Computer Vision
Explanation Stability of Test-Time Adaptation in Computational Pathology: A Large-Scale Benchmark
R. G. Bahumanya, Harshith V. M., Shreyank N. Gowda et al. (+1 more)
Test-time adaptation (TTA) has become a practical way to adapt deployed models to unlabeled target data, a setting that is especially relevant in computational pathology where staining, scanner, and cohort shifts are routine. While most TTA methods are evaluated by their effect on accuracy, clinical use also depends on whether the model's explanations remain reliable after adaptation. In this paper, we take a closer look at this largely unmeasured effect. We study explanation stability under TTA across two histopathology
benchmarks, Camelyon17 and NCT CRC-HE, using five architectures ranging from convolutional networks to
vision transformers and a pathology foundation model, seventeen TTA methods, and four attribution families. Across 2,958 adaptation runs, we observe a clear and systematic pattern: TTA methods differ sharply in how much they move model explanations, with frozen-backbone methods leaving attributions almost unchanged and continual methods such as CoTTA and RoTTA causing the largest drift. This effect is not uniform. Convolutional networks are substantially more sensitive than
transformer and foundation-model backbones, and explanation drift increases with adaptation strength while remaining largely insensitive to batch size. Surprisingly, explanation stability is only weakly coupled to adaptation quality. Some methods preserve explanations almost perfectly while degrading calibration or accuracy, producing silent failures that would be missed by accuracy-only or explanation-only evaluation. These findings show that explanation stability is a distinct reliability axis for TTA in computational pathology. We release the metric, protocol, and full
benchmark to support future work on adaptation methods that are not only accurate, but also stable and clinically auditable. Code: https://github.com/bahumanyarg11/tta-explanation-stability-pipeline
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All authors R. G. Bahumanya, Harshith V. M., Shreyank N. Gowda, Anala M. R
Computer Vision
KnifeHunter: Structured Local Representation Learning for Fine-Grained Knife Image Retrieval in Law Enforcement
Syed Sameed Husain, Eng-Jon Ong, Stephen Simpson et al. (+3 more)
Knife-enabled violence presents a major public safety challenge, and law enforcement agencies require scalable tools for catalogue-level knife identification, intelligence analysis, and source attribution. Manual visual comparison is specialist, time-consuming, and difficult to scale under operational imaging conditions. We introduce KnifeHunter, an end-to-end forensic knife image
retrieval system developed with UK law enforcement. The work contributes the KnifeHunter
dataset, comprising 25,843 images across 543 knife classes from police evidence, retail catalogues, and border-force seizures, with structured metadata, Medium/Hard evaluation protocols, and large-scale distractor evaluation. We further propose CoRe-Net, a compact single-descriptor
retrieval architecture that combines global context with spatially localised discriminative evidence. CoRe-Net introduces Structured Complementary Representation Learning (SCRL) to organise local evidence into complementary prototype-based representations, and Bi-Directional Reciprocal Fusion (BDRF) to integrate global and local evidence through residual projection and gated local-to-global injection. Using an EVA02-Base backbone and cosine-similarity
retrieval, CoRe-Net achieves 88.0% mAP and 86.7% mP@10 on the Medium protocol, and 85.1% mAP and 83.8% mP@10 under distractor conditions. KnifeHunter was deployed by UK police forces during Operation Sceptre deployments from 2023 to 2025, achieving 99.2% mP@1 on field queries. These results demonstrate a practical and effective multimedia
retrieval framework for fine-grained forensic knife matching in operational law-enforcement settings.
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All authors Syed Sameed Husain, Eng-Jon Ong, Stephen Simpson, Trevor Hamshere, Matt Turner, Miroslaw Bober
Computer Vision
YOLO-PEFT: Parameter-Efficient Fine-Tuning on YOLO Family
Xu Lin, WenJie Nie, Jinlong Peng et al. (+4 more)
Generic parameter-efficient fine-tuning (PEFT) methods transferred from
language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specific components impose placement constraints absent from regular
Transformer stacks. We propose YOLO-PEFT, a structure-aware framework that formulates adapter placement as an auditable constraint-planning problem. Given a detector graph, a PEFT request, and a resource budget, YOLO-PEFT assigns operator and semantic roles, evaluates explicit operator-validity, detector-semantic, graph-interface, and deployment predicates, records a reason code for each excluded module, and either emits a budgeted target-module plan or returns Refuse before training. Under the official VOC07+12 trainval-to-VOC07 test protocol, planner-selected RS-LoRA reaches 0.7138 and 0.7307 mAP50-95 on YOLO11s and YOLO12s, respectively, compared with 0.6428 and 0.6662 for Full-SFT. On RT-DETR-L, all seven evaluated LoRA-family configurations cross the predefined catastrophic threshold, supporting a calibrated Refuse-to-Full-SFT decision within the evaluated coverage. A controlled YOLO11 audit further shows that LoRA reduces peak training
memory by 43.9 percent, although training takes 1.72 times longer. Within the evaluated detector families, placement policies, and calibration coverage, YOLO-PEFT replaces manual target-module trial and error with explicit, inspectable planning while preserving verified train-save-merge-export paths; refusal on unseen detector architectures remains an open validation problem. Project Page: github.com/Tencent/YOLO-Master
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All authors Xu Lin, WenJie Nie, Jinlong Peng, Weifu Fu, YueXiao Ma, Xiawu Zheng, Yong Liu
Computer Vision · Cross-listed from cs.RO
C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video
Jie Ren, Zhehao Jiang, Yinhong Yang et al. (+9 more)
High-quality demonstrations for dexterous
robot manipulation are costly and difficult to collect, whereas
monocular human
videos provide a scalable source of diverse manipulation behaviors. However, transferring such demonstrations to dexterous
robots remains challenging:
monocular hand-object interaction (HOI) reconstruction often produces temporally unstable contacts and physically implausible interactions, while conventional retargeting methods struggle to preserve task-relevant contacts and local interaction geometry across different hand embodiments. We present C2Dex, a
video-to-dexterous-manipulation framework built around a shared interaction representation: stable object-side contacts recovered by aggregating noisy frame-wise observations in the canonical object space. These stable contacts serve a dual role: as trajectory-level constraints that guide reconstruction toward temporally coherent and physically plausible human HOI trajectories, and as explicit transfer targets for the dexterous hand, where
Laplacian interaction optimization preserves the local hand-object geometry across embodiments and residual reinforcement learning refines the trajectory in simulation. Experiments on DexYCB and TACO show that C2Dex achieves end-to-end trajectory success rates of 57.78% and 26.67%, respectively, substantially outperforming the strongest baselines (17.78% and 10.00%) under identical evaluation criteria. Real-
robot replay experiments further demonstrate physical feasibility across diverse contact-rich manipulation tasks. Project page: https://k-jie.github.io/C2Dex/
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All authors Jie Ren, Zhehao Jiang, Yinhong Yang, Haorui Jia, Han Jiang, Ben Li, Yao Yao, Cheng Lin, Qiu Shen, Zhenshan Bing, Xiao-Xiao Long, Xun Cao
Computer Vision · Cross-listed from cs.SE
CAS2UML: A Handwritten Sketch-to-PlantUML Dataset for Class and Activity Diagrams
Simon Scholz and Mersedeh Sadeghi
Automated UML generation from sketches and images is gaining renewed
attention with the rise of large
language models and
multimodal AI. However, reproducible evaluation remains difficult due to the lack of public
datasets with executable groundtruth models. We present CAS2UML, a public
dataset of 557 handdrawn UML diagrams, including 271 class diagrams and 286 activity diagrams, each paired with manually validated PlantUML code. We also provide a PlantUML-based validation tool and reusable scripts for checking the syntactic correctness and renderability of generated UML artifacts, enabling reproducible benchmarking of sketch-to-UML approaches. The
dataset, validation tool, processing scripts, documentation, and demonstration
video are publicly available at:
Dataset: https://huggingface.co/
datasets/Seym0n /cas2uml_hand-drawn_to_plantuml_
dataset; Tool and Scripts: https://github.com/Seym0n/handwritten-uml-
dataset;
Video: https://www.youtube.com/watch?v=KQrYeGgT3hs.
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Computer Vision · Cross-listed from eess.IV
IceHorizon: A Dataset for Horizon Detection in Ice-Covered Maritime Environments and Comparative Evaluation of Detection Methods
Alisa Pesotskaia and Emin Zerman
Horizon detection in images of ice-covered waters is a challenging problem for maritime
navigation due to low contrast between water and sky, cluttered ice structures, and varying illumination conditions. This paper presents a comparative evaluation of six horizon detection
algorithms, including four classical computer
vision methods and two hybrid approaches combining deep learning with classical line detection. A new bespoke IceHorizon
dataset consisting of 30 ship-based and 8 drone-based
videos is used to evaluate detection accuracy, horizon coverage, and computational performance. The results show that hybrid methods achieve the highest accuracy and most reliable horizon estimates. In contrast, purely classical methods exhibit reduced robustness, particularly in visually ambiguous scenes. Performance on ship-based imagery was consistently higher than on drone-based imagery, indicating a strong dependency on acquisition characteristics. The created
dataset and codes used in this study are made publicly available to support further research on this topic. The code is available at https://github.com/allythe/HorizonDetection. The
dataset is available at https://doi.org/10.5281/zenodo.20411867
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Computer Vision
Understand Before Detect: Vision–Language Learning for Omni-Domain Infrared Small Target Detection
Haoyang Yuan, Boyang Li, Yingqian Wang et al. (+7 more)
Omni-domain infrared small target (IRST) detection is crucial for infrared surveillance, yet remains challenging due to heterogeneous imaging domains and inconsistent target characteristics. Previous deep learning-based methods have been developed for visual-only paradigms and achieved promising performance on domain-specific tasks. However, existing methods follow the task-specific supervised learning paradigm. This paradigm simplifies the full-scene infrared observations to sparse target supervision, discarding the semantics that remain invariant across heterogeneous domains. Consequently, detection performance suffers substantially under domain shifts. To handle this issue, we introduce “understand before detect”, a paradigm that formulates omni-domain IRST detection as an understanding-driven process, where holistic infrared target understanding precedes precise detection. Building on this paradigm, we propose JinSight, which first develops holistic IRST understanding through
language supervision and then transfers the learned cross-domain representations to precise small-target detection. By grounding infrared representations in
language semantics, JinSight enables a single model to generalize across heterogeneous infrared domains. We then introduce Latent Semantic Interaction (LSI), which exchanges
language-aligned global semantics with fine-grained spatial features in a compact low-rank space. To address the lack of
multimodal omni-domain IRST
benchmarks, we build OmniIRST-VL, the first large-scale, highly diverse
vision–
language dataset for omni-domain IRST detection. It comprises over 39k annotations across six complementary instruction tasks covering both scene-level understanding and target-centric
reasoning.
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All authors Haoyang Yuan, Boyang Li, Yingqian Wang, Yimian Dai, Nuo Chen, Xinfei Huang, Shuqi Yi, Zaiping Lin, Weidong Sheng, Wei An
Computer Vision
Stable Curves, Unstable Items: Item-Level Scaling Heterogeneity in Video LLMs
Wenzhang Sun, Chunfeng Wang, Xiangchen Yin et al. (+3 more)
Aggregate scaling curves suggest that
Video LLMs improve smoothly or saturate as visual budgets grow. We show that this view can conceal large, opposing changes at the item level. We represent each frozen model–item pair by its response trajectory under controlled visual budgets and derive matched-grid measures of configuration complementarity, harmful transitions, and text overwrite. Across five open
Video LLMs from three architecture families, four multiple-choice
benchmark splits, open-ended QA and summarization, and fixed-history dialogue generation, no single budget serves all items. On the four-model matched MCQA grid, item-level oracle headroom spans $8.8$–$18.9$ accuracy points and $12.5$–$25.5\%$ of items are correct at a lower budget but wrong at a higher one. Task-appropriate continuous metrics show the same complementarity beyond multiple choice:
Token-F1 oracle gaps are $2.7$–$3.7$ score points on MLVU generation and $3.8$–$4.8$ points on AVSD current-turn generation, even when mean quality improves with budget. The effect persists across frame count, spatial resolution, sampling policy, temporal–spatial allocation, and independently executed raw-
video and cached pipelines, with per-item rates and membership tracking protocol choices. A controlled sampling intervention recovers $29.0\%$ of terminal regressions, and a structured frame audit identifies several recurring evidence pathways. We release per-item trajectories, protocol provenance, derived annotations, and reproducible analysis code as an
auditing artifact. A confidence cascade matches fixed-$128f$ accuracy while reducing average shared frame cost by $31.7\%$, illustrating one operational use of the response
matrix.
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All authors Wenzhang Sun, Chunfeng Wang, Xiangchen Yin, Yujia Chen, Hao Li, Kun Zhan
Computer Vision
Scenix: Sparse-View 3D Scene Reconstruction via Executable Scene Programs
Kai Li, Lutao Jiang, Zhenyang Li et al. (+10 more)
Synthesizing a structured and editable 3D indoor scene from a few uncalibrated RGB views requires more than generating high-quality individual assets: a system must infer the room structure, associate objects across incomplete observations, and recover a globally consistent spatial configuration. Previous methods mainly focus on 3D scene generation with text input or require continuous visual inputs with additional priors, \ e.g., human-annotated masks or accurate 3D layouts, which makes these methods labor demanding and hard to apply in general cases. We present Scenix, a sparse-view 3D scene reconstruction framework via executable scene programs, a structured representation that can be directly instantiated into editable 3D scenes. Given sparse views, Scenix predicts executable scene programs through perception-grounded asset instantiation and closed-loop spatial refinement. % We present \method, a framework that predicts an executable scene representation from sparse views and realizes it through perception-grounded asset instantiation and closed-loop spatial refinement. To support this task, we construct \dataset, a
dataset of approximately 110,000 synthetic and real indoor scenes with multiview imagery, room structures, object-centric descriptions, and metric spatial annotations. We further introduce observation-consistent supervision that aligns each target scene with the visual evidence available in its input views. Experiments on held-out XScene scenes, real indoor images, and out-of-distribution SpatialGen cases evaluate structured scene prediction, object grounding, and spatial refinement.
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All authors Kai Li, Lutao Jiang, Zhenyang Li, Jiayu Dong, Jierui Zhang, Yingda Yin, Runze Zhang, Kai Yan, Xiaoyang Huang, Keyang Luo, Xin Wang, Xiangyu Zhao, Weikai Chen
Computer Vision · Cross-listed from cs.CL
Does More Retrieved Evidence Help Visual Retrieval-Augmented Generation with Diffusion Language Models?
Jiankun Wang, Yisen Gao, Ziwei Zhang et al. (+3 more)
Visual
retrieval-augmented generation (RAG) commonly expands the retrieved evidence set to improve answer-page coverage, implicitly assuming that all available evidence should be passed to the generator. We show that this assumption does not hold for
diffusion language models (DLMs): retrieving more pages increases answer-page recall, whereas unconditionally passing all retrieved pages to the generator often reduces answer accuracy, primarily because of semantic conflict. A latent-source analysis explains this mismatch through source-coherence loss in parallel denoising, where position-wise proposals can combine incompatible visual sources into unsupported answers. We further find that such interference is already visible in the first-step answer-block distribution, making it possible to assess evidence before decoding. To preserve
retrieval coverage while limiting harmful visual exposure, we propose the
Entropy-Based Candidate Filter (ECF), a training-free evidence-admission framework. To reduce irrelevant content within individual candidates, ECF constructs multi-granularity evidence units; to identify beneficial additional evidence, it uses blank-controlled block confidence and
retrieval rank to determine whether and which candidate should enter the final context. Across three
multimodal DLMs and five visual QA
benchmarks, ECF improves answer accuracy by 2.62 percentage points on average over the strongest fixed top-$k$ input and, with LLaDA2.0-Uni, by 2.37 percentage points on average over the best competing training-free result for each
dataset. These results show that broader
retrieval benefits visual DLM-RAG through selective evidence admission rather than unconditional evidence expansion. Code is publicly available at https://github.com/wjkuser/ECF.
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All authors Jiankun Wang, Yisen Gao, Ziwei Zhang, Xingcheng Fu, Jiaxin Bai, Chen Gao
Computer Vision
HRDiT: Training-Free High-Resolution Image Generation with Off-the-Shelf Diffusion Transformer Models
Yu Xue, Haoxuan Qu, Zhuoling Li et al. (+5 more)
Training-free text-to-high-resolution image generation has recently attracted growing research
attention. However, existing studies on this task primarily focus on adapting off-the-shelf U-Net-based
diffusion models to high resolutions, with limited progress on adapting off-the-shelf
Diffusion Transformer (DiT) models despite their strong text-to-image generation capabilities at limited resolutions. In this work, we find two key challenges particularly hindering the application of off-the-shelf DiT models for high-resolution image synthesis in a training-free manner, namely, spatial disorder and long generation time. To address these challenges, we propose a novel method tailored to adapt off-the-shelf DiT models for high-resolution image synthesis. Extensive experiments show the efficacy of our method. Our code is available at: https://github.com/zylwithxy/HRDiT.
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All authors Yu Xue, Haoxuan Qu, Zhuoling Li, Hongbin Xu, Jianxiong Yin, Simon See, Hossein Rahmani, Jun Liu
Computer Vision
Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration
Jiaqi Zhang, Zheng Pang, Rongrong Gao et al. (+2 more)
Diffusion models have demonstrated remarkable effectiveness in image restoration tasks. However, when guiding image reconstruction, existing
Diffusion Model-based Image Restoration (DMIR) methods typically rely on fixed data constraints and uniform step sizes, thereby overlooking the dynamic nature of the generative process. Such rigid designs render the models vulnerable to spatially non-uniform degradations, thus resulting in structural distortions and loss of fine details. Meanwhile, uniform step sizes introduce computational redundancy, whereas naïve step reduction strategies tend to accumulate approximation errors. To address these limitations, we propose a Local Epistemic Uncertainty Guided Active Sampling framework (LEADer). In the spatial domain, LEADer leverages pixel-wise uncertainty to dynamically modulate the prior strength within the null space, which effectively balances detail preservation and artifact suppression. In the temporal domain, it quantifies sampling stability via the uncertainty trace to enable adaptive trajectory
pruning, thereby accelerating convergence. Theoretical proofs demonstrate that our framework achieves strict data consistency, while the trajectory
pruning strategy admits a deterministic error bound, thereby guaranteeing stable convergence under skip sampling. Notably, our plug-and-play method can be seamlessly integrated into various DMIR baselines. Extensive experiments show that LEADer improves the performance of multiple state-of-the-art DMIR methods, while significantly reducing sampling time with negligible
memory overhead. Code is available at https://www.jiaqizhang-sengoku.com/Code/LEADer.
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All authors Jiaqi Zhang, Zheng Pang, Rongrong Gao, Qiyuan Zhang, Yang Yang
Computer Vision
When One Modality Is Not Enough: Multimodal Sex and Life-Stage Classification of Red Deer from Aerial RGB-Thermal Video
Hugo Markoff, Christoph Praschl, Ivan Ludoški et al. (+3 more)
Aerial drone surveys increasingly support wildlife population estimation, yet a useful census is more than a count: population dynamics are defined by species composition, sex ratios and age structure, that is, by which species are present and how a herd splits into adult males, adult females and juveniles. We use red deer ($\textit{Cervus elaphus}$) as a test case, because managers act on these dynamics and because the visible cue defining adult males, the antlers, is seasonally variable. Surveys are flown nadir, high enough not to disturb the animals, so each deer occupies only a small, low-resolution patch. The two recording modalities fail in opposite conditions: in color a deer under canopy blends into the ground, while in thermal it becomes a bright blob that loses fine detail. Rather than trust either modality alone, we fuse them at every stage using self-supervised DINOv3 features. Our pipeline tracks animals in both modalities, treats an animal as confirmed only when the two cameras agree, keeps only the clear, non-occluded frames, and assigns species and sex by a vote across them; life stage is read separately from geo-referenced body size, since at survey resolution a juvenile often only differs from an adult female in size. Across four flights spanning the antler season the fused pipeline correctly classifies 25 of the 26 detected individuals (7 of 8 adult males, all 16 adult females and 2 juveniles), against 20 of 26 for either sensor alone.
Multimodal species classification reaches 96.0%, while for sex classification fusing the two sensors matters most: the combined RGB+thermal model is the most robust across environments and seasons. Automating the demographic classification turns a drone flight from a count into a repeatable reading of herd structure, so the sex ratios and age structure that managers already act on can be gathered as often as a survey can be flown.
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All authors Hugo Markoff, Christoph Praschl, Ivan Ludoški, Sara Beery, Michael Ørsted, David C. Schedl
Computer Vision
Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding?
Yun Li, Biao Yang, Peixi Wu et al. (+5 more)
Embeddings have emerged as a standard representational interface linking foundation models with downstream systems. Most
embedding benchmarks assess representations through discriminative tasks or
geometric criteria centered on separability in
embedding space. However, strong performance on such evaluations does not establish whether content compressed into an
embedding remains accessible to a downstream generator. To address this gap, we introduce the Generative
Embedding Benchmark (GEB), in which a decoder answers questions using only a frozen
embedding and question text, without access to the original image or intermediate visual features. Answer quality under this readout measures generative information: the answer-relevant content recoverable from an
embedding. GEB includes a curated visual-question-answering
dataset with a 1,800-item development split and a held-out 900-item test split covering natural images, scene text, and visual documents. Using a common decoder and training recipe, we evaluate seven public
embedding models in visual-only and
vision-
language joint modes. On the test set, visual-only scores range from 28.25 to 33.21; with image-question joint encoding, all five VLM-based
embedding models score higher, and the best reaches 65.56. Matched
embeddings also outperform text-only inputs, zero
embeddings, and shuffled
embeddings. Natural-image information is much easier to recover than scene text or visual-document information, while a Qwen3-VL-2B reference with access to the original image reaches 84.30. Together, these results show that generative readout exposes information bottlenecks that separability-based evaluation does not capture.
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All authors Yun Li, Biao Yang, Peixi Wu, Yunhao Zhou, Mingzhou Jiang, Wei Yuan, Fan Yang, Wenwu Ou
Computer Vision
Summarize First, Download Later: Onboard VLMs for Bandwidth-Efficient Earth Observation
Junghwan Park, Sangcheol Sim, Woojin Cho et al. (+1 more)
Modern Earth observation (EO) satellites carry increasingly advanced sensors that produce vast volumes of high-resolution, multispectral data, yet downlink capacity remains a critical bottleneck – often causing significant latency or the loss of valuable observations within limited contact windows. We propose a "Summarize First, Download Later" paradigm that exploits recent advances in onboard edge computing and
Vision-
Language Models (VLMs). Rather than indiscriminately downlinking raw imagery, the system follows a three-phase interaction protocol: the satellite first transmits concise natural
language summaries generated by a quantized onboard VLM; ground operators then issue targeted Visual Question Answering (VQA) queries to verify scene relevance (e.g., wildfires or maritime anomalies); and full-resolution images are downloaded only when critical information is confirmed. This transforms the downlink from passive bulk transfer into an active, semantics-aware dialogue. We implement and evaluate the system on a resource-constrained NVIDIA Jetson platform, and experiments on diverse remote sensing scenes show that the proposed strategy substantially reduces bandwidth consumption while accelerating time-to-insight for time-sensitive missions.
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All authors Junghwan Park, Sangcheol Sim, Woojin Cho, Darongsae Kwon
Computer Vision · Cross-listed from cs.CR
Casting the Net! Revisiting MasterFace Impersonation Attacks
Seunghun Paik, Sunpill Kim, Chanwoo Hwang et al. (+1 more)
Impersonation is a fundamental security threat in face recognition systems (FRSs). While the security of FRSs has been challenged by various attack vectors, under realistic
adversarial capabilities, e.g., a limited number of decision-only authentication trials and no internal system knowledge, most attack techniques become infeasible. As a result, impersonation by zero-effort impostors, characterized by false match rate (FMR), is commonly regarded as a standalone baseline. A few years ago, impersonation attacks based on MasterFaces emerged as a notable security threat that could break the barrier of the FMR-based baseline under such realistic constraints. However, they were believed not to yield impersonation above the standard FMR in modern FRSs, as discussed by multiple follow-up studies. In this paper, we demonstrate that even legitimate access to public commercial APIs allows an adversary to amplify impersonation rates through MasterFaces, resulting in a non-trivial impersonation attack beyond FMR on downstream applications built on top of these APIs. We observe that several real-world FRS deployments are implemented using commercial APIs, and that the backend service provider is publicly disclosed or trivially inferable. As a result, the adversary can purchase these pay-as-you-go API services without requiring any additional privilege over the target FRS. From this observation, we formalize the MasterFaces attack as a maximum coverage problem over the biometric representation space, which we call a NET, and show that the adversary can construct an API-tailored NET by leveraging the
geometric structure of the representation space. We demonstrate that our attack amplifies the impersonation rates of several open-source and commercial API-based FRSs by up to 9.5$\times$ within at most 30 authentication trials, compared to those expected from the standard FMR.
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All authors Seunghun Paik, Sunpill Kim, Chanwoo Hwang, Jae Hong Seo
Computer Vision
Dual-Space Modality Consistency Learning for Universal Cross-Modal Re-Identification
Yujian Zhao, Yukang Zhao, Hankun Liu et al. (+4 more)
Cross-modal Re-Identification (ReID) aims to retrieve the same identity across heterogeneous imaging modalities and has been widely studied in visible-infrared person ReID and cross-modal ship ReID. Existing methods have achieved promising performance by learning modality consistency in the spatial
embedding space, yet often overlook frequency-domain modality discrepancy, particularly in high-frequency representations that are both highly discriminative and modality-sensitive. In addition, most approaches are tailored to specific modality settings, limiting their applicability across diverse cross-modal scenarios. To address these challenges, we propose a Dual-Space Modality Consistency Learning (DSMCL) framework for universal cross-modal ReID. Specifically, DSMCL jointly models spatial feature distribution consistency and frequency-domain discriminative consistency. A Spatial Modality Consistency Learning (SMCL) branch performs Gaussian-based feature
alignment, while a Frequency-aware Discriminative Consistency Learning (FDCL) strategy regularizes high-frequency representations through identity-aware cross-modal contrastive learning. By jointly capturing modality-specific characteristics and modality-shared identity cues, DSMCL learns robust representations and establishes a unified framework capable of accommodating diverse heterogeneous modality settings. Moreover, DSMCL is a plug-and-play framework that can be readily integrated into existing cross-modal ReID architectures. Extensive experiments on SYSU-MM01, RegDB, LLCM, HOSS-ReID, and CMShipReID across seventeen evaluation protocols show that DSMCL consistently improves multiple representative baselines.
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All authors Yujian Zhao, Yukang Zhao, Hankun Liu, Haoxuan Xu, Bo Li, Hanzi Wan, Guanglin Niu
Computer Vision · Cross-listed from cs.LG
ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlink
Woojin Cho, Junghwan Park, Sangcheol Sim et al. (+3 more)
The acquisition of multispectral imagery via small satellites (e.g., CubeSats) presents significant data downlink challenges due to high data volumes and restricted communication windows. While onboard image compression is critical to address this bottleneck, traditional methods often struggle to adapt to the nonlinear statistics of multi-band, multi-resolution data. To overcome these limitations, we propose ELMZip, a novel framework based on Extreme Learning Machines (ELM) and domain decomposition strategies for efficient, resolution-free onboard neural representation. ELMZip formulates the fitting process as a convex least-squares problem using random-feature single-layer networks, thereby eliminating the need for computationally expensive backpropagation. By adopting an asymmetric transmission protocol that sends only the compact output weights, the proposed method significantly reduces the downlink payload. Unlike previous neural representation approaches that rely on iterative optimization and require transmitting full network parameters, ELMZip achieves significant compression efficiency while maintaining high reconstruction fidelity. This capability enables immediate image reconstruction for analysis, allowing resource-constrained platforms to maximize data return and advancing real-time AI-powered Earth observation.
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All authors Woojin Cho, Junghwan Park, Sangcheol Sim, Steve Andreas Immanuel, Junhyuk Heo, Darongsae Kwon
Computer Vision
Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal
Wanshu Fan, Yunzhe Zhang, Yue Shen et al. (+5 more)
Adverse weather causes diverse and complex image degradations, severely compromising the reliability of computer
vision systems. Existing all-in-one restoration models attempt to address multiple degradation types within a unified framework, but often lack explicit spatial and semantic modeling of degradation characteristics, limiting their adaptability to diverse weather conditions. To address this limitation, we propose a Degradation-Aware Cross-Modal
Prompt Compensation Network (DCMPC-Net) that leverages cross-modal degradation cues from a pretrained
vision-
language model to condition restoration features within a unified backbone. Specifically, our DCMPC-Net mainly consists of the Cross-Modal
Prompt Generator (CMPG),
Prompt-Guided
Attention Alignment Module (PGAAM), and Dual Feature Compensation Module (DFCM). The CMPG integrates textual
embeddings with visual features to produce degradation-aware
prompts that encode degradation-related semantic and contextual cues. These
prompts are injected into the decoder via a PGAAM, which adaptively aligns semantic information with degraded regions to facilitate context-aware restoration. To further enhance structural fidelity, DFCM is introduced that disentangles degradation artifacts from scene structures, thereby improving the reconstruction of fine textures and detailed content. By integrating cross-modal semantic guidance with spatial
alignment and structural enhancement, DCMPC-Net achieves robust and perceptually consistent restoration across diverse weather conditions. Extensive experiments show that DCMPC-Net outperforms state-of-the-art methods in both task-specific and unified settings, achieving superior accuracy and visual fidelity.
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All authors Wanshu Fan, Yunzhe Zhang, Yue Shen, Liyan Wang, Jing Qin, Kin-Man Lam, Cong Wang, Jinshan Pan
Computer Vision
Debias in Text, Believe Your Eyes: Text-Anchored Cross-Modal Transfer for Visual Counter-Commonsense Reasoning
Chen Ling, Hanqian Li, Dongnan Liu et al. (+9 more)
The visual
reasoning ability of
multimodal large
language models (MLLMs) is crucial for downstream applications, particularly counter-commonsense
reasoning, which requires models to reason beyond common assumptions. Recent studies mainly improve visual counter-commonsense
reasoning by enhancing visual inputs, following the assumption that failures originate from insufficient visual grounding. However, our empirical analysis reveals that the bottleneck is not visual perception. MLLMs already capture the relevant visual evidence, and the correct answer exists in their decoding space. Instead, the shared
language decoder resolves prior–evidence conflicts by favoring dominant
language priors, especially for low-frequency factual scenarios. Motivated by this, we first propose a text-anchored data construction pipeline, whose core component, Fact-Frequency
Distillation (FFD), estimates the prior strength of commonsense facts and distills verified counter-commonsense scenarios into a high-quality text corpus. Building upon this corpus, we introduce TACT, a text-anchored post-training framework that debiases the shared
language decoder without requiring any visual training data. TACT routes evidence-following and prior-driven
reasoning trajectories into different optimization stages, enabling the decoder to resolve prior–evidence conflicts. Across counter-commonsense visual
benchmarks, TACT substantially improves visual
reasoning while preserving general capabilities, demonstrating effective text-to-
vision cross-modal transfer.
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All authors Chen Ling, Hanqian Li, Dongnan Liu, Keyu Qian, Jungang Li, Xinglong liu, Shiyi Wang, Xin Dong, Pengcheng Zhu, Wei Zhou, Linjian Mo, Nai Ding
Computer Vision · Cross-listed from cs.LG
Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning
Moloud Damandeh and Meead Saberi
Visual perception of walkability varies substantially across individuals, reflecting differences in personal characteristics, experiences, and preferences. Existing studies, however, often reduce these diverse judgements to aggregated scores, implicitly assuming uniform perception, and commonly rely on vehicle-mounted street-view imagery that does not reflect the pedestrian's visual experience. This paper introduces a
dataset of 29,870 walkability ratings from 1,196 respondents, linking sidewalk-view imagery across urban, suburban, and regional Australian environments with individual rater attributes, and proposes the first user-conditioned
multimodal deep learning framework for walkability perception, fusing visual features with respondent-level representations. A viewpoint-comparison study shows that sidewalk-view images receive significantly higher walkability ratings than matched street-view images, indicating that imagery source is a substantive design decision in perception surveys. The user-conditioned model improves rank agreement with observed ratings by 65% over an image-only baseline (quadratic weighted kappa 0.47 vs. 0.29), demonstrating that who is evaluating an environment carries predictive indication beyond image content alone. These findings support moving from aggregated, observer-independent walkability scores toward models that represent diverse users, enabling more inclusive assessment of pedestrian environments.
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Computer Vision
AVCap: Reinforcing Audio-Video Joint Caption with Detail-Aware Reward
Mingyang Wu, Kaituo Feng, Bohao Li et al. (+3 more)
Detailed audio-
video joint captioning is essential for
multimodal video understanding and generation. However, prior works are constrained by three main limitations: (1) the scarcity of high-quality public
datasets with fine-grained audio-visual joint captions; (2) reinforcement-learning methods that rely on coarse reward signals; and (3) the lack of a
benchmark and metric for evaluating detailed audiovisual captions at the atomic level. To address these challenges, we propose: (1) AVCap-100K, a high-quality
dataset of 100K temporally aligned, detail-rich audio-
video captions; (2) AVCap, a model optimized via Detail-Aware GRPO (Da-GRPO) that achieves state-of-the-art performance among open-source models and matches or surpasses proprietary models on several evaluations; and (3) AVCap-Bench and AVCap-Score, a specialized
benchmark and metric for evaluating atomic-level details in audiovisual captions. Our code, models, and
datasets are available at https://huggingface.co/collections/Apryle/avcap.
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All authors Mingyang Wu, Kaituo Feng, Bohao Li, Kaixiong Gong, Zihao Yin, Xiangyu Yue
Computer Vision
MaskFlow: Precise, Consistent and Seamless Regional Image Editing
Rui Xu, Yang Yong, Shunzi Yang et al. (+2 more)
Regional image editing has attracted considerable
attention for its spatial controllability. Although instruction-based and mask-based editing methods can achieve strong semantic
alignment, reliable regional control remains challenging, where an edit must be accurately localized and naturally integrated with the preserved context. We propose MaskFlow, a training framework for precise localization, consistent background preservation, and seamless boundary transitions. MaskFlow incorporates the mask into the probability path and flow-matching objective, coordinating generation within the editable region with source preservation outside it. The proposed Soft-Poisson De-seaming module further refines the predicted vector field during both training and sampling to improve the smooth integration of the edited foreground with the preserved background. We also introduce MaskEdit-
Benchmark for general scene and infographics editing, where
prompts describe the desired edits without localization cues, leaving masks to specify the target regions. Experiments on natural scenes and infographic images demonstrate consistent improvements over competing methods in both quantitative and qualitative evaluations. Project page: https://reychiaro.github.io/MaskFlow
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All authors Rui Xu, Yang Yong, Shunzi Yang, Ruihao Gong, Chengtao Lv
Computer Vision
Vernata: Self-Supervised Learning of LiDAR Point Representations
Oliver Lemke, Alexander Liniger, Abel Gawel et al. (+1 more)
LiDAR serves as a primary sensing modality for
robots operating in outdoor environments. However, the performance of deep learning models in this domain is severely limited by the scarcity of labeled data, a direct result of the high cost of 3D annotation. Self-supervised learning addresses this scarcity by learning general-purpose features from unlabeled data. In this work, we present a multi-modal, multi-teacher
distillation framework for self-supervised learning on outdoor
LiDAR point clouds. Building upon the Sonata architecture, we introduce Vernata, consisting of three extensions: sparse view augmentation to improve robustness against varying point densities, a
memory bank mechanism to stabilize resource-constrained training, and cross-modal
distillation utilizing dense, high-resolution 2D image features to enable fine-grained semantic guidance. We evaluate our method on the GrandTour, TartanGround, and Waymo
datasets, as well as data collected from our own robotic platforms. Our experiments demonstrate a significant performance improvement over Sonata baselines, yielding mIoU scores of 54.7 on TartanGround (+5.9 points, +12.1%) and 57.1 on Waymo (+7.3 points, +14.7%). Finally, we show that the self-supervised approach maintains strong performance even in reduced-modality settings (lacking color or normals), achieving competitive mIoU scores of 49.4 and 50.2 on the respective
datasets.
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All authors Oliver Lemke, Alexander Liniger, Abel Gawel, Marco Hutter
Computer Vision
RibAssist 3D: Biplanar Rib-Fracture Detection, Addressing, and Selective 3D Localization from CT-Derived Projections
Kabila Haile Soboka
Rib fractures are common and time-consuming to localize on computed
tomography (CT). We ask whether fractures detected independently in two orthogonal CT-derived projections (anteroposterior and lateral) can be paired across views and triangulated into reliable 3D points at a controlled rate of false outputs, and we answer it with a staged diagnostic study. The projection geometry is exact, and given correct correspondence, localization is accurate (median 4.0 mm, 88% within 10 mm, 93.6% rib-exact). On a sealed 55-case cohort, a large share of fractures is in principle recoverable (61.1% dual-view availability, and a correct pair present in the candidate graph for 58.4% of fractures), yet the binding limitation is neither geometry nor localization but confidence-limited cross-view correspondence. A controlled detector-by-correspondence factorial attributes the operational gain to lateral-detector quality rather than the tested matching methods; retraining the lateral detector produces the first nonzero controlled-budget reconstructions. Under a deliberately conservative commitment policy, a pre-specified sealed pass promotes 15 of 601 fractures to correct 3D localizations at 0.436 false points per case (2.50% end-to-end commitment yield), and committed points are accurate (median 1.49 mm, 93% rib-exact). The low yield is a consequence of confidence-gated abstention, not of geometry or detection: the study establishes a reproducible framework for selective 3D localization and identifies cross-view correspondence as the dominant operational bottleneck.
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Computer Vision
MuST-VAD: Mutual Structured Learning for Video Anomaly Detection
Satoshi Hashimoto, Hitoshi Nishimura and Mori Kurokawa
In this paper, we propose MuST-VAD, a mutual structured learning framework for weakly supervised
video anomaly detection (VAD) in which an anomaly detector and a large
vision-
language model (LVLM) exchange their acquired knowledge. Detectors in weakly supervised VAD learn anomaly scores from features extracted by a fixed, task-agnostic backbone. These fixed features bound the achievable detection accuracy. Recent methods therefore transfer LVLM semantics into the detector as richer features. However, this transfer is one-way: what the detector learns about the target
videos never returns to the LVLM. MuST-VAD extends the one-way transfer into a bidirectional learning loop. In this loop, the latest detector predictions supervise the LVLM adaptation, and the adapted LVLM returns updated representations that retrain the detector; the two models alternate these updates over small
video groups. Both models train on detector-selected key clips, while confidence weighting and annotation-anchored question answering keep the exchanged supervision reliable. On UCF-Crime, our mutual learning improves the one-pass transfer baseline from 88.15% to 88.63% AUROC and from 37.25% to 42.46% average precision (AP), outperforming the state-of-the-art method in AP by 4.13 points.
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Computer Vision
Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models
Minseok Kang, Hyunwoo Kim, Chanyoung Kim et al. (+3 more)
Vision-
language models (VLMs) have achieved remarkable generalization across diverse
multimodal tasks through large-scale pre-training, yet their rapidly increasing computational and
memory requirements pose significant challenges for deployment in constrained environments. Existing
pruning strategies often depend on task-specific criteria or LLM-oriented importance measures, making them unsuitable for task-agnostic
pruning, where no task-specific samples are available at
pruning time and the pruned model remains broadly applicable. We introduce a retraining-free VLM
pruning framework called PORTA that derives a task- and modality-agnostic importance formulation based on activation variation, estimated from generic calibration data, which reliably captures feature-level representation utility across modalities. PORTA further incorporates an adaptive sparsity allocation mechanism that assigns layer-wise
pruning ratios based on output feature variability, avoiding the limitations of uniform sparsity and reducing performance degradation at high compression levels. Extensive experiments across VLM architectures, such as CLIP, BLIP, and Qwen2-VL, demonstrate that PORTA achieves competitive downstream performance under high sparsity without requiring any retraining, supporting efficient VLM compression. Code is available at https://github.com/cau-hai-lab/PORTA.git.
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All authors Minseok Kang, Hyunwoo Kim, Chanyoung Kim, Minwoo Kim, Jaekoo Lee, Dahuin Jung
Computer Vision · Cross-listed from cs.AI
From Points to Edges: Edge-Conditioned Spectral Operators for Physics-Sensitive PDE Learning
Zhentao Tan, Ruijie Quan and Yi Yang
Neural operators have become a central tool for solving partial differential equations (PDEs), with spectral operators offering efficient global mixing across spatial locations. However, many PDEs contain physics-sensitive local structures that are critical to the underlying physical behavior. For example, in Darcy flow, local material interfaces are often reflected by sharp changes in the permeability field and can strongly influence the solution. Existing spectral operators primarily adapt modal mixing based on center-point representations, making them insufficiently responsive to such localized structural variations. We propose the Edge-Conditioned Spectral Operator (ESO), a novel spectral operator framework that modulates global spectral mixing using local edge-wise variations. By incorporating the Pairwise-Variation Modal Mixer (PVMM) to inject local edge information into spectral mode selection, ESO preserves the global approximation capability of spectral neural operators while enabling the learned kernel to adapt to physics-sensitive local structures. Furthermore, we introduce a task-adaptive Physics-Aware Reweighting (PAR) that emphasizes physically important regions, identified by taskspecific physical quantities. Across nine PDE
benchmarks, ESO consistently achieves state-of-the-art performance. Visual and region-wise analyses further demonstrate that ESO reduces solution errors near coefficient jumps, high-gradient flow structures, and other physically sensitive regions. The code is available at https://github.com/Tanpig-X/ESO.
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Computer Vision
HazeSpikeMamba: Coupling Spiking-Inspired and State-Space Features for Self-Supervised Real-World Dehazing
Haoran Liu, Huibin Li, Mingzhe Liu et al. (+2 more)
Dehazing networks are commonly trained on synthetic hazy-clear pairs, but their performance often drops on real photographs. Synthetic haze generated using the atmospheric scattering model does not fully capture the variability of real haze, and paired real hazy-clear images are scarce. In this work, we propose HazeSpikeMamba, a compact dehazing framework that combines a spiking-inspired local path and an attentive state-space global path in a multi-scale U-Net. The local path uses TPCNNSpike, a new spike-emission scheme inspired by the neighborhood coupling of Pulse-Coupled Neural Network (PCNN). Unlike grouped directional scanning, TPCNNSpike updates all
neurons in parallel using the previous firing states of their Gaussian-weighted neighborhoods. The global path adapts the Attentive State-Space Module of MambaIRv2, retaining semantic prompting and sequence reordering while removing the window self-
attention branch. Its state-space processing models long-range dependencies with complexity linear in sequence length. For target-domain adaptation, a frozen degradation network, pretrained on paired NH-HAZE data, re-synthesizes haze from the dehazed prediction. The reconstruction error updates only the final restoration layers of HazeSpikeMamba without haze-free labels during adaptation. A shared checkpoint is adapted once on each complete unlabeled target set, making the evaluation
dataset-level and transductive rather than zero-shot or per-image optimization. The forward network contains 2.02M active parameters and requires 13.27G nominal MACs (measured with thop at 256x256 input). This adaptation consistently improves BRISQUE and NIMA on RTTS, URHI, and HSTS. On RTTS, BRISQUE decreases from 30.13 to 27.72 and NIMA increases from 4.13 to 4.87. Under this transductive protocol, the adapted model also achieves the best BRISQUE and NIMA on URHI and HSTS among the compared methods.
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All authors Haoran Liu, Huibin Li, Mingzhe Liu, Peng Li, Guibin Zan
Computer Vision
ControlRef: Efficient Layout-Guided Multi-Instance Generation via Anchored 4D-RoPE
Yunkai Yang, Yudong Zhang, Xinying Chen et al. (+7 more)
Layout-guided multi-instance generation is essential for controllable image synthesis in Multi-Modal
Diffusion Transformers (MM-DiTs). However, integrating this capability into unified architectures remains challenging. Prior frameworks rely on redundant full-resolution canvas padding and Shifted-RoPE to manage multiple reference images. This mechanism drastically inflates computational overhead for sparse layouts and disrupts critical low-frequency RoPE features, creating a severe spatial-frequency compromise that blurs absolute spatial correspondence. To overcome these limitations, we propose ControlRef, a highly efficient and precise multi-instance synthesis framework. ControlRef utilizes a Unified Instance-Layout Control (UILC)
attention mask to strictly decouple inter-instance semantic interactions and enforce precise regional binding. To further promote region-level spatial
alignment, we introduce Anchored 4D-RoPE, a novel positional encoding mechanism that directly anchors
tokens to their absolute
geometric centers. By pre-aligning reference images to their corresponding bounding box resolutions, physically anchoring both layout and reference
tokens to their absolute
geometric centers, and stacking the references along the z-axis, Anchored 4D-RoPE natively preserves spatial priors and mitigates the spatial-frequency compromise without lossy shifting. Extensive experiments demonstrate that ControlRef achieves state-of-the-art visual fidelity and localization accuracy, while concurrently slashing inference latency by over 80% in sparse layouts and reducing
memory overhead by 50% in dense scenarios.
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All authors Yunkai Yang, Yudong Zhang, Xinying Chen, Haoyuan Liang, Yizhuo Niu, Jinshuai Cheng, Kunquan Zhang, Liziyue Fang, Weitao Wan, Runmin Dong