations in image quality and
prompt-image
alignment. We revisit this "seed effect" and show that
attention dynamics over
prompt core
tokens, the content-bearing words, measured during the first few denoising steps, strongly predict final generation quality. Building on this observation, we introduce
Attention-Based Seed Selection (ABSS), a training-free, plug-and-play method that ranks seeds for a given
prompt by leveraging cross-
attention to core
tokens during the denoising process. ABSS requires no finetuning and does not alter the initial noise; it scores and ranks all candidate seeds, keeps only the top-k for full generation, and discards the rest, without relying on a fixed accept/reject threshold. Operating purely at inference time, ABSS can serve as a lightweight pre-selection add-on for existing seed-optimization pipelines, enabling additional gains. Across three
benchmarks, extensive experiments show that ABSS enables consistent improvements in text-image
alignment and visual quality for Stable
Diffusion variants, as corroborated by human preference and
alignment metrics.
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Computer Vision
Towards Camera-Robust 3D Localization: Equation-Anchored Tool-Use for MLLMs
Xueying Jiang, Wenhao Li, Quanhao Qian et al. (+4 more)
3D localization in
Multimodal Large
Language Models (MLLMs), including 3D object detection and 3D visual grounding, is fundamentally limited by camera intrinsic ambiguity: the same image admits different 3D scenes under different cameras. Existing MLLMs either ignore camera parameters and overfit to a canonical training intrinsic, or retrieve depth and 3D cues from external tools but treat the returned values as reference cues (numerical hints that the model is free to interpret implicitly), both preventing camera information from being deterministically propagated into the prediction. We propose an equation-anchored tool-use framework that re-purposes spatial tools as formula variables. The proposed framework proactively retrieves camera intrinsics and samples multi-point metric depths, writes the pinhole back-projection equation $\hat{X} = (u_c - c_x)\bar{Z}/f_x$ explicitly in Chain-of-Thought (CoT), and substitutes tool outputs into the formula before regressing the final 9-DoF bounding box. On both 3D object detection and 3D visual grounding tasks under rescaled camera intrinsics from $0.5\times$ to $1.5\times$, our method outperforms RGB-only and tool-augmented baselines, with significant gains where the camera deviates most from the training scale. Code and data will be released.
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All authors Xueying Jiang, Wenhao Li, Quanhao Qian, Deli Zhao, Shijian Lu, Gongjie Zhang, Ran Xu
Computer Vision
Dual-Prompt CLIP with Hybrid Visual Encoders for Occluded Person Re-Identification
Zhangjian Ji, Shaotong Qiao, Kai Feng et al. (+1 more)
Occluded person re-identification focuses on matching partially visible pedestrians across multiple camera views. However, occlusions disrupt body-region cues, thereby complicating cross-view matching. Most person ReID methods built on pretrained
vision-
language models only focus on enhancing
prompt-based feature learning while ignoring the semantic information of occluders. Based on the success of CLIP-ReID, we propose a novel Dual
Prompt Learning ReID (DPL-ReID) model for occluded person ReID. It incorporates a Dual
Prompt Learning (Dual-PL) strategy, which can utilize textual cues to capture complete pedestrian semantics and keep robustness against occlusion, and a Real-World Occlusion Augmentation (RWOA) method that realistically simulates occlusion scenarios encountered in real word to enrich occluded samples. In addition, we also design a Weighted Gated Feature Fusion (WGFF) method, which in corporates LSNet to capture global information and act as a feature-gating mechanism. This mechanism can effectively guide the CLIP visual encoder toward generating more comprehensive feature representations. Extensive experiments on several
benchmark occluded ReID
datasets show that our proposed DPL-ReID achieves the state-of-the art performance. The occlusion instance library are available at https://github.com/stone-qiao/DPL-ReID.
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All authors Zhangjian Ji, Shaotong Qiao, Kai Feng, Wei Wei
Computer Vision · Cross-listed from cs.RO
SafeAlign-VLA: A Negative-Enhanced Safe Alignment Framework for Risk-Aware Autonomous Driving
Kefei Tian, Yuansheng Lian, Kai Yang et al. (+2 more)
End-to-end autonomous driving systems excel in common scenarios but struggle with safety-critical long-tail cases.
Vision-
Language-Action (VLA) models are promising due to their strong
reasoning capabilities. However, most VLA-based approaches rely on positive expert demonstrations, rarely exploiting negative samples, leading to insufficient understanding of risky behaviors and safety boundaries. To address this limitation, we propose SafeAlign-VLA, a unified negative-enhanced safe
alignment framework that incorporates negative data into supervised learning and reinforcement learning. First, we develop a counterfactual safety pairing paradigm to generate structured safety labels and counterfactual positive trajectories from risky scenarios via counterfactual
reasoning. Then, a two-stage training strategy is adopted: negative-enhanced supervised fine-tuning for failure feedback and trajectory correction, followed by anchor-based group relative policy optimization that uses positive and negative trajectories as contrastive anchors to steer sampling and penalize high-risk behaviors via group-relative advantages. Experiments on NAVSIM and DeepAccident validate the proposed framework. SafeAlign-VLA achieves 89.1 PDMS on the NAVSIM v1 testset, improving over the baseline without negative data by 1.3%. On DeepAccident, it reduces the collision rate to 3.36%, while achieving 84.2%
language accuracy and 85.8% risk prediction accuracy. These results demonstrate the effectiveness of the proposed negative-enhanced safe
alignment framework for safe and robust autonomous driving.
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All authors Kefei Tian, Yuansheng Lian, Kai Yang, Xiangdong Chen, Shen Li
Computer Vision · Cross-listed from cs.CL
Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters
Zhiyu Xu, Lean Wang, Yuanxin Liu et al. (+5 more)
Vision-
Language Models (VLMs) have demonstrated remarkable proficiency in general multi-modal understanding; yet they struggle to efficiently acquire continually evolving domain-specific skills. Conventional approaches to enhancing VLM capabilities, such as Supervised Fine-Tuning (SFT), require extensive
dataset curation and substantial computational resources. Model merging has emerged as an efficient alternative that enables the transfer of domain-specific expertise from Large
Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead. Unlike conventional merging of homogeneous LLMs, which mainly aggregates existing capabilities, cross-modal skill injection aims to induce emergent cross-modal capabilities by integrating a domain-expert LLM into a VLM. However, existing research lacks a systematic analysis of the applicability and methodology of cross-modal skill injection. In this study, we investigate cross-modal skill injection across three main aspects: scenarios, methods, and hyperparameters. For scenarios, we find that cross-modal skill injection generally performs well in instruction-following and cross-lingual settings, yet struggles with mathematical
reasoning. For methods, we find that classic approaches such as TA and DARE consistently achieve superior performance over alternative merging methods. We also provide a systematic and quantitative analysis of the hyperparameter tuning that these classic methods critically depend on.
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All authors Zhiyu Xu, Lean Wang, Yuanxin Liu, Lei Li, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun
Computer Vision
iDiff: Interpretable Difference-aware Framework for Pairwise Image Quality Assessment
Xinli Yue, JianHui Sun, Tao Shao et al. (+3 more)
Pairwise image quality assessment (IQA) in professional photography requires a model not only to identify the preferred image between two candidates, but also to provide convincing and image-grounded
reasoning. In the NTIRE 2026 RAIM challenge, this requirement is further emphasized by jointly evaluating preference prediction and rationale generation. To address this task, we propose iDiff, an Interpretable Difference-aware framework for pairwise image quality assessment. Our method adopts a dual-branch design consisting of an Answer Model and a Thinking Model. The Answer Model performs robust preference prediction by explicitly decomposing each sample into left/right global and local views, followed by content-aware specialization for person and scene images and ensemble-based aggregation across backbones. The Thinking Model focuses on rationale generation and is progressively enhanced with expert-style templates, multi-source quality features, and answer-aware supervision conditioned on the Answer Model prediction. In this way, iDiff jointly models discriminative decision making and structured explanation, improving both robustness and interpretability. Extensive experiments demonstrate the effectiveness of the proposed framework on both accuracy and
reasoning-quality metrics. Our method achieved first place in the NTIRE 2026 RAIM challenge, showing the effectiveness of integrating explicit difference modeling with structured
multimodal reasoning for pairwise IQA.
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All authors Xinli Yue, JianHui Sun, Tao Shao, Liangchao Yao, Fan Xia, Yuetang Deng
Computer Vision
Are Watermarked Images Editable? SafeMark for Watermark-Preserving Text-Guided Image Editing
Xiaodong Wu, Qi Li, Xiangman Li et al. (+3 more)
This paper investigates a fundamental yet underexplored question: can watermarked images remain editable without compromising watermark integrity? We propose SafeMark, a framework for watermark-preserving text-guided image manipulation that explicitly integrates watermark integrity into the editing process. Specifically, SafeMark adds a thresholded watermark-decoding loss directly to the
diffusion editor's training objective, fine-tuning the editor so that semantically valid edits also preserve the embedded watermark at the final output. This design admits a clean information-theoretic justification: maintaining high bit-accuracy on the edited image lower-bounds the mutual information that the editor channel preserves between watermark and edited output, the quantity that fundamentally controls watermark recoverability. SafeMark is compatible with differentiable
diffusion-based editors, and requires no architectural modification. Extensive evaluations across multiple
datasets, text-guided editing methods, and post-edit distortion settings demonstrate that SafeMark achieves high watermark bit accuracy across diverse editing settings while maintaining high-quality semantic edits, without sacrificing robustness to common post-edit distortions. These results demonstrate that semantic editability and watermark integrity are fundamentally compatible, enabling trustworthy image provenance in generative editing pipelines.
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All authors Xiaodong Wu, Qi Li, Xiangman Li, Zelin Zhang, Lingshuang Liu, Jianbing Ni
Computer Vision
Return of Frustratingly Easy Unsupervised Video Domain Adaptation
Pengfei Wei, Yiqun Sun, Zhiqiang Xu et al. (+2 more)
Unsupervised
video domain adaptation (UVDA) is a practical but under-explored problem. In this paper, we propose a frustratingly easy UVDA method, called MetaTrans. Specifically, MetaTrans adopts a concise learning objective that contains only two fundamental loss terms. Despite the simplicity of the learning objective, MetaTrans embodies an advanced UVDA idea, that is, handling the spatial and temporal divergence of cross-domain
videos separately, through a subtle model architecture design. By implementing a temporal-static subtraction module, MetaTrans effectively removes spatial and temporal divergence. Extensive empirical evaluations, particularly on various cross-domain action recognition tasks, show substantial absolute adaptation performance enhancement and significantly superior relative performance gain compared with state-of-the-art UVDA baselines.
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All authors Pengfei Wei, Yiqun Sun, Zhiqiang Xu, Yiping Ke, Lawrence B. Hsieh
Computer Vision
EventPrune: Cascaded Event-Assisted Token Pruning for Efficient First-Person Dynamic Spatial Reasoning
Pengtao Ma, Ziliang Zhou, Ciyu Ruan et al. (+7 more)
First-person dynamic spatial
reasoning requires models to track continuous motion and precise
geometric structure, but the quadratic
attention cost of
Transformer-based
Video-LLMs makes dense visual
tokens computationally expensive. Existing
token pruning paradigms predominantly rely on discrete static snapshots, failing to preserve the motion and
geometric cues essential for
reasoning. We propose Event Cascade
Pruning (ECP), to our knowledge the first training-free framework that leverages the high-frequency motion cues from event cameras as a continuous event-guided motion prior to guide
token selection. ECP combines three stages: Event-Triggered Causal Sampling to anchor motion-informative keyframes, Event-guided Motion Saliency Filtering to suppress event-inactive visual
tokens, and Event-
Attention Ranking Fusion to calibrate spatial
attention with motion-salient dynamics. With 80% visual
token reduction, ECP outperforms the full-
token baseline (37.62% vs. 36.31%) while achieving 1.89x inference speedup and 52% GFLOPs reduction. We further introduce ESR-Real, the first real-world RGB-event
benchmark for first-person spatial
reasoning, where ECP improves accuracy by 2.68 percentage points over full-
token baselines.
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All authors Pengtao Ma, Ziliang Zhou, Ciyu Ruan, Haoyang Wang, Kaiyuan Li, Zihang Gong, Wenhua Ding, Chen Gao, Jingao Xu, Xinlei Chen
Computer Vision
Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning
Jiusong Ge, Yingkang Zhan, Wenjie Zhao et al. (+13 more)
Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and aggregates them for slide-level prediction. However, such exhaustive patch-level processing is computationally expensive, severely limiting the efficiency and scalability of WSI analysis. To address this challenge, we propose PathCTM (a Pathology-oriented Continuous Thought Model) that enables
token-efficient scale-space continuous
reasoning for gigapixel WSIs. PathCTM formulates diagnostic inference as a dynamic sequential information pursuit. It progressively transitions from low-magnification global to high-magnification local inspection, and adaptively terminates inference when sufficient evidence is gathered to effectively bound decision uncertainty. Specifically, it uses conditional computation for dynamic scale switching with
attention-guided region
pruning, coupled with confidence-aware early stopping. Extensive experiments demonstrate that, compared with standard MIL-based methods, PathCTM reduces the number of required image patches by 95.95% and shortens inference time by approximately 95.62%, while maintaining AUC without degradation. Code is available at https://github.com/JSGe-AI/PathCTM.
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All authors Jiusong Ge, Yingkang Zhan, Wenjie Zhao, Di Zhang, Ke Wang, Jiashuai Liu, Chunze Yang, Chengzu Li, Jian Zhang, Yuxin Dong, Ni Zhang, Qidong Liu, Mireia Crispin-Ortuzar, Huazhu Fu, Chen Li, Zeyu Gao
Computer Vision · Cross-listed from cs.RO
Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation
Kanglong Quan, Zhebing Xia, Linfeng Jiang et al. (+4 more)
Comprehensive and efficient validation of connected and automated vehicles (CAVs) is critical prior to real-world deployment. While simulation-based testing offers scalability, existing approaches often lack seamless integration with real vehicles and field data, limiting their fidelity in capturing dynamic, real-world interactions. To bridge this gap, this paper proposes a novel real-time hybrid digital twin platform. Its core innovation lies in the tight coupling of a high-fidelity CARLA-SUMO co-simulation with a physical test site and vehicle via a low-latency Vehicle-to-Everything (V2X) communication link. A custom-developed middleware serves as the critical bridge, synchronizing a real CAV's kinematic state as a shadow vehicle in the simulation and translating virtual control commands into chassis-actuating Controller Area Network (CAN) messages for closed-loop control. Detailed implementation includes using photogrammetry for full-scale asset reconstruction and a cloud-edge collaborative architecture for scalable, multi-user operation. Experimental results demonstrate stable synchronization and effective closed-loop control with low latency, confirming the platform's practicality for multi-scenario CAV verification.
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All authors Kanglong Quan, Zhebing Xia, Linfeng Jiang, Hao Yu, Ziheng Qiao, Dapeng Dong, Dongyao Jia
Computer Vision
CutVerse: A Compositional GUI Agents Benchmark for Media Post-Production Editing
Haobo Hu, Xiangwu Guo, Zhiheng Chen et al. (+4 more)
While GUI
agents have made significant progress in web
navigation and basic operating system tasks, their capabilities in professional creative workflows remain largely underexplored. To bridge this gap, we introduce Cutverse, a
benchmark designed to systematically evaluate autonomous GUI
agents in realistic media post-production environments. We curate expert demonstrations across 7 professional applications (e.g., Premiere Pro, Photoshop), covering 186 complex, long-horizon tasks grounded in authentic editing workflows, involving dense
multimodal interfaces and tightly coupled interaction sequences. To support scalable evaluation, we develop a lightweight parser that transforms raw screen recordings and low-level interaction logs into structured, compositional GUI action trajectories with precise grounding. Extensive evaluations reveal that existing
agents achieve only 36.0% task success on realistic media editing tasks, underscoring the challenges posed by complex, long-horizon media post-production workflows in our
benchmark.While current models demonstrate promising spatial grounding,
multimodal alignment, and coordinated action execution, they remain limited in long-horizon reliability and domain-specific planning.
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All authors Haobo Hu, Xiangwu Guo, Zhiheng Chen, Difei Gao, Haotian Liu, Libiao Jin, Qi Mao
Computer Vision · Cross-listed from cs.CR
Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures
Zeyao Liu, Zhendong Zhao, Xiaojun Chen et al. (+3 more)
Existing ViT
backdoor attacks based on backbone-overwriting full-tuning are computationally expensive and inflict performance degradation. This has forced adversaries towards the Visual Parameter-Efficient Fine-Tuning (PEFT) paradigm, dominated by adapter-based (e.g., LoRA) and
prompt-based (e.g., VPT) approaches. While adapter security has seen initial study, the risks of the burgeoning
prompt-based ecosystem remain critically unexplored. We fill this critical gap, exposing how the
evolution of VPT towards dynamic and context-aware architectures can facilitate a far more dangerous and emergent threat. This vulnerability arises even though these dynamic modules unlock superior benign performance. We propose VIPER, an attack framework built on a lightweight, dynamic Visual
Prompt Generator (VPG) that demonstrates this vulnerability. Critically, this dynamic architecture enables Functional Fusion: an emergent phenomenon where malicious logic and benign task utility are tightly fused into the same sparse, high-magnitude parameter core. This fusion creates a formidable “hostage" dilemma, as
pruning the attack necessarily destroys the benign performance. Comprehensive evaluations show VIPER effectively addresses the attacker's trilemma: VIPER not only achieves state-of-the-art performance on clean data, but also maintains near-100% ASR even under 90% VPG-module
pruning (where LoRA attacks collapse), while adding only an imperceptible 0.06ms (1.16%) of inference latency. VIPER's results, driven by Functional Fusion, expose a new, paradigm-level risk in dynamic
prompt architectures.
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All authors Zeyao Liu, Zhendong Zhao, Xiaojun Chen, Xin Zhao, Yuexin Xuan, Xiaoshuang Ji
Computer Vision
Targeted Downstream-Agnostic Attack
Zhuxin Lei, Ziyuan Yang and Yi Zhang
Recently, pre-trained encoders have gained widespread use due to their strong capability in representation extraction. However, they are vulnerable to downstream-agnostic attacks (DAAs). Existing DAA methods operate under a permissive threat model, where an attack is successful if the generated downstream-agnostic
adversarial examples (DAEs) change the original prediction, without requiring a specific target. In this paper, we propose a Targeted DAA (TDAA) method under a stricter threat model requiring the attack to be both targeted and downstream-agnostic. Since the downstream task is unknown and encoders do not directly produce predictions, achieving a targeted attack is particularly challenging. To address this, we introduce a novel component termed the 'threat image', pre-selected by the attacker as the target. Specifically, a generator is designed to produce example-specific
adversarial perturbations that compel the victim encoder to output identical features for both the DAEs and the threat image. Unlike previous DAA methods that generate a single shared perturbation for all samples, which often fails due to image diversity, our method adopts an example-specific paradigm. This generates tailored perturbations for each image to ensure a high attack success rate and invisibility. By leveraging the threat image as a feature-level anchor, our method builds a task-agnostic bridge to reveal the vulnerabilities of the victim encoder. Extensive experiments on 10 self-supervised methods across 3
benchmark datasets demonstrate the effectiveness of our approach and reveal the pronounced vulnerability of pre-trained encoders. The code will be made publicly available after the review period.
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Computer Vision · Cross-listed from cs.LG
CEPO: RLVR Self-Distillation using Contrastive Evidence Policy Optimization
Ahmed Heakl, Abdelrahman M. Shaker, Youssef Mohamed et al. (+4 more)
When a model produces a correct solution under reinforcement learning with verifiable rewards (RLVR), every
token receives the same reward signal regardless of whether it was a decisive
reasoning step or a grammatical filler. A natural fix is to condition the model on the correct answer as a teacher, identifying
tokens it would have generated differently had it known the answer. Prior work shows this either corrupts training by leaking the answer into the gradient, or produces a weak signal that cannot distinguish decisive steps from filler, since both look equally surprising relative to the model's baseline. We propose Contrastive Evidence Policy Optimization (CEPO), which asks a sharper question at every
token: not just "does the correct answer favor this
token?" but "does the correct answer favor it while the wrong answer disfavors it?" A
token satisfying both is a genuine
reasoning step; one satisfying neither is filler. The wrong-answer teacher is constructed from rejected rollouts already in the training batch, incurring no additional sampling cost. We prove CEPO inherits all structural safety guarantees of the prior state of the art while strictly sharpening credit at decisive
tokens, with the improvement vanishing exactly at filler positions. Empirically, CEPO achieves 43.43% and 60.56% average accuracy across five
multimodal mathematical
reasoning benchmarks at 2B and 4B scale, respectively, versus 41.17% and 57.43% for GRPO under identical training budgets. Distribution-matching self-
distillation methods (OPSD, SDPO) fall below the untrained baseline, empirically confirming the information leakage our theory predicts. Our code is available at https://github.com/ahmedheakl/CEPO.
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All authors Ahmed Heakl, Abdelrahman M. Shaker, Youssef Mohamed, Rania Elbadry, Omar Fetouh, Fahad Shahbaz Khan, Salman Khan
Computer Vision
KappaPlace: Learning Hyperspherical Uncertainty for Visual Place Recognition via Prototype-Anchored Supervision
Maya Yanko and Yoli Shavit
Visual Place Recognition (VPR) is critical for autonomous
navigation, yet state-of-the-art methods lack well-calibrated uncertainty estimation. Standard pipelines cannot reliably signal when a query is ambiguous or a match is likely incorrect, posing risks in safety-critical robotics. We propose KappaPlace, a principled framework for learning uncertainty-aware VPR representations. Our core contribution is a Prototype-Anchored supervision strategy that leverages latent class representatives as targets for a probabilistic objective. By modeling image descriptors as von Mises-Fisher (vMF) variables, we learn a lightweight module to predict the concentration parameter as a direct proxy for aleatoric uncertainty. While existing VPR uncertainty methods are typically restricted to a query-centric view, we derive a novel match-level formulation to quantify the reliability of specific query-reference pairs. Across five diverse
benchmarks, KappaPlace reduces Expected Calibration Error (ECE@K) by up to 50% compared to existing methods while maintaining or improving
retrieval recall. We provide both a joint-training variant and a post-training extension for frozen backbones. Our results demonstrate that KappaPlace provides a robust, stable, and well-calibrated signal that enables reliable decision-making within the VPR pipeline. Our code is available at: https://github.com/mayayank95/UncertaintyAwareVPR
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Computer Vision
Zilin Wang and Stella X. Yu
Segmentation has become easy when the concept is known, requiring
retrieval of a learned visual grounding from text. It remains hard for open ad-hoc concepts, where the grounding may not exist as one learned mask and must often be constructed from image evidence through parts, relations, exclusions, and collections. We propose a
Vision-guided Ad-hoc
Segmentation Agent (VASA), the first
vision harnessing
agent for open ad-hoc
segmentation. VASA is training-free and couples a VLM
agent, a
segmentation foundation model, and a visual harness that maintains a working mask to make visual progress persistent, inspectable, and editable. Rather than revising text
prompts alone, it plans visual operations, invokes
segmentation tools, inspects results, edits the mask, and recovers from errors. We construct PARS, a new
benchmark that turns part-level labels into open ad-hoc concepts through long-form definition queries. We show that VASA is consistently effective across six VLMs with varying capabilities. Using Qwen3-VL 32B Thinking as the VLM, VASA outperforms various baselines on PARS, surpassing SAM3
Agent by 13.5%-25.3%. On RefCOCOm, VASA improves over SAM3
Agent by 4.8%-8.8% and over other agentic baselines by more. VASA also remains competitive with SAM3
Agent on ReasonSeg for common, named concepts. These results validate VASA's agentic visual construction for open ad-hoc
segmentation.
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Computer Vision
Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models
Wooseok Jeon, Seungho Park, Seunghyun Shin et al. (+3 more)
Image-to-
video models often generate
videos that remain overly static, compared to text-to-
video models. While prior approaches mitigate this issue by weakening or modifying the image-conditioning signal, they often require additional training or sacrifice fidelity to the reference image. In this work, we identify reference-frame dominance as a key mechanism behind motion suppression. We observe that non-reference frames in I2V models allocate excessive self-
attention to reference-frame key
tokens, causing reference information to be over-propagated across time and suppressing inter-frame dynamics. Based on this finding, we propose DyMoS (Dynamic Motion Slider), a training-free and model-agnostic method that rebalances the
attention pathway from generated frames to the reference frame during initial denoising steps. DyMoS leaves both the input image and model weights unchanged and introduces a single scalar parameter for continuous control over motion strength. Experiments across multiple state-of-the-art I2V backbones demonstrate that DyMoS consistently improves motion dynamics while maintaining visual quality and fidelity to the reference image.
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All authors Wooseok Jeon, Seungho Park, Seunghyun Shin, Sangeyl Lee, Hyeonho Jeong, Hae-Gon Jeon
Computer Vision
Neuron Incidence Redistribution for Fairness in Medical Image Classification
Abin Shoby, Lyle John Palmer and Nikhil Cherian Kurian
Deep learning models for medical image classification are susceptible to subgroup performance disparities across demographic attributes such as age, gender, and race. We identify a latent representational mechanism underlying these disparities: in transfer-learned models, the dominant penultimate-layer activation channel under positive predictions is co-activated by both disease-positive samples and privileged demographic groups (male, older patients), producing over-diagnosis; conversely, the dominant channel under negative predictions is co-activated by disadvantaged groups (female, younger patients), producing systematic under-diagnosis. To address this, we propose
Neuron Incidence Redistribution (NIR), a lightweight regularization method that penalizes the variance of predicted-probability-weighted mean activations across penultimate-layer
neurons, requiring no demographic labels at training time. On HAM10000, TPR disparity drops from 10.81% to 0.93% across age groups and from 12.04% to 0.74% across gender, with a marginal AUC improvement of 0.51 points. On Harvard OCT-RNFL, NIR reduces FPR disparity for race (from 15.68% to 10.66%) and age (from 12.69% to 1.80%), demonstrating that distributing latent disease evidence across the full penultimate layer is a principled and effective strategy for improving demographic
fairness in medical AI.
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Computer Vision
LMM-Track4D: Eliciting 4D Dynamic Reasoning in LMMs via Trajectory-Grounded Dialogue
Chaoyue Li, Yongxue Xu, Jie Feng et al. (+1 more)
Recent large
multimodal models (LMMs) have become increasingly capable on image and
video understanding, yet still struggle to sustain 4D continuous spatiotemporal dynamic
reasoning. To study this capability gap, we formulate trajectory-grounded multi-turn spatiotemporal dialogue, a new task in which a model must answer spatiotemporal queries while returning structured 3D target trajectories over an entire short clip or a specified segment of a longer clip, and introduce Track4D-Bench, a
benchmark with 526 clip-level dialogue samples spanning 23.5k frames and 7.5k object annotations, for training and evaluation. Building on this task, we propose LMM-Track4D, which combines RTGE (Ray–Time Geometry Encoding), a dedicated streaming state
token TRK for long-horizon dynamic propagation, and an Object-Slot Kinematic, Residual-Anchor (OSK-RA) decoder for stable 4-step 3D state estimation under occlusion and viewpoint variation. Experiments on Track4D-Bench show consistent improvements over strong baselines, suggesting that explicit dynamic state modeling is a useful design principle for eliciting 4D dynamic
reasoning in LMMs. Our code and
dataset will be publicly available at https://github.com/mikubaka88/LMM-Track4D.
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All authors Chaoyue Li, Yongxue Xu, Jie Feng, Jiayu Ding
Computer Vision
MatPhys: Learning Material-Aware Physics Parameters for Deformable Object Simulation from Videos
Yang Yang, Yiyan Wang, Zheming Liu et al. (+1 more)
Reconstructing simulation-ready deformable objects is important for
vision, graphics, and robotics. Existing physics-driven methods can recover physical digital twins from
videos, but they suffer from two fundamental limitations: they typically assume a homogeneous material across the whole object, and their scene-specific inverse optimization, combined with the inherent ambiguity of
monocular observation, yields inconsistent parameters for the same material across different scenes or interactions. We propose MatPhys, a material-aware feed-forward framework that predicts spring-mass parameters from a single-view
video, addressing these two issues with two coupled designs. To relax the homogeneous material assumption, we use DINO features to decompose the object into semantically meaningful parts and to query a part-level material prior, assigning each part its own physical behavior. To enforce cross-scene consistency, we introduce a learned material codebook of shared material
embeddings as the bridge between appearance and physics, and further use the part-level prior as a reference distribution that constrains the decoder so that the same material yields consistent parameters across scenes and interactions. Together, these designs turn an under-constrained
monocular problem into feed-forward inference grounded on shared, reusable material concepts. Experiments show that our method matches per-scene optimization baselines in reconstruction and future prediction, while achieving stronger generalization to unseen interactions and objects with more consistent physical parameters.
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All authors Yang Yang, Yiyan Wang, Zheming Liu, Naoya Iwamoto
Computer Vision
Concept-Guided Noisy Negative Suppression for Zero-Shot Classification and Grounding of Chest X-Ray Findings
Chenyu Lian, Hong-Yu Zhou, Chun-Ka Wong et al. (+1 more)
Vision-
language alignment using chest X-rays and radiology reports has emerged as an advanced paradigm for zero-shot classification and grounding of chest X-ray findings. However, standard contrastive learning typically treats radiographs and reports from different patients simply as negative pairs. This assumption introduces noisy negatives, as different patients frequently exhibit similar findings. Such noisy negatives cause semantic ambiguity and degrade performance in zero-shot understanding tasks. To address this challenge, we propose CoNNS, a concept-guided noisy-negative suppression framework. To support the negative suppression mechanism, unlike previous methods that use raw reports or templatized texts, we construct a hierarchical concept ontology using large
language models. The ontology structures 41 key clinical concepts by explicitly modeling presence, attributes (location and characteristics), and texts (evidential segment and presence statement). Leveraging this ontology, we implement a cross-patient pair relabeling strategy comprising three steps: (1) Fine-Grained Breakdown to categorize pairs based on finding presence; (2) Noisy Negative Filtering to resolve semantic conflicts by removing false negatives; and (3) Hard Negative Mining to identify subtle attribute discrepancies using a lightweight
language model. Finally, we propose a Concept-Aware NCE loss to align visual features with text while suppressing the identified noisy negatives. Extensive experiments across multi-granularity zero-shot grounding tasks and five zero-shot classification
datasets validate that CoNNS outperforms existing state-of-the-art models. The code is available at https://github.com/DopamineLcy/conns.
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All authors Chenyu Lian, Hong-Yu Zhou, Chun-Ka Wong, Jing Qin
Computer Vision
Multi-Scale Generative Modeling with Heat Dissipation Flow Matching
Jun Ma, Hanquan Zhang, Yanjun Qin et al. (+2 more)
Diffusion models are widely used in image generation, with most relying on noise-based corruption and denoising. A distinct branch instead uses blur as the main corruption, preserving better color budgets and multi-scale detail by providing multi-scale priors. However, blur-based models remain in SDE-based frameworks and are not integrated into ODE-based frameworks, such as Flow Matching (FM). Meanwhile, in the blur-based formulation, the classical inverse heat-dissipation (IHD) process faces an ill-posed challenge. Moreover, under the data-
manifold assumption, regressing blurred images from high-dimensional noise (or velocity) space is also difficult. We propose Heat Dissipation Flow Matching (HDFM), which introduces a continuous blurred (heat-dissipation) process into FM to inject multi-scale priors. HDFM aligns an interpolated heat-dissipation path to address ill-posedness and adopts $x$-prediction to mitigate high-dimensional regression difficulty. Toy experiments and ablation studies show that HDFM consistently benefits from both blur and $x$-prediction. The performance of HDFM outperforms most baseline methods on all
datasets.
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All authors Jun Ma, Hanquan Zhang, Yanjun Qin, Haoyuan Guan, Ke Zhang
Computer Vision
Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection
Parnian Ghapandar Kashani, Shiqi Chen and Aydogan Ozcan
The rapid proliferation of AI-generated visual media has created an urgent need for efficient, trustworthy deepfake detection systems. However, existing deep learning-based detection methods rely on computationally intensive and energy-demanding inference
algorithms, limiting their scalability. Here, we present a hybrid digital-analog deepfake
video detection framework that combines a lightweight digital front-end with a spatially multiplexed optical decoding back-end for massively parallel analog inference through a programmable spatial light modulator. By simultaneously processing 15 or more
video streams within a single optical propagation pass, the system enables high-throughput and accurate
video-level authenticity prediction at reduced computational cost compared with purely digital methods. We validated this hybrid deepfake
video processor using different
datasets spanning classical face-swapping, real-world deepfake recordings, and fully AI-generated
videos. Using a spatially multiplexed experimental set-up operating in the visible spectrum, we achieved average deepfake detection accuracy, sensitivity and specificity of 97.79%, 99.86% and 95.72%, respectively, on the Celeb-DF
video dataset with 15
videos tested in parallel in a single optical pass per inference. The multiplexed optical decoder also demonstrates resilience against various types of
video degradation, noise, compression, experimental misalignments and black-box
adversarial attacks. Our results show that integrating optical computation into AI inference enables simultaneous gains in throughput, energy efficiency, and
adversarial robustness - three properties that are difficult to achieve together in purely digital systems.
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Computer Vision
MAM-CLIP: Vision-Language Pretraining on Mammography Atlases for BI-RADS Classification
Halil Ibrahim Gulluk and Olivier Gevaert
Deep learning methods have demonstrated promising results in predicting BI-RADS scores from mammography images. However, the interpretation of these images can vary, leading to discrepancies even among radiologists. Given the inherent complexity of mammograms, training classification models solely on image labels often yields limited performance. To address this challenge, we curated 2313 mammogram images and their corresponding captions from two mammography atlases. Our proposed approach employs a multi-modal model that uses a pretrained PubMedBERT as the
language component. By training this model on image-text pairs with contrastive learning, we enable the
vision encoder to absorb the rich information contained in the captions, thereby improving its understanding of mammography findings. We then fine-tune the
vision encoder on two
datasets for BI-RADS prediction, achieving superior performance compared with models trained without this pretraining, particularly when labeled samples are scarce. The improvement in the 3-class average F1 score ranges from +1% to +14%: a +1% increase with 40K training samples, and a +14% increase with 1K samples. Furthermore, our experiments reveal that 2K image-text pairs from mammography atlases can be more informative than 2K labeled samples for label prediction, with an average margin of +1.1% when more than 10K training samples are available. Overall, our work provides a
vision-
language model for mammography and highlights the value of textual information from mammography atlases. In addition, we publicly release preprocessed mammography images of the TEKNOFEST
dataset. The training code, pre-trained model weights, data extraction scripts, and the released
dataset are publicly available at: https://github.com/igulluk/MAM-CLIP
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