Computer Vision
VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction
Hiroto Nakata, Yawen Zou, Shunsuke Sakai et al. (+5 more)
Logical anomaly detection in industrial inspection remains challenging due to variations in visual appearance (e.g., background clutter, illumination shift, and blur), which often distract
vision-centric detectors from identifying rule-level violations. However, existing
benchmarks rarely provide controlled settings where logical states are fixed while such nuisance factors vary. To address this gap, we introduce VID-AD, a
dataset for logical anomaly detection under
vision-induced distraction. It comprises 10 manufacturing scenarios and five capture conditions, totaling 50 one-class tasks and 10,395 images. Each scenario is defined by two logical constraints selected from quantity, length, type, placement, and relation, with anomalies including both single-constraint and combined violations. We further propose a
language-based anomaly detection framework that relies solely on text descriptions generated from normal images. Using contrastive learning with positive texts and contradiction-based negative texts synthesized from these descriptions, our method learns
embeddings that capture logical attributes rather than low-level features. Extensive experiments demonstrate consistent improvements over baselines across the evaluated settings. The
dataset is available at: https://github.com/nkthiroto/VID-AD.
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All authors Hiroto Nakata, Yawen Zou, Shunsuke Sakai, Shun Maeda, Chunzhi Gu, Yijin Wei, Shangce Gao, Chao Zhang
Computer Vision
USIS-PGM: Photometric Gaussian Mixtures for Underwater Salient Instance Segmentation
Lin Hong, Xiangtong Yao, Mürüvvet Bozkurt et al. (+2 more)
Underwater salient instance
segmentation (USIS) is crucial for marine robotic systems, as it enables both
underwater salient object detection and instance-level mask prediction for visual scene understanding. Compared with its terrestrial counterpart, USIS is more challenging due to the
underwater image degradation. To address this issue, this paper proposes USIS-PGM, a single-stage framework for USIS. Specifically, the encoder enhances boundary cues through a frequency-aware module and performs content-adaptive feature reweighting via a dynamic weighting module. The decoder incorporates a
Transformer-based instance activation module to better distinguish salient instances. In addition, USIS-PGM employs multi-scale Gaussian heatmaps generated from ground-truth masks through Photometric Gaussian Mixture (PGM) to supervise intermediate decoder features, thereby improving salient instance localization and producing more structurally coherent mask predictions. Experimental results demonstrate the superiority and practical applicability of the proposed USIS-PGM model.
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All authors Lin Hong, Xiangtong Yao, Mürüvvet Bozkurt, Xin Wang, Fumin Zhang
Computer Vision
Chenru Wang, Yunyi Chen, Zijun Yang et al. (+2 more)
Dataset Distillation aims to synthesize compact
datasets that can approximate the training efficacy of large-scale real
datasets, offering an efficient solution to the increasing computational demands of modern deep learning. Recently,
diffusion-based
dataset distillation methods have shown great promise by leveraging the strong generative capacity of
diffusion models to produce diverse and structurally consistent samples. However, a fundamental goal misalignment persists:
diffusion models are optimized for generative likelihood rather than discriminative utility, resulting in over-concentration in high-density regions and inadequate coverage of boundary samples crucial for classification. To address this issue, we propose two complementary strategies. Inversion-Matching (IM) introduces an inversion-guided fine-tuning process that aligns denoising trajectories with their inversion counterparts, broadening distributional coverage and enhancing diversity. Selective Subgroup Sampling(S^3) is a training-free sampling mechanism that improves inter-class separability by selecting synthetic subsets that are both representative and distinctive. Extensive experiments demonstrate that our approach significantly enhances the discriminative quality and generalization of distilled
datasets, achieving state-of-the-art performance among
diffusion-based methods.
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All authors Chenru Wang, Yunyi Chen, Zijun Yang, Joey Tianyi Zhou, Chi Zhang
Computer Vision
DCP-CLIP:A Coarse-to-Fine Framework for Open-Vocabulary Semantic Segmentation with Dual Interaction
Jing Wang, Huimin Shi, Quan Zhou et al. (+3 more)
The recent years have witnessed the remarkable development for open-vocabulary semantic
segmentation (OVSS) using visual-
language foundation models, yet still suffer from following fundamental challenges: (1) insufficient cross-modal communications between textual and visual spaces, and (2) significant computational costs from the interactions with massive number of categories. To address these issues, this paper describes a novel coarse-to-fine framework, called DCP-CLIP, for OVSS. Unlike prior efforts that mainly relied on pre-established category content and the inherent spatial-class interaction capability of CLIP, we dynamic constructing category-relevant textual features and explicitly models dual interactions between spatial image features and textual class semantics. Specifically, we first leverage CLIP's open-vocabulary recognition capability to identify semantic categories relevant to the image context, upon which we dynamically generate corresponding textual features to serve as initial textual guidance. Subsequently, we conduct a coarse
segmentation by cross-modally integrating semantic information from textual guidance into the visual representations and achieve refined
segmentation by integrating spatially enriched features from the encoder to recover fine-grained details and enhance spatial resolution. In final, we leverage spatial information from the
segmentation side to refine category predictions for each mask, facilitating more precise semantic labeling. Experiments on multiple OVSS
benchmarks demonstrate that DCP-CLIP outperforms existing methods by delivering both higher accuracy and greater efficiency.
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All authors Jing Wang, Huimin Shi, Quan Zhou, Qibo Liu, Suofei Zhang, Huimin Lu
Computer Vision
Sat-JEPA-Diff: Bridging Self-Supervised Learning and Generative Diffusion for Remote Sensing
Kursat Komurcu and Linas Petkevicius
Predicting satellite imagery requires a balance between structural accuracy and textural detail. Standard deterministic methods like PredRNN or SimVP minimize pixel-based errors but suffer from the "regression to the mean" problem, producing blurry outputs that obscure subtle geographic-spatial features. Generative models provide realistic textures but often misleadingly reveal structural anomalies. To bridge this gap, we introduce Sat-JEPA-Diff, which combines Self-Supervised Learning (SSL) with Hidden
Diffusion Models (LDM). An IJEPA module predicts stable semantic representations, which then route a frozen Stable
Diffusion backbone via a lightweight cross-
attention adapter. This ensures that the synthesized high-accuracy textures are based on absolutely accurate structural predictions. Evaluated on a global Sentinel-2
dataset, Sat-JEPA-Diff excels at resolving sharp boundaries. It achieves leading perceptual scores (GSSIM: 0.8984, FID: 0.1475) and significantly outperforms deterministic baselines, despite standard autoregressive stability limits. The code and
dataset are publicly available on https://github.com/VU-AIML/SAT-JEPA-DIFF.
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Computer Vision
Bidirectional Cross-Attention Fusion of High-Resolution RGB and Low-Resolution Hyperspectral Inputs for Multimodal Semantic Segmentation
Jonas V. Funk, Lukas Roming, Andreas Michel et al. (+4 more)
Multimodal semantic
segmentation with heterogeneous sensors must reconcile complementary information across modalities that differ in spatial resolution and channel dimensionality. In particular, high-resolution RGB imaging provides detailed spatial structure but often fails to distinguish visually similar materials, whereas hyperspectral imaging (HSI) provides discriminative spectral signatures but at lower spatial resolution. We present Bidirectional Cross-
Attention Fusion (BCAF), which aligns high-resolution RGB with low-resolution HSI at their native grids via localized, bidirectional cross-
attention, avoiding pre-upsampling or early spectral collapse. BCAF uses two independent backbones: a standard Swin
Transformer for RGB and an HSI-adapted Swin backbone that preserves spectral structure through 3D tokenization with spectral self-
attention. Although our evaluation targets RGB-HSI fusion, BCAF is modality-agnostic and applies to co-registered RGB with lower-resolution, high-channel auxiliary sensors. On the
benchmark SpectralWaste
dataset, BCAF delivers strong performance, achieving 75.4% at 55 images/s. We further evaluate a novel industrial
dataset: K3I-Cycling (first RGB subset already released on Fordatis). On this
dataset, BCAF reaches 62.3% mIoU for material
segmentation (paper, metal, plastic, etc.) and 66.2% mIoU for plastic-type
segmentation (PET, PP, HDPE, LDPE, PS, etc.). These results show that preserving native-grid spatial detail and spectral structure improves
multimodal segmentation under real-time constraints. Code and model checkpoints are publicly available at https://github.com/jonasvilhofunk/BCAF_2026.
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All authors Jonas V. Funk, Lukas Roming, Andreas Michel, Paul Bäcker, Georg Maier, Thomas Längle, Markus Klute
Computer Vision
Discriminative Flow Matching Via Local Generative Predictors
Om Govind Jha, Manoj Bamniya and Ayon Borthakur
Traditional discriminative computer
vision relies predominantly on static projections, mapping input features to outputs in a single computational step. Although efficient, this paradigm lacks the iterative refinement and robustness inherent in biological
vision and modern generative modelling. In this paper, we propose Discriminative Flow Matching, a framework that reformulates classification and object detection as a conditional transport process. By learning a vector field that continuously transports samples from a simple noise distribution toward a task-aligned target
manifold – such as class
embeddings or bounding box coordinates – we are at the interface between generative and discriminative learning. Our method attaches multiple independent flow predictors to a shared backbone. These predictors are trained using local flow matching objectives, where gradients are computed independently for each block. We formulate this approach for standard image classification and extend it to the complex task of object detection, where targets are high-dimensional and spatially distributed. This architecture provides the flexibility to update blocks either sequentially to minimise activation
memory or in parallel to suit different hardware constraints. By aggregating the predictions from these independent flow predictors, our framework enables robust, generative-inspired inference across diverse architectures, including CNNs and
vision transformers.
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Computer Vision
OpenCOOD-Air: Prompting Heterogeneous Ground-Air Collaborative Perception with Spatial Conversion and Offset Prediction
Xianke Wu, Songlin Bai, Chengxiang Li et al. (+5 more)
While Vehicle-to-Vehicle (V2V) collaboration extends sensing ranges through multi-
agent data sharing, its reliability remains severely constrained by ground-level occlusions and the limited perspective of chassis-mounted sensors, which often result in critical perception blind spots. We propose OpenCOOD-Air, a novel framework that integrates
UAVs as extensible platforms into V2V collaborative perception to overcome these constraints. To mitigate gradient interference from ground-air domain gaps and data sparsity, we adopt a transfer learning strategy to fine-tune
UAV weights from pre-trained V2V models. To prevent the spatial information loss inherent in this transition, we formulate ground-air collaborative perception as a heterogeneous integration task with explicit altitude supervision and introduce a Cross-Domain Spatial Converter (CDSC) and a Spatial Offset Prediction
Transformer (SOPT). Furthermore, we present the OPV2V-Air
benchmark to validate the transition from V2V to Vehicle-to-Vehicle-to-
UAV. Compared to state-of-the-art methods, our approach improves 2D and 3D AP@0.7 by 4% and 7%, respectively.
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All authors Xianke Wu, Songlin Bai, Chengxiang Li, Zhiyao Luo, Yulin Tian, Fenghua Zhu, Yisheng Lv, Yonglin Tian
Computer Vision
Evaluation of Visual Place Recognition Methods for Image Pair Retrieval in 3D Vision and Robotics
Dennis Haitz, Athradi Shritish Shetty, Michael Weinmann et al. (+1 more)
Visual Place Recognition (VPR) is a core component in computer
vision, typically formulated as an image
retrieval task for localization, mapping, and
navigation. In this work, we instead study VPR as an image pair
retrieval front-end for registration pipelines, where the goal is to find top-matching image pairs between two disjoint image sets for downstream tasks such as scene registration, SLAM, and Structure-from-Motion. We comparatively evaluate state-of-the-art VPR families - NetVLAD-style baselines, classification-based global descriptors (CosPlace, EigenPlaces), feature-mixing (MixVPR), and foundation-model-driven methods (AnyLoc, SALAD, MegaLoc) - on three challenging
datasets: object-centric outdoor scenes (Tanks and Temples), indoor RGB-D scans (ScanNet-GS), and autonomous-driving sequences (KITTI). We show that modern global descriptor approaches are increasingly suitable as off-the-shelf image pair
retrieval modules in challenging scenarios including perceptual aliasing and incomplete sequences, while exhibiting clear, domain-dependent strengths and weaknesses that are critical when choosing VPR components for robust mapping and registration.
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All authors Dennis Haitz, Athradi Shritish Shetty, Michael Weinmann, Markus Ulrich
Computer Vision
Towards Stable Self-Supervised Object Representations in Unconstrained Egocentric Video
Yuting Tan, Xilong Cheng, Yunxiao Qin et al. (+2 more)
Humans develop visual intelligence through perceiving and interacting with their environment - a self-supervised learning process grounded in
egocentric experience. Inspired by this, we ask how can artificial systems learn stable object representations from continuous, uncurated first-person
videos without relying on manual annotations. This setting poses challenges of separating, recognizing, and persistently tracking objects amid clutter, occlusion, and ego-motion. We propose EgoViT, a unified
vision Transformer framework designed to learn stable object representations from unlabeled
egocentric video. EgoViT bootstraps this learning process by jointly discovering and stabilizing "proto-objects" through three synergistic mechanisms: (1) Proto-object Learning, which uses intra-frame
distillation to form discriminative representations; (2) Depth Regularization, which grounds these representations in
geometric structure; and (3) Teacher-Filtered Temporal Consistency, which enforces identity over time. This creates a virtuous cycle where initial object hypotheses are progressively refined into stable, persistent representations. The framework is trained end-to-end on unlabeled first-person
videos and exhibits robustness to
geometric priors of varied origin and quality. On standard
benchmarks, EgoViT achieves +8.0% CorLoc improvement in unsupervised object discovery and +4.8% mIoU improvement in semantic
segmentation, demonstrating its potential to lay a foundation for robust visual abstraction in
embodied intelligence.
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All authors Yuting Tan, Xilong Cheng, Yunxiao Qin, Zhengnan Li, Jingjing Zhang
Computer Vision
Scene Generation at Absolute Scale: Utilizing Semantic and Geometric Guidance From Text for Accurate and Interpretable 3D Indoor Scene Generation
Stefan Ainetter, Thomas Deixelberger, Edoardo A. Dominici et al. (+3 more)
We present GuidedSceneGen, a text-to-3D generation framework that produces metrically accurate, globally consistent, and semantically interpretable indoor scenes. Unlike prior text-driven methods that often suffer from
geometric drift or scale ambiguity, our approach maintains an absolute world coordinate frame throughout the entire generation process. Starting from a textual scene description, we predict a global 3D layout encoding both semantic and
geometric structure, which serves as a guiding proxy for downstream stages. A semantics- and depth-conditioned panoramic
diffusion model then synthesizes 360{\deg} imagery aligned with the global layout, substantially improving spatial coherence. To explore unobserved regions, we employ a
video diffusion model guided by optimized camera trajectories that balances coverage and collision avoidance, achieving up to 10x faster sampling compared to exhaustive path exploration. The generated views are fused using 3D Gaussian Splatting, yielding a consistent and fully navigable 3D scene in absolute scale. GuidedSceneGen enables accurate transfer of object poses and semantic labels from layout to reconstruction, and supports progressive scene expansion without re-
alignment. Quantitative results and a user study demonstrate greater 3D consistency and layout plausibility compared to recent panoramic text-to-3D baselines.
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All authors Stefan Ainetter, Thomas Deixelberger, Edoardo A. Dominici, Philipp Drescher, Konstantinos Vardis, Markus Steinberger
Computer Vision · Cross-listed from cs.RO
LineMaster Pro: A Low-Cost Intelligent Line Following Robot with PID Control and Ultrasonic Obstacle Avoidance for Educational Robotics
Jeni Shahi, Abhishek Shah and A. S. M. Ahsanul Sarkar Akib
Line following
robots are fundamental platforms in robotics education, yet commercially available solutions remain prohibitively expensive ($150-300$) while lacking integrated obstacle detection capabilities essential for real-world applications. This paper presents LineMaster Pro, an intelligent low-cost line following
robot implemented on an Arduino Nano platform that integrates dual TCRT5000 infrared sensors for precision line tracking, an HC-SR04 ultrasonic sensor for real-time obstacle detection, a digitally tuned PID controller with Ziegler-Nichols optimization, and a hierarchical finite state machine for robust obstacle avoidance. A systematic four-phase sensor calibration methodology ensures reliable operation across varying lighting and surface conditions. Experimental validation through 200 controlled trials and 72-hour continuous operation demonstrates mean tracking accuracy of 1.18 cm at 0.4 m/s (95% CI [1.06, 1.30]), obstacle detection reliability of 96.7% within 10-40 cm range with 0.7% false positive rate, and 94% successful recovery from path deviations. The PID implementation achieves 43% improvement over conventional on-off control ($p<0.001$). At a total hardware cost of \$28.50 based on verified Bangladesh
market prices, LineMaster Pro achieves a 94% cost reduction compared to commercial alternatives, establishing a practical
benchmark for accessible robotics education in resource-constrained environments.
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Computer Vision
Pixel-level Scene Understanding in One Token: Visual States Need What-is-Where Composition
Seokmin Lee, Yunghee Lee, Byeonghyun Pak et al. (+1 more)
For robotic
agents operating in dynamic environments, learning visual state representations from streaming
video observations is essential for sequential decision making. Recent self-supervised learning methods have shown strong transferability across
vision tasks, but they do not explicitly address what a good visual state should encode. We argue that effective visual states must capture what-is-where by jointly encoding the semantic identities of scene elements and their spatial locations, enabling reliable detection of subtle dynamics across observations. To this end, we propose CroBo, a visual state representation learning framework based on a global-to-local reconstruction objective. Given a reference observation compressed into a compact bottleneck
token, CroBo learns to reconstruct heavily masked patches in a local target crop from sparse visible cues, using the global bottleneck
token as context. This learning objective encourages the bottleneck
token to encode a fine-grained representation of scene-wide semantic entities, including their identities, spatial locations, and configurations. As a result, the learned visual states reveal how scene elements move and interact over time, supporting sequential decision making. We evaluate CroBo on diverse
vision-based
robot policy learning
benchmarks, where it achieves state-of-the-art performance. Reconstruction analyses and perceptual straightness experiments further show that the learned representations preserve pixel-level scene composition and encode what-moves-where across observations. Project page available at: https://seokminlee-chris.github.io/CroBo-ProjectPage.
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All authors Seokmin Lee, Yunghee Lee, Byeonghyun Pak, Byeongju Woo
Computer Vision
CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution
Liutao Yang, Zi Wang, Peiyuan Jing et al. (+5 more)
PET super-resolution is highly under-constrained because paired multi-resolution scans from the same subject are rarely available, and effective resolution is determined by scanner-specific physics (e.g., PSF, detector geometry, and acquisition settings). This limits supervised end-to-end training and makes purely image-domain generative restoration prone to hallucinated structures when anatomical and physical constraints are weak. We formulate PET super-resolution as posterior inference under heterogeneous system configurations and propose a CT-conditioned
diffusion framework with physics-constrained sampling. During training, a conditional
diffusion prior is learned from high-quality PET/CT pairs using cross-
attention for anatomical guidance, without requiring paired LR–HR PET data. During inference, measurement consistency is enforced through a scanner-aware forward model with explicit PSF effects and gradient-based data-consistency refinement. Under both standard and OOD settings, the proposed method consistently improves experimental metrics and lesion-level clinical relevance indicators over strong baselines, while reducing
hallucination artifacts and improving structural fidelity.
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All authors Liutao Yang, Zi Wang, Peiyuan Jing, Xiaowen Wang, Javier A. Montoya-Zegarra, Kuangyu Shi, Daoqiang Zhang, Guang Yang
Computer Vision · Cross-listed from cs.LG
Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels
Zhanhui Lin, Yanlin Liu and Sanping Zhou
Training deep neural networks with noisy labels remains a significant challenge, often leading to degraded performance. Existing methods for handling label noise typically rely on either transition
matrix, noise detection, or meta-learning techniques, but they often exhibit low utilization efficiency of noisy samples and incur high computational costs. In this paper, we propose a self-training label correction framework using decoupled bilevel optimization, where a classifier and neural correction function co-evolve. Leveraging a small clean
dataset, our method employs noisy posterior simulation and intermediate features to transfer ground-truth knowledge, forming a closed-loop feedback system that prevents error amplification. Theoretical guarantees underpin the stability of our approach, and extensive experiments on
benchmark datasets like CIFAR and Clothing1M confirm state-of-the-art performance with reduced training time, highlighting its practical applicability for learning from noisy labels.
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Computer Vision · Cross-listed from cs.LG
UVLM: A Universal Vision-Language Model Loader for Reproducible Multimodal Benchmarking
Joan Perez and Giovanni Fusco
Vision-
Language Models (VLMs) have emerged as powerful tools for image understanding tasks, yet their practical deployment remains hindered by significant architectural heterogeneity across model families. This paper introduces UVLM (Universal
Vision-
Language Model Loader), a Google Colab-based framework that provides a unified interface for loading, configuring, and benchmarking multiple VLM architectures on custom image analysis tasks. UVLM currently supports two major model families – LLaVA-NeXT and Qwen2.5-VL – which differ fundamentally in their
vision encoding, tokenization, and decoding strategies. The framework abstracts these differences behind a single inference function, enabling researchers to compare models using identical
prompts and evaluation protocols. Key features include a multi-task
prompt builder with support for four response types (numeric, category, boolean, text), a consensus validation mechanism based on majority voting across repeated inferences, a flexible
token budget (up to 1,500
tokens) enabling users to design custom
reasoning strategies through
prompt engineering, and a built-in chain-of-thought reference mode for benchmarking. UVLM is designed for reproducibility, accessibility, and extensibility and as such is freely deployable on Google Colab using consumer-grade GPU resources. The paper also presents the first benchmarking of different VLMs on tasks of increasing
reasoning complexity using a corpus of 120 street-view images.
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Computer Vision
Multi-Modal Character Localization and Extraction for Chinese Text Recognition
Qilong Li and Chongsheng Zhang
Scene text recognition (STR) methods have demonstrated their excellent capability in English text images. However, due to the complex inner structures of Chinese and the extensive character categories, it poses challenges for recognizing Chinese text in images. Recently, studies have shown that the methods designed for English text recognition encounter an accuracy bottleneck when recognizing Chinese text images. This raises the question: Is it appropriate to apply the model developed for English to the Chinese STR task? To explore this issue, we propose a novel method named LER, which explicitly decouples each character and independently recognizes characters while taking into account the complex inner structures of Chinese. LER consists of three modules: Localization, Extraction, and Recognition. Firstly, the localization module utilizes
multimodal information to determine the character's position precisely. Then, the extraction module dissociates all characters in parallel. Finally, the recognition module considers the unique inner structures of Chinese to provide the text prediction results. Extensive experiments conducted on large-scale Chinese
benchmarks indicate that our method significantly outperforms existing methods. Furthermore, extensive experiments conducted on six English
benchmarks and the Union14M
benchmark show impressive results in English text recognition by LER. Code is available at https://github.com/Pandarenlql/LER.
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Computer Vision
SCoCCA: Multi-modal Sparse Concept Decomposition via Canonical Correlation Analysis
Ehud Gordon, Meir Yossef Levi and Guy Gilboa
Interpreting the internal
reasoning of
vision-
language models is essential for deploying AI in safety-critical domains. Concept-based explainability provides a human-aligned lens by representing a model's behavior through semantically meaningful components. However, existing methods are largely restricted to images and overlook the cross-modal interactions. Text-image
embeddings, such as those produced by CLIP, suffer from a modality gap, where visual and textual features follow distinct distributions, limiting interpretability. Canonical Correlation Analysis (CCA) offers a principled way to align features from different distributions, but has not been leveraged for multi-modal concept-level analysis. We show that the objectives of CCA and InfoNCE are closely related, such that optimizing CCA implicitly optimizes InfoNCE, providing a simple, training-free mechanism to enhance cross-modal
alignment without affecting the pre-trained InfoNCE objective. Motivated by this observation, we couple concept-based explainability with CCA, introducing Concept CCA (CoCCA), a framework that aligns cross-modal
embeddings while enabling interpretable concept decomposition. We further extend it and propose Sparse Concept CCA (SCoCCA), which enforces sparsity to produce more disentangled and discriminative concepts, facilitating improved activation, ablation, and semantic manipulation. Our approach generalizes concept-based explanations to multi-modal
embeddings and achieves state-of-the-art performance in concept discovery, evidenced by reconstruction and manipulation tasks such as concept ablation.
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Computer Vision
Dual-Strategy Improvement of YOLOv11n for Multi-Scale Object Detection in Remote Sensing Images
Shuaiyu Zhu and Sergey Ablameyko
Satellite remote sensing images pose significant challenges for object detection due to their high resolution, complex scenes, and large variations in target scales. To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery, this paper proposes two improvement strategies. Method 1: (a) a Large Separable Kernel
Attention (LSKA) mechanism is introduced into the backbone network to enhance feature extraction for small objects; (b) a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion, thereby improving the detection performance of objects at different scales. Method 2: (a) the Gold-YOLO structure is also integrated into the neck network; (b) a MultiSEAMHead detection head is combined to further strengthen the representation and detection capability for small and multi-scale objects. To verify the effectiveness of the proposed improvements, experiments are conducted on the DOTAv1
dataset. The results show that, while maintaining the lightweight advantage of the model, the proposed methods improve detection accuracy (mAP@0.5) by 1.3% and 1.8%, respectively, compared with the baseline YOLOv11n, demonstrating the effectiveness and practical value of the proposed approaches for object detection in remote sensing images.
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Computer Vision
Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering
Lin Fan, Yafei Ou, Zhipeng Deng et al. (+8 more)
Chain-of-thought (CoT)
reasoning has advanced medical visual question answering (VQA), yet most existing CoT rationales are free-form and fail to capture the structured
reasoning process clinicians actually follow. This work asks: Can traceable, multi-step
reasoning supervision improve
reasoning accuracy and the interpretability of Medical VQA? To this end, we introduce Step-CoT, a large-scale medical
reasoning dataset with expert-curated, structured multi-step CoT aligned to clinical diagnostic workflows, implicitly grounding the model's
reasoning in radiographic evidence. Step-CoT comprises more than 10K real clinical cases and 70K VQA pairs organized around diagnostic workflows, providing supervised intermediate steps that guide models to follow valid
reasoning trajectories. To effectively learn from Step-CoT, we further introduce a teacher-student framework with a dynamic graph-structured focusing mechanism that prioritizes diagnostically informative steps while filtering out less relevant contexts. Our experiments show that using Step-CoT can improve
reasoning accuracy and interpretability.
Benchmark: github.com/hahaha111111/Step-CoT.
Dataset Card: huggingface.co/
datasets/fl-15o/Step-CoT
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All authors Lin Fan, Yafei Ou, Zhipeng Deng, Pengyu Dai, Hou Chongxian, Jiale Yan, Yaqian Li, Kaiwen Long, Xun Gong, Masayuki Ikebe, Yefeng Zheng
Computer Vision
Zero-Forgetting CISS via Dual-Phase Cognitive Cascades
Yuquan Lu, Yifu Guo, Zishan Xu et al. (+6 more)
Continual semantic
segmentation (CSS) is a cornerstone task in computer
vision that enables a large number of downstream applications, but faces the catastrophic forgetting challenge. In conventional class-incremental semantic
segmentation (CISS) frameworks using Softmax-based classification heads, catastrophic forgetting originates from Catastrophic forgetting and task affiliation probability. We formulate these problems and provide a theoretical analysis to more deeply understand the limitations in existing CISS methods, particularly Strict Parameter Isolation (SPI). To address these challenges, we follow a dual-phase intuition from human annotators, and introduce Cognitive Cascade
Segmentation (CogCaS), a novel dual-phase cascade formulation for CSS tasks in the CISS setting. By decoupling the task into class-existence detection and class-specific
segmentation, CogCaS enables more effective continual learning, preserving previously learned knowledge while incorporating new classes. Using two
benchmark datasets PASCAL VOC 2012 and ADE20K, we have shown significant improvements in a variety of challenging scenarios, particularly those with long sequence of incremental tasks, when compared to exsiting state-of-the-art methods. Our code will be made publicly available upon paper acceptance.
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All authors Yuquan Lu, Yifu Guo, Zishan Xu, Siyu Zhang, Yu Huo, Siyue Chen, Siyan Wu, Chenghua Zhu, Ruixuan Wang
Computer Vision · Cross-listed from cs.CR
Backdoor Attacks under Lossy Compression: From Failure to Reactivation and Adaptation
Qian Li, Yunuo Chen and Yuntian Chen
Real-world
backdoor attacks often require poisoned
datasets to be stored and transmitted before they are used to compromise deep learning systems. In the era of big data, however, the inevitable use of lossy compression poses a fundamental challenge to invisible
backdoor attacks. We observe that triggers embedded in RGB images can become ineffective once the images are lossily compressed into binary bitstreams, such as JPEG files, for storage and transmission. Consequently, poisoned data may lose their malicious functionality after compression, causing
backdoor injection to fail. Prior compression-based attacks typically exploit compression artifacts as certain triggers to distinguish poisoned RGB samples from uncompressed benign ones, rather than addressing whether malicious information can survive a shared lossy storage-and-transmission pipeline. In this paper, we highlight the necessity of explicitly accounting for lossy compression in
backdoor attacks. This requires attackers to ensure that transmitted binary bitstreams preserve malicious trigger information, such that effective triggers can be induced after decompression. Building on the region-of-interest (ROI) coding mechanism in image compression, we propose two poisoning strategies tailored to inevitable lossy compression. First, we introduce Universal Attack Reactivation, a general method that uses sample-specific ROI masks to reactivate trigger information in bitstreams for learned image compression (LIC). Second, we present Compression-Adapted Attack, a new attack strategy that employs customized ROI masks to encode trigger information into bitstreams and applies to both traditional codecs and LIC. Extensive experiments demonstrate the effectiveness of both strategies.
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Computer Vision
Geo-ID: Test-Time Geometric Consensus for Cross-View Consistent Intrinsics
Alara Dirik and Stefanos Zafeiriou
Intrinsic image decomposition aims to estimate physically based rendering (PBR) parameters such as albedo, roughness, and
metallicity from images. While recent methods achieve strong single-view predictions, applying them independently to multiple views of the same scene often yields inconsistent estimates, limiting their use in downstream applications such as editable neural scenes and 3D reconstruction.
Video-based models can improve cross-frame consistency but require dense, ordered sequences and substantial compute, limiting their applicability to sparse, unordered image collections. We propose Geo-ID, a novel test-time framework that repurposes pretrained single-view intrinsic predictors to produce cross-view consistent decompositions by coupling independent per-view predictions through sparse
geometric correspondences that form uncertainty-aware consensus targets. Geo-ID is model-agnostic, requires no retraining or inverse rendering, and applies directly to off-the-shelf intrinsic predictors. Experiments on synthetic
benchmarks and real-world scenes demonstrate substantial improvements in cross-view intrinsic consistency as the number of views increases, while maintaining comparable single-view decomposition performance. We further show that the resulting consistent intrinsics enable coherent appearance editing and relighting in downstream neural scene representations.
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Computer Vision
Learning through Creation: A Hash-Free Framework for On-the-Fly Category Discovery
Bohan Zhang, Weidong Tang, Zhixiang Chi et al. (+4 more)
On-the-Fly Category Discovery (OCD) aims to recognize known classes while simultaneously discovering emerging novel categories during inference, using supervision only from known classes during offline training. Existing approaches rely either on fixed label supervision or on
diffusion-based augmentations to enhance the backbone, yet none of them explicitly train the model to perform the discovery task required at test time. It is fundamentally unreasonable to expect a model optimized on limited labeled data to carry out a qualitatively different discovery objective during inference. This mismatch creates a clear optimization misalignment between the offline learning stage and the online discovery stage. In addition, prior methods often depend on hash-based encodings or severe feature compression, which further limits representational capacity. To address these issues, we propose Learning through Creation (LTC), a fully feature-based and hash-free framework that injects novel-category awareness directly into offline learning. At its core is a lightweight, online pseudo-unknown generator driven by kernel-energy minimization and
entropy maximization (MKEE). Unlike previous methods that generate synthetic samples once before training, our generator evolves jointly with the model dynamics and synthesizes pseudo-novel instances on the fly at negligible cost. These samples are incorporated through a dual max-margin objective with adaptive thresholding, strengthening the model's ability to delineate and detect unknown regions through explicit creation. Extensive experiments across seven
benchmarks show that LTC consistently outperforms prior work, achieving improvements ranging from 1.5 percent to 13.1 percent in all-class accuracy. The code is available at https://github.com/brandinzhang/LTC
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All authors Bohan Zhang, Weidong Tang, Zhixiang Chi, Yi Jin, Zhenbo Li, Yang Wang, Yanan Wu
Computer Vision · Cross-listed from cs.LG
Can AI Understand the Language of Origami?
Naaisha Agarwal, Yihan Wu, Xin Guan et al. (+7 more)
Building AI systems that can plan, act, and create in the physical world requires more than pattern recognition. Such systems must reason about the generative mechanisms and constraints governing physical processes, using structured representations that connect observations, actions, and their effects. Yet, many existing
benchmarks study these capabilities separately, focusing either on visual recognition or on abstract symbolic or programmatic
reasoning. Origami provides a natural testbed that integrates these abilities: constructing shapes through folds requires visual perception,
reasoning about
geometric and physical constraints, and sequential planning, while remaining sufficiently structured for systematic evaluation. We introduce OrigamiBench, a
benchmark for evaluating programmatic understanding of the mechanisms underlying origami synthesis through a high-level
language of physically grounded fold actions. Experiments with modern
vision-
language models reveal that scaling model size alone does not reliably improve
reasoning about physical transformations. Moreover, models struggle to ground programmatic information in visual observations, suggesting that visual and
language representations remain weakly integrated.
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All authors Naaisha Agarwal, Yihan Wu, Xin Guan, Ayaan Garg, Yikuan Hu, Mohan Li, Vincenzo Collura, Wang-Zhou Dai, Yao-Xiang Ding, Emanuele Sansone
Computer Vision
VFM-Loc: Training-Free Cross-View Geo-Localization via Aligning Discriminative Visual Hierarchies
Jun Lu, Zehao Sang, Haoqi Wei et al. (+5 more)
Cross-View Geo-Localization (CVGL) in remote sensing aims to locate a drone-view query by matching it to geo-tagged satellite images. Although supervised methods have achieved strong results on close-set
benchmarks, they often fail to generalize to unconstrained, real-world scenarios due to severe viewpoint differences and
dataset bias. To overcome these limitations, we present VFM-Loc, a training-free CVGL framework that leverages the generalizable visual representations from
vision foundational models (VFMs). VFM-Loc identifies and matches discriminative visual clues across different viewpoints through a progressive
alignment strategy. First, we design a hierarchical clue extraction mechanism using Generalized Mean pooling and Scale-Weighted R-MAC to preserve distinctive visual clues across scales while maintaining hierarchical confidence. Second, we introduce a statistical
manifold alignment pipeline based on domain-wise PCA and Orthogonal Procrustes analysis, linearly aligning heterogeneous feature distributions in a shared metric space. Experiments demonstrate that VFM-Loc exhibits high accuracy on standard
benchmarks and surpasses supervised methods by over 20% in Recall@1 on the challenging LO-UCV
dataset with large oblique angles. This work highlights that principled
alignment of pre-trained features can effectively bridge the cross-view gap, establishing a robust and training-free paradigm for real-world CVGL. The relevant code is made available at: github.com/DingLei14/VFM-Loc.
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All authors Jun Lu, Zehao Sang, Haoqi Wei, Xiangyun Liu, Kun Zhu, Haitao Guo, Zhihui Gong, Lei Ding
Computer Vision
MOGeo: Beyond One-to-One Cross-View Object Geo-localization
Bo Lv, Qingwang Zhang, Le Wu et al. (+2 more)
Cross-View Object Geo-Localization (CVOGL) aims to locate an object of interest in a query image within a corresponding satellite image. Existing methods typically assume that the query image contains only a single object, which does not align with the complex, multi-object geo-localization requirements in real-world applications, making them unsuitable for practical scenarios. To bridge the gap between the realistic setting and existing task, we propose a new task, called Cross-View Multi-Object Geo-Localization (CVMOGL). To advance the CVMOGL task, we first construct a
benchmark, CMLocation, which includes two
datasets: CMLocation-V1 and CMLocation-V2. Furthermore, we propose a novel cross-view multi-object geo-localization method, MOGeo, and
benchmark it against existing state-of-the-art methods. Extensive experiments are conducted under various application scenarios to validate the effectiveness of our method. The results demonstrate that cross-view object geo-localization in the more realistic setting remains a challenging problem, encouraging further research in this area.
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All authors Bo Lv, Qingwang Zhang, Le Wu, Yuanyuan Li, Yingying Zhu
Computer Vision
Efficient Semi-Automated Material Microstructure Analysis Using Deep Learning: A Case Study in Additive Manufacturing
Sanjeev S. Navaratna, Nikhil Thawari, Gunashekhar Mari et al. (+3 more)
Image
segmentation is fundamental to microstructural analysis for defect identification and structure-property correlation, yet remains challenging due to pronounced heterogeneity in materials images arising from varied processing and testing conditions. Conventional image processing techniques often fail to capture such complex features rendering them ineffective for large-scale analysis. Even deep learning approaches struggle to generalize across heterogeneous
datasets due to scarcity of high-quality labeled data. Consequently,
segmentation workflows often rely on manual expert-driven annotations which are labor intensive and difficult to scale. Using an additive manufacturing (AM)
dataset as a case study, we present a semi-automated active learning based
segmentation pipeline that integrates a U-Net based convolutional neural network with an interactive user annotation and correction interface and a representative core-set image selection strategy. The active learning workflow iteratively updates the model by incorporating user corrected
segmentations into the training pool while the core-set strategy identifies representative images for annotation. Three subset selection strategies, manual selection, uncertainty driven sampling and proposed maximin Latin hypercube sampling from
embeddings (SMILE) method were evaluated over six refinement rounds. The SMILE strategy consistently outperformed other approaches, improving the macro F1 score from 0.74 to 0.93 while reducing manual annotation time by about 65 percent. The segmented defect regions were further analyzed using a coupled classification model to categorize defects based on microstructural characteristics and map them to corresponding AM process parameters. The proposed framework reduces labeling effort while maintaining scalability and robustness and is broadly applicable to image based analysis across diverse materials systems.
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All authors Sanjeev S. Navaratna, Nikhil Thawari, Gunashekhar Mari, Amritha V P, Murugaiyan Amirthalingam, Rohit Batra
Computer Vision · Cross-listed from cs.AI
PA-Net: Precipitation-Adaptive Mixture-of-Experts for Long-Tail Rainfall Nowcasting
Xinyu Xiao, Sen Lei, Eryun Liu et al. (+5 more)
Precipitation nowcasting is vital for flood warning, agricultural management, and emergency response, yet two bottlenecks persist: the prohibitive cost of modeling million-scale spatiotemporal
tokens from multi-variate atmospheric fields, and the extreme long-tailed rainfall distribution where heavy-to-torrential events – those of greatest societal impact – constitute fewer than 0.1% of all samples. We propose the Precipitation-Adaptive Network (PA-Net), a
Transformer framework whose computational budget is explicitly governed by rainfall intensity. Its core component, Precipitation-Adaptive MoE (PA-MoE), dynamically scales the number of activated experts per
token according to local precipitation magnitude, channeling richer representational capacity toward the rare yet critical heavy-rainfall tail. A Dual-Axis Compressed Latent
Attention mechanism factorizes spatiotemporal
attention with convolutional reduction to manage massive context lengths, while an intensity-aware training protocol progressively amplifies learning signals from extreme-rainfall samples. Experiment on ERA5 demonstrate consistent improvements over state-of-the-art baselines, with particularly significant gains in heavy-rain and rainstorm regimes.
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All authors Xinyu Xiao, Sen Lei, Eryun Liu, Shiming Xiang, Hao Li, Cheng Yuan, Yuan Qi, Qizhao Jin
Computer Vision
ALTIS: Automated Loss Triage and Impact Scoring from Sentinel-1 SAR for Property-Level Flood Damage Assessment
Amogh Vinaykumar and Prem Kamasani
Floods are among the costliest natural catastrophes globally, yet the property and casualty insurance industry's post-event response remains heavily reliant on manual field inspection: slow, expensive, and geographically constrained. Satellite Synthetic Aperture Radar (SAR) offers cloud-penetrating, all-weather imaging uniquely suited to rapid post-flood assessment, but existing research evaluates SAR flood detection against academic
benchmarks such as IoU and F1-score that do not capture insurance-workflow requirements. We present ALTIS: a five-stage pipeline transforming raw Sentinel-1 GRD and SLC imagery into property-level impact scores within 24-48 hours of flood peak. Unlike prior approaches producing pixel-level maps or binary outputs, ALTIS delivers a ranked, confidence-scored triage list consumable by claims platforms, integrating (i) multi-temporal SAR change detection using dual-polarization VV/VH intensity and InSAR coherence, (ii) physics-informed depth estimation fusing flood extent with high-resolution DEMs, (iii) property-level zonal statistics from parcel footprints, (iv) depth-damage calibration against NFIP claims, and (v) confidence-scored triage ranking. We formally define Insurance-Grade Flood Triage (IGFT) and introduce the Inspection Reduction Rate (IRR) and Triage Efficiency Score (TES). Using Hurricane Harvey (2017) across Harris County, Texas, we present preliminary analysis grounded in validated sub-components suggesting ALTIS is designed to achieve an IRR of approximately 0.52 at 90% recall of high-severity claims, potentially eliminating over half of unnecessary dispatches. By blending SAR flood intelligence with the realities of claims management, ALTIS establishes a methodological baseline for translating earth observation research into measurable insurance outcomes.
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