Computer Vision
RFMSR: Residual Flow Matching for Image Super-Resolution
Shuwei Huang, Tianyao Luo, Jicheng Liu et al. (+1 more)
Image super-resolution (ISR) has witnessed remarkable progress with
diffusion models and flow matching. The dominant text-to-image (T2I) based approaches leverage large-scale foundation models as generative priors, achieving impressive perceptual quality but at the cost of massive model sizes and prohibitive training expenses. Recent flow-matching-based
vision-only approaches have made significant strides; however, they adopt standard flow formulations that transport from a pure Gaussian prior to the data distribution, discarding the rich structural information already present in the low-quality (LQ) input. Furthermore, existing single-step acceleration techniques often forfeit the model's multi-step inference capability. In this paper, we propose Residual Flow Matching for Image Super-Resolution (RFMSR), a
vision-only framework that centers the source distribution at the LQ latent, reducing transport distance and preserving structural priors throughout the flow trajectory. We further introduce a two-phase training strategy: Phase I pretrains the velocity field via conditional flow matching, while Phase II applies end-to-end supervision to the single-step prediction while retaining the velocity loss across all timesteps, achieving high-quality single-step generation without sacrificing multi-step refinement. Extensive experiments demonstrate that RFMSR achieves comparable or even superior perceptual quality compared to state-of-the-art (SOTA) methods. The source code is available at https://github.com/Faze-Hsw/RFMSR.
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All authors Shuwei Huang, Tianyao Luo, Jicheng Liu, Pan Zhou
Computer Vision
Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
Hongbo Wang, Huaibo Huang, Jie Cao et al. (+3 more)
While recent advances in 3D generation have enabled impressive visual synthesis, existing methods often rely on 2D
diffusion supervision without explicit mechanisms for
geometric consistency, leading to spatial
hallucinations such as duplicated structures and misaligned geometry. These issues become more severe in 4D generation, where maintaining consistency across viewpoints and temporal
evolution introduces additional challenges, including jitter, identity flicker, and structural drift. We present Hallo4D, a unified and model-agnostic framework for mitigating spatiotemporal
hallucinations in 3D and 4D content generation. Hallo4D introduces a generation-detection-correction paradigm that leverages large
multimodal language models (LMMs) to identify and summarize spatial and temporal inconsistencies from multi-view and multi-frame renderings. These insights guide a consensus-driven image-space consistency optimization, where an LMM-based selector evaluates candidate corrections through multi-model voting, without requiring retraining or architectural modifications. To further improve temporal consistency and optimization efficiency, Hallo4D incorporates motion-aware keyframe sampling, LMM-guided initialization, and appearance
alignment. We additionally introduce exposure-aware optimization and visibility
pruning to enhance robustness under challenging viewpoints. Extensive experiments demonstrate that Hallo4D consistently outperforms strong baselines across diverse 3D and 4D generation settings, providing a scalable and generalizable solution for consistency-aware content generation.
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All authors Hongbo Wang, Huaibo Huang, Jie Cao, Jin Liu, Haoyang Tong, Ran He
Computer Vision
CRC-HGD: A Histopathological Image Dataset for Grading Colorectal Cancer
Elham Amjadi, Amin Bahreini, Sayed Mohammad Hasan Emami et al. (+4 more)
Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1,926,425 new cases and 904,019 deaths reported in 2022. Accurate histologic grading plays a critical role in prognosis and treatment planning for colorectal adenocarcinoma. In recent years, artificial intelligence and its subcategories, including machine learning and deep learning, have been increasingly employed for automated cancer detection and classification. An appropriate and well-organized
dataset is the essential first step to achieve this goal. This paper introduces CRC-HGD, a histopathological microscopy image
dataset of 1,914 images obtained from 214 colorectal adenocarcinoma patients (Grade I: 106, Grade II: 75, Grade III: 33). The specimens are H&E-stained colorectal tissue sections acquired at the Poursina Hakim Research Center of Isfahan University of Medical Sciences, Iran, diagnosed between 2014 and 2019, and graded according to the World Health Organization (WHO) criteria into three grades: well-differentiated (Grade I), moderately differentiated (Grade II), and poorly differentiated (Grade III). For each specimen, four magnification levels are provided: 4x, 10x, 20x, and 40x. The
dataset is accessible via Mendeley Data (https://doi.org/10.17632/yfp5sfj47m.4) and at http://databiox.com, where the latest version is also available. The distinctive feature of this
dataset is the provision of labeled specimens across all three differentiation grades at multiple magnification levels, enabling comprehensive computational analysis of colorectal cancer grading.
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All authors Elham Amjadi, Amin Bahreini, Sayed Mohammad Hasan Emami, Sayyed Mohammadreza Hakimian, Alireza Fahim, Hojjatollah Rahimi, Hamidreza Bolhasani
Computer Vision
Weakly Supervised Spatio-Temporal Candidate Discovery of Dairy Farm Sites from Seasonal Satellite Imagery
Usman Haider, Fatima Khalid and Karl Mason
Farm site discovery from satellite imagery is a spatiotemporal candidate ranking problem because farm evidence is distributed across pasture, field boundaries, roads, buildings, and seasonal vegetation patterns. Direct farm labels are often incomplete, which makes fully supervised detection difficult. This paper proposes a weakly supervised pipeline for ranking dairy farm candidate clusters from seasonal Sentinel imagery and open map priors. The method uses aligned spring, summer, and autumn image tiles from County Cork, Ireland, with spectral bands, vegetation indices, built area indices, and a pasture channel. A Barlow Twins encoder learns multi-season tile
embeddings without farm labels. In parallel, weak OpenStreetMap farm priors are split into a prior and a held-out set. Prior features support a rule-based tile score that combines farm proximity, seasonal pasture evidence, and summer greenness, while held-out features are reserved only for proxy evaluation. The rule score is smoothed over a spatial representation graph using geographic proximity and
embedding similarity, and high-scoring tiles are grouped into ranked candidate clusters. From 26,722 valid tiles, the main run selects 535 high-confidence tiles and forms 71 candidate clusters. The top 5 clusters achieve 0.60 precision within 500 m and 0.80 precision within 1000 m of held-out OpenStreetMap farm features. The top 10 clusters achieve 0.40 precision within 500 m and 0.80 precision within 1000 m. The results show that seasonal representation learning and weak geographic priors can reduce large satellite image collections into compact candidate sets for human review.
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Computer Vision
Color Pass-Through via Camera-Display Coupling
Ruikang Li, Molin Li, Jiarui Wu et al. (+3 more)
When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and contrast. This gap persists despite substantial advances in both modern cameras and displays. A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and display stages, and then connect them through low-dimensional color transforms, leading to information bottlenecks and inevitable error accumulation. To address this systemic challenge, we propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images. Our key insight is to treat the camera and display as a coupled system rather than calibrating them in isolation. Coupling the camera and display yields two practical advantages: (1) it brings the entire real-world scenes to the display via end-to-end optimization, and (2) it allows efficient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digital and human observers. Compared with representative baselines, our method achieves an average gain of +2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics, demonstrating improved reproduction of the perceived color of the original scene.
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All authors Ruikang Li, Molin Li, Jiarui Wu, Zhe Wei, Pengpeng Liu, Tianfan Xue
Computer Vision
Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification
Alaa Almouradi and Erchan Aptoula
Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training. Feature-statistics augmentation such as MixStyle, EFDMix, and correlated style uncertainty improves generalization at low cost but perturbs channel statistics globally, treating each image as a single style; one class can then contaminate the augmentation of another. Domain generalization is understudied for multi-label remote sensing; no prior method or multi-source
benchmark targets it. A label-decoupled augmentation framework is therefore proposed, confining style perturbation to label-specific regions. Per-label
attention, obtained from a learnable module or from gradient class-activation maps, yields per-label feature statistics; these statistics are mixed with cross-domain samples that share present labels, under independent per-label coefficients, and features are recomposed by
attention-weighted normalization. Three operators combined with two
attention sources produce six variants, evaluated on a leave-one-domain-out
benchmark from multi-label UCM, AID, and DFC15 over six shared labels. Averaged over three splits and five seeds, the best variant attains 71.5% mean average precision, exceeding empirical risk minimization by 5.0 points and the strongest global-statistics baseline by 1.3 points, with the largest gain on the hardest transfer (up to 7.7 points). Ablations indicate that spatial
attention and refreshed localization maps are most influential. The framework adds at most 0.35% parameters, leaves inference unchanged, and appears to offer a generic, inexpensive upgrade path for multi-label statistics-based domain generalization. Code is available upon acceptance at https://github.com/Alaa-Almouradi/Style-Augmentation-Upgrade.
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Computer Vision
Lesion Segmentation in Moderate to Severe Traumatic Brain Injury: An nnU-Net Based Approach with Adaptive Normalization in the AIMS-TBI 2025 Challenge
Inhwa Son, Gaeun Lee, Sohyeon Sim et al. (+1 more)
The
segmentation of lesions in Moderate to Severe Traumatic
Brain Injury (msTBI) from T1-weighted MRI presents a significant clinical challenge due to the profound heterogeneity of lesion characteristics in terms of size, shape, and location. To address this, the AIMS-TBI 2025 Challenge was organized to promote the development of robust and accurate
segmentation algorithms. In this paper, we present our deep learning-based solution. Our methodology employs the nnU-Net framework with an adaptive intensity normalization strategy confined to the
brain parenchyma, effectively reducing inter-subject variability and mitigating artifacts from non-
brain structures. Upon final evaluation on the held-out test set, our method demonstrated highly competitive performance on the official leaderboard, achieving an Overall Dice Coefficient of 0.6305. The model obtained a Dice score of 0.4805 for lesion
segmentation and 0.9324 for non-lesion tissue. While the lesion Dice reflects the difficulty of detecting highly heterogeneous lesions, the high non-lesion Dice primarily indicates the model's strong ability to correctly identify non-lesion voxels, demonstrating good specificity in differentiating lesion from non-lesion regions. These results demonstrate that incorporating anatomically constrained normalization within the nnU-Net pipeline is a powerful and effective strategy for tackling the complexities of msTBI lesion
segmentation.
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All authors Inhwa Son, Gaeun Lee, Sohyeon Sim, Kwang-Hyun Uhm
Computer Vision
MambaPSA: A Mamba-based Replacement for C2PSA in YOLO26
Sheng-Wei Chan, Chia-Min Lin, Hsin-Jui Pan et al. (+4 more)
State space models (SSMs), notably Mamba, have recently emerged as efficient alternatives to self-
attention with linear computational complexity. We investigate the integration of Mamba into YOLO26, the latest non-maximum suppression (NMS)-free object detection framework, by proposing MambaPSA, a lightweight Mamba-based replacement for the C2PSA block at the end of the backbone. To complement this study, we additionally insert a bidirectional
Vision Mamba (BiViM) module at the P3, P4, and P5 levels of the neck. Experiments on PASCAL VOC 2007+2012 show that MambaPSA reduces parameters by 2.9%, FLOPs by 12.1%, and improves CPU inference throughput by 17.6% (from 17 to 20 FPS) with negligible accuracy change (-0.1 mAP50:95), while the P4 BiViM placement yields the best accuracy gain (+0.9 mAP50:95). These results suggest that SSMs offer a favorable efficiency-accuracy trade-off when replacing
attention-based blocks in NMS-free lightweight detectors.
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All authors Sheng-Wei Chan, Chia-Min Lin, Hsin-Jui Pan, Ching-Yu Tsai, Chih-Hsiang Yang, Yung-Che Wang, Jen-Shiun Chiang
Computer Vision
Jiahang Wang, Yirong Yang, Yanqing Zhu et al. (+4 more)
Existing
vision-
language navigation methods often couple a VLM with waypoint decoders to produce multi-step action plans, but they typically lack an explicit closed-loop mechanism for tracking semantic progress, diagnosing execution failures, and recovering from error accumulation in long-horizon
navigation. To address this gap, we propose ReflectVLN, an agentic VLN framework that organizes decision-making through bidirectionally interactive intention and execution
agents. The intention
agent performs subtask decomposition and reflection, generating executable subtask descriptions as corrective plans. Conditioned on these descriptions, the execution
agent grounds them into short-horizon actions under current observations while monitoring sub-goal progress and detecting off-track behavior. Crucially, ReflectVLN enables closed-loop bidirectional communication: the execution
agent emits progress and deviation signals to trigger reflection and subtask updates on demand, and the intention
agent returns structured guidance that reconditions subsequent actions for recovery. To encourage temporally coherent decisions with interpretable intermediate rationales, we introduce Action Chain-of-Thought (Action-CoT), a path-conditioned dual-query training scheme for action generation. Experiments on standard VLN
benchmarks show that ReflectVLN improves success rates and path efficiency under a constrained data budget, with favorable training cost and fewer high-level intention calls at inference time, while providing interpretable intermediate decisions for analysis and collaboration. Code is available at: https://github.com/AIprogrammer/ReflectVLN
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All authors Jiahang Wang, Yirong Yang, Yanqing Zhu, Minghua Luo, Shichao Xie, Fei Liu, Mu Xu
Computer Vision
Text-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings
Yan Gong, Bohao Li, Bowen Du et al. (+1 more)
Computer-Aided Design (CAD) floor plan drawings contain both graphical primitives and textual annotations, which provide complementary
geometric and semantic cues for intelligent design understanding. Among CAD analysis tasks, panoptic symbol spotting has become increasingly important with the growing demand for industrial digitalization and deep learning-based automation. However, most existing methods remain primarily primitive-centric and underexploit textual annotations, despite their critical semantic value. Even the few text-aware approaches often treat annotations only superficially, without properly modeling complex syntax and hierarchical semantics of CAD annotations, which leads to semantic loss and suboptimal spotting performance. To address these limitations, we propose TextCAD, a
multimodal framework that jointly models graphical primitives and textual annotations for panoptic symbol spotting. Specifically, we design a Type-Attribute Correlation Encoder (TACE) to explicitly encode the compositional semantics within annotations by jointly modeling their types and attributes. We further introduce a Semantic Hierarchy
Alignment framework with Multi-level Semantic Filtering (MSF) and primitive downsampling, which adaptively aligns annotation semantics with graphical primitives at different semantic levels and enables accurate cross-modal semantic injection and fusion. Experiments on real-world building-design
datasets show that TextCAD effectively improves symbol spotting performance and achieves state-of-the-art results.
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All authors Yan Gong, Bohao Li, Bowen Du, Junchen Ye
Computer Vision
MAGE: Color-Invariant and Spatial Knowledge Distillation for Gastric Neoplasm Classification
Jiho Jun, Jeongwon Woo, Jaemin Song et al. (+6 more)
Accurate differentiation between gastric adenoma and carcinoma during endoscopy is critical for clinical decision-making. Yet, this task is highly challenging due to high inter-class similarity and ambiguous boundaries between the two classes. Existing ROI-based classification methods often suffer from detection/
segmentation error propagation and loss of surrounding global context. In contrast, full-image classification lacks the necessary spatial focus. Furthermore, we observe that deep neural networks gravitate towards domain-specific texture biases(e.g. bleeding, lighting artifacts), often causing models to predict based on spurious correlations instead of intrinsic morphological features. To address these limitations, we propose a novel framework, Masked Achromatic Guidance Expert (MAGE). During training, we introduce an auxiliary local expert branch trained on masked achromatic views of the neoplasm. By suppressing background context and color, this branch is forced to learn highly discriminative, purely structural features. We then employ a dual-objective
distillation strategy, transferring both classification logits and spatial
attention maps to provide implicit spatial supervision to the main branch that receives full WLI as input. This dual-objective
distillation forces the model to ground its predictions in morphology rather than relying on shortcuts, while still retaining clinically relevant color cues. At inference time, our deployable model operates on images without annotated masks, ensuring real-time deployability . Extensive experiments on a clinical gastric endoscopy
dataset show that our method significantly outperforms existing detection-based methodologies (e.g. YOLO) and classification-based methodologies (e.g. Swin-
Transformer), providing not only superior classification performance but also interpretable
attention maps for clinical reliability.
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All authors Jiho Jun, Jeongwon Woo, Jaemin Song, Thanh Bong Nguyen, Dong-heon Yeon, Donghoon Kang, Jae-Myung Park, Sung-Jea Ko, Kwang-Hyun Uhm
Computer Vision · Cross-listed from cs.RO
Instance-Enriched Semantic Maps for Visual Language Navigation
Jiho Hong, Eunae Kang, Sanghyun Kim et al. (+1 more)
Visual
Language Navigation (VLN) aims to enable an
embodied agent to navigate complex environments by following natural
language instructions. Recent approaches build semantic spatial maps and leverage Large
Language Models (LLMs) for
reasoning and decision making. Despite these advances, existing systems lack instance-level object detail and robustness to diverse user queries, limiting reliable
navigation in complex indoor environments. To address these limitations, we propose Instance-Enriched Semantic Maps, a unified framework with three key contributions: (1) Instance-level two-and-a-half-dimensional (2.5D) rich information mapping that constructs maps from color and depth observations via open-vocabulary panoptic
segmentation, preserving vertical distinctions and capturing small objects, while storing diverse semantic attributes and natural
language captions enriched with room-level context. (2) Robust query processing via LLM-based target selection, which dynamically routes queries across type-specialized experts and integrates their outputs through score-level fusion, enabling consistent goal selection across diverse query formulations. (3) Storage-efficient semantic representation that achieves approximately 96% reduction compared to three-dimensional (3D) scene-graph approaches while preserving sufficient spatial information for
navigation. The proposed 2.5D representation outperforms the 3D baseline by over 27% in prediction-normalized Area Under the Curve (AUC). In
navigation experiments, our method achieves over 17% improvement in object
retrieval and over 23% in
navigation success compared to the baseline across diverse query types. The project page is available at https://rcilab.github.io/iesm_vln.
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All authors Jiho Hong, Eunae Kang, Sanghyun Kim, Young-Sik Shin
Computer Vision · Cross-listed from cs.CL
KnowAct-GUIClaw: Know Deeply, Act Perfectly, Personal GUI Assistant with Self-Evolving Memory and Skill
Yunxin Li, Jinchao Li, Shibo Su et al. (+7 more)
OpenClaw has emerged as a leading
agent framework for complex task automation, yet it faces insufficient cross-platform GUI interaction support and a well-built self-
evolution mechanism. These flaws limit its adaptation to diverse device ecosystems and prevent performance improvements through continuous learning from execution experience. To resolve these issues, we propose the Know Deeply, Act Perfectly paradigm for personal assistants, which holds that accumulated user interaction and task-running experience directly improve execution accuracy and efficiency, unifying cognitive comprehension and operational execution. Based on this paradigm, we introduce KnowAct-GUIClaw, a novel Know-Route-Act-Reflect framework designed to address OpenClaw's GUI manipulation deficits and break through its cross-platform and recursive self-improvement constraints. First, the host
agent leverages accumulated interaction experience and task-relevant knowledge for long-horizon task decomposition and allocation (Know). Second, a pluggable GUI subagent with an experience-attributable
memory system (Know) and self-evolving skill library (Act), enabling seamless cross-platform migration and fast-path integration. Especially, this framework continuously stores user profiles and feedback to improve the accuracy of task decomposition and tool calls. Extensive experiments across Android, iOS, HarmonyOS and Windows show that KnowAct-GUIClaw achieves superior efficiency, accuracy and cross-platform adaptability. Especially, the GUIClaw with open-source Kimi-2.6 models achieves the best performance (64.1%) on the long-horizon MobileWorld
benchmark, beating all agentical frameworks and closed-source agentical models, e.g., Seed-2.0-Pro and GPT-5.5. Additionally, the knowledgeable
memory and execution skills supported by our framework are transferable across diverse base models, improving by 8.5% with Kimi-2.6.
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All authors Yunxin Li, Jinchao Li, Shibo Su, Zhenran Xu, Chenrui Zhao, Tongshu Bian, Xiaoman Liang, Meishan Zhang, Baotian Hu, Min Zhang
Computer Vision
Towards Vision-Free CIR: Attribute-Augmented Scoring and LLM-Based Reranking for Zero-Shot Composed Image Retrieval
Ryotaro Shimada, Yu-Chieh Lin, Yuji Nozawa et al. (+3 more)
Recent work has shown that "
Vision-Free” approaches (representing images as text) can be effective for standard image
retrieval tasks. However, it remains unclear whether this paradigm can effectively handle a more complex,
multimodal task, Composed Image
Retrieval (CIR), due to the inherent information loss in textual descriptions. In this paper, we introduce a
Vision-Free CIR framework that addresses this challenge through two key techniques: (1) Attribute-Augmented Hybrid Scoring, which compensates for lost visual details via explicit attribute matching, and (2) LLM-Based Reranking, which verifies semantic consistency of top candidates. Experiments on the open-domain CIRR
dataset show that our approach outperforms existing Zero-shot CIR methods (44.04% R@1, +8.79%). On FashionIQ, our results highlight the trade-off between semantic
reasoning and fine-grained visual matching. Ablation studies reveal that both attribute-augmented scoring and LLM-Based Reranking consistently improve performance.
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All authors Ryotaro Shimada, Yu-Chieh Lin, Yuji Nozawa, Youyang Ng, Osamu Torii, Yusuke Matsui
Computer Vision
Decouple and Reason: Anatomically Guided Two-Stage Voxel-Level Grounding of Free-Text Findings in 3D Chest CT
Kwang-Hyun Uhm, Inhwa Son and Sung-Jea Ko
Automatic voxel-level grounding of free-text findings in 3D chest Computed
Tomography (CT) is critical for clinical interpretability. However, this task remains highly challenging due to the intricate spatial complexity of large 3D volumes and the heterogeneity of free-text findings. Existing end-to-end approaches often struggle to simultaneously learn the localized feature representations required for accurate 3D
segmentation and the complex semantic understanding needed for text
alignment, leading to suboptimal grounding performance. To overcome this fundamental limitation, we propose a novel decoupled framework that disentangles the problem into two specialized stages: (1) class-agnostic lesion
segmentation and (2) text-volume
reasoning. This structural separation allows the model to first extract candidate sub-volumes by localizing potential abnormalities. Subsequently, intensive cross-modal
reasoning is performed to align these localized sub-volumes with free-text medical findings. To resolve the spatial ambiguities inherent in local regions, the
reasoning module is augmented with explicit anatomical guidance, utilizing relative spatial coordinates and lung lobe priors. Evaluated on the ReXGroundingCT
benchmark, our method achieves state-of-the-art performance in overall grounding quality on the official leaderboard. These results demonstrate that decoupling detection from
reasoning is a highly effective paradigm for handling the complexity of 3D medical visual grounding. Our code is publicly available at https://github.com/khuhm/DAGG.
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Computer Vision
WanToFight: Real-Time Generative Game Engine for Multi-Player Combat Interaction
Li Hu, Guangyuan Wang, Peng Zhang et al. (+1 more)
We present WanToFight, a generative game engine that simulates real-time, two-player The King of Fighters '97 (KOF~'97) gameplay from keyboard input. Prior generative game engines target either single-player first-person settings or non-real-time cooperative scenarios; multi-player control, real-time inference, complex physical interaction, and
adversarial gameplay have not been jointly addressed. WanToFight closes this gap with three components built on the Wan-1.3B
video diffusion transformer: a streaming autoregressive generator with block-causal
attention and a rolling KV cache; a visually grounded Player Association module that binds each player's keyboard signal to a character identity; and a gated, locally causal keyboard injection module trained with a single-player-to-full-gameplay curriculum. A four-step DMD-distilled student paired with a pruned VAE decoder sustains 30FPS at 512x384 on a single NVIDIA RTX 5090 over the duration of a complete match. To our knowledge, WanToFight is the first generative game engine to combine multi-player control, real-time inference, complex physical interaction, and
adversarial gameplay in one system.
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All authors Li Hu, Guangyuan Wang, Peng Zhang, Bang Zhang
Computer Vision · Cross-listed from eess.IV
Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function
Xiao-qiang Zhai, Zhi-feng Pang, Peng Zheng et al. (+2 more)
Medical image
segmentation is an important task in clinical analysis. Although deep learning techniques are widely used, training at the individual pixel level ignores
geometric prior information about the region being segmented. Integrating the Chan-Vese model into the loss function is a well-established remedy that accounts for the regions inside and outside the
segmentation and, through its length term, for boundary regularity. However, such losses still lack an effective characterisation of local boundary geometry. We introduce the mean
curvature as a natural
geometric constraint and propose a Deep Active Contour and Mean
Curvature (DACMC) loss function, in which a fixed convolution kernel approximates the mean
curvature at negligible computational cost. The loss has a single hyper-parameter, the
curvature weight $\lambda$, fixed at $10^{-3}$ for all experiments. We evaluate DACMC on three public
datasets - liver computed
tomography (CT), spleen
magnetic resonance imaging (MRI) and dermoscopy images from the International Skin Imaging Collaboration (ISIC) - using two encoder-decoder networks as backbones and the Dice similarity coefficient (DSC), the 95th-percentile Hausdorff distance (HD95), the Jaccard similarity (JS) and the average surface distance (ASD) as metrics, against the cross-
entropy, Dice, active contour and elastica losses. DACMC attains the best or second-best DSC in five of the six
dataset-backbone settings; on spleen MRI it reduces HD95 to 16.28 millimetres and ASD to 1.90 millimetres, and on ISIC it reduces HD95 to 7.08 millimetres. A sensitivity study shows a broad plateau for $\lambda \leq 10^{-3}$ and degeneration only when the
curvature term dominates.
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All authors Xiao-qiang Zhai, Zhi-feng Pang, Peng Zheng, Ze-wen Li, Yan-zhe Hou
Computer Vision
Traceback Translators Against Forgetting in Continual Fake Speech Detection
Enrico Gottardis, Mattia Tamiazzo and Simone Milani
Fake
speech detectors are increasingly challenged by the development of new and more accurate generative models. To cope with this problem, continual learning techniques are nowadays widely considered feasible strategies for updating models to new
datasets, but they also lead to decreased performance on previously seen samples (catastrophic forgetting). In this work, we propose a forgetting-resilient solution based on the adoption of domain translators within a frozen detector, which remaps the new feature spaces into the original ones by means of a traceback translator network. Experimental results show that this strategy enables the achievement of high detection rates with respect to traditional retraining, while minimizing the computational effort and preserving the detection accuracy on previous data.
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Computer Vision
Gaussian Mixture Modeling for Event-Aware Visual Allocation in Long Video Understanding
Yifan Lu, Ziqi Zhang, Chunfeng Yuan et al. (+3 more)
Large
Vision-
Language Models (LVLMs) face significant challenges in long
video understanding due to the excessive computational cost and information loss associated with uniform sampling. Existing keyframe selection methods often treat
video frames as atomic entities and allocate visual budgets equally, thereby overlooking high-level semantic structures and introducing substantial redundancy. To address these limitations, we propose GMM-EVA (Gaussian Mixture Modeling for Event-Aware Visual Allocation), which leverages Gaussian Mixture Models to model event-level structure from discrete frame-wise observations. A differentiated allocation strategy is then applied to preserve one primary high-resolution keyframe per event for high-fidelity detail, while utilizing lower-resolution secondary keyframes to maintain temporal context and optimize
token budgets. GMM-EVA is a training-free, plug-and-play framework that generalizes robustly across various relevance measures and downstream LVLMs. Extensive experiments on multiple long
video benchmarks demonstrate that our method significantly outperforms uniform sampling. Notably, GMM-EVA achieves comparable performance to baseline selection methods while utilizing only approximately half of the visual
token budget, highlighting its superior efficiency and effectiveness.
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All authors Yifan Lu, Ziqi Zhang, Chunfeng Yuan, Jun Gao, Bing Li, Weiming Hu
Computer Vision
CGRL: Concept-Guided Pruning and Representation Learning for Whole-Slide Image Classification
Thuc Huynh, Tuan Le and Doanh C. Bui
Weakly supervised whole-slide image (WSI) classification is widely used in computational pathology because slide-level labels are easier to obtain than dense region annotations. Existing multiple instance learning (MIL) methods often aggregate large bags of patch
embeddings using mainly visual cues, which can retain many non-informative patches and provide weak
alignment between instance features and class-level disease semantics. We propose Concept-Guided
Pruning and Representation Learning (CGRL), a simple framework that introduces class-level concept prototypes derived from disease
prompts into the MIL pipeline. First, concept-relevance
pruning ranks patch instances by their similarity to class concepts and retains the top-K concept-relevant patches for downstream MIL aggregation. Second, concept-guided contrastive representation learning constructs class-wise positive and negative patch sets from the same similarity
matrix and optimizes target-class, symmetric auxiliary, and cross-class separation objectives, thereby regularizing the projected concept space. We evaluate CGRL on TCGA-BRCA and TCGA-NSCLC using multiple representative MIL methods. Experimental results show that CGRL improves several model-
dataset combinations, with gains depending on the downstream MIL model and
dataset. It achieves particularly clear improvements in accuracy and macro-F1 while reducing computational cost through concept-relevance
pruning. These findings demonstrate that class-level semantic concepts provide an effective and practical prior for patch selection and representation learning in weakly supervised computational pathology.
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Computer Vision · Cross-listed from cs.CR
VanillaBench: The Hidden Accuracy Cost of Adversarial Robustness
Niklas Bunzel
Adversarial robustness research has produced hundreds of defended models over the past decade, yet the literature almost universally reports robustness results in isolation: standard (clean) accuracy and
adversarial accuracy of the robust model are shown, but the gap to the corresponding vanilla model is rarely quantified. We introduce VanillaBench, a systematic
benchmark that makes this gap explicit. For every adversarially-trained model catalogued by RobustBench across four threat models, we compute the accuracy difference against multiple vanilla references from Papers with Code, computed over both all entries and no-extra-data entries, the best vanilla model as of the robust model's publication year, and an architecture-matched baseline. Across all 186 robust models, the mean delta clean relative to the best vanilla model ranges from -7.7 to -29.5 percentage points, and even the single most robust model per track still trails its temporal vanilla counterpart by 4.0-21.0 points. The architecture-matched comparison, which isolates the effect of
adversarial training from architectural differences, reveals a mean gap of -3.5 to -17.5 points. Restricting this architecture-matched comparison to models whose vanilla accuracy is known for the exact same architecture, rather than approximated from a related one, narrows the gap to -4.0 to -14.0 points. These results demonstrate that the robustness-accuracy trade-off is substantially larger than what is typically conveyed by individual papers. This information is critical for practitioners and decision-makers. When deploying models in real-world settings, the accuracy cost of robustness directly affects business outcomes, yet current publications do not provide the vanilla baseline needed to assess it. We argue that future robustness evaluations should report vanilla-referenced accuracy gaps as a standard component.
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Computer Vision
Edge-Aware Thermal Infrared UAV Swarm Tracking
Yu-Hsi Chen
Thermal infrared (TIR) imaging is essential for
UAV swarm operations in visually degraded environments. However, tracking tiny
UAVs remains challenging due to limited appearance cues, frequent occlusions, and rapid maneuvers. Despite significant progress driven by
benchmarks such as the Anti-
UAV challenge, existing methods primarily prioritize accuracy while overlooking the computational constraints of real-time edge deployment. The standard Kalman Filter (KF) offers the efficiency required for edge devices, yet its constant-velocity assumption often breaks down under highly dynamic
UAV motion and thermal sensor jitter. More sophisticated nonlinear
estimators can improve robustness but often introduce additional computational costs. To address this gap, we propose an edge-aware online tracking pipeline centered on the Adaptive Kinematic Kalman Filter (AKKF), which augments the linear KF with state-dependent kinematic modeling while preserving real-time efficiency. Combined with transient false-positive suppression and kinematics-driven predictive coasting, the presented pipeline improves trajectory continuity under challenging TIR conditions. Experiments on the Beyond Strong Baseline (BSB)
benchmark provide a starting point for edge-aware
UAV tracking by jointly evaluating tracking performance and computational efficiency, offering insights toward future real-time deployment.
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Computer Vision
DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models
Francesco Taioli, Daniel Coelho, Iaroslav Melekhov et al. (+4 more)
Despite remarkable progress in text-guided image editing, generative models frequently fail to preserve visual object consistency, defined as the preservation of a subject's key attributes throughout the editing process. We address this limitation through three contributions. First, we introduce ABO-Edit, a
dataset specifically designed to study object consistency, comprising over 12,000 triplets of source images, editing
prompts, and high-quality target images rendered from artist-designed 3D assets, with multi-view coverage and human-verified quality control. Second, we uncover an overlooked property of image-editing rectified flow models: the conditioning
embedding space, not directly supervised during training, encodes a prediction of the final generated image even at high noise levels. Third, exploiting this finding, we propose FlowMirror, a parameter-free auxiliary loss that supervises this conditioning
embedding space. Without architectural changes, our method improves generation quality across several metrics over baselines.
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All authors Francesco Taioli, Daniel Coelho, Iaroslav Melekhov, Roberto Alcover-Couso, Jose Miguel Grande Saiz, Virginia Fernandez Arguedas, Artur Bekasov
Computer Vision
DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLMs
Rongxin Gao, Yuzhi Huang, Dongxuan Liu et al. (+8 more)
4D spatio-temporal
reasoning, jointly modeling 3D spatial structure and temporal
evolution, is essential for understanding dynamic worlds and enabling
embodied interaction. While current
Multimodal Large
Language Models (MLLMs) show strong capabilities in static scene understanding and coarse-grained 4D tasks, they still have notable limitations in continuous dynamic scene perception, especially in tracking dynamic object evidence for coherent 4D spatio-temporal
reasoning. This shortcoming stems mainly from relying on sparse frame-level observations, fragmenting continuous dynamic cues and leaving models unable to disentangle genuine object dynamics from camera-induced apparent motion. Inspired by humans tracking dynamic cues while compensating for viewpoint changes, we propose DynTrace, a training-free framework for 4D spatio-temporal
reasoning with two complementary components. Dynamic Trajectory Visualization (DTV) reprojects world-coordinate trajectories onto the image plane, providing geometry-informed visual priors that disentangle genuine object dynamics from camera-induced apparent motion. Meanwhile, the Dynamic Trace
Token (DT-
Token), organized into a Dynamic Trace Graph (DTG), tracks object-level dynamic cues, trace
evolution, and key moments, maintaining continuous dynamic object evidence for coherent 4D
reasoning. Together, these two components equip MLLMs with continuously tracked dynamic object evidence, grounded in geometry-informed visual priors and structured spatio-temporal traces. DynTrace consistently improves open-source MLLMs, achieving state-of-the-art results on Dyn-Bench, VLM4D, and DSI-Bench, validating the importance of tracking dynamic object evidence for robust 4D spatio-temporal
reasoning.
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All authors Rongxin Gao, Yuzhi Huang, Dongxuan Liu, Chu Li, Zhenye Wang, Jie Wu, Shuzhao Xie, Jingyan Jiang, Xinghao Ding, Xiaotong Tu, Yue Huang
Computer Vision · Cross-listed from cs.LG
Adversarial Attacks on Online Handwriting using Salience-based Temporal Editing
Yataro Tamura, Brian Kenji Iwana and Jiseok Lee
Deep learning models for online handwriting recognition have been shown effective and are increasingly deployed in practical applications. However, their vulnerability to
adversarial attacks is still a challenge. Existing
adversarial methods are predominantly designed for image-based inputs and typically rely on additive spatial perturbations. When applied to online handwriting, which is inherently represented as a time series of pen trajectories, such perturbations often introduce high-frequency jitter and visibly unnatural stroke artifacts. In this work, we propose a novel
adversarial attack framework for online handwriting recognition based on salience-guided temporal editing. Instead of adding noise, the proposed method generates
adversarial examples by inserting and deleting points at time steps selected according to temporal salience, preserving the shape and smoothness of the original handwriting. Temporal salience is estimated using gradient-based activation mapping, which guides edits toward time steps that strongly support the original class prediction. We evaluate the proposed approach on the Unipen and CASIA-OLHWDB
datasets under both white-box and one-shot black-box attack settings. Experimental results demonstrate that while conventional image-based attacks achieve strong white-box performance, they exhibit poor transferability across models. In contrast, the proposed temporal editing attack achieves stronger one-shot black-box transferability while preserving the visual structure of the handwriting. These results indicate that temporal editing is a relevant threat model for online handwriting recognition, particularly in one-shot black-box transfer settings.
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Computer Vision
TerraLogic: A Benchmark for Hierarchical Geospatial Reasoning in Earth Observation
Yuhang Yan, Linchao Mou, Bokang Yang et al. (+1 more)
Beyond perception,
reasoning is essential in remote sensing for advanced interpretation, inference, and decision-making. Recent advances in large
language models (LLMs) have enabled tool-augmented
agents that leverage external tools to perform complex analytical tasks. However, existing studies in remote sensing primarily focus on perception-oriented tasks, leaving cognitive geospatial
reasoning largely underexplored. To address this gap, we introduce TerraLogic, a
benchmark for geospatial
reasoning. TerraLogic comprises 545 scenario-driven, hierarchy-aware tasks, such as hazard vulnerability assessment, urban heat island analysis, and forest fragmentation dynamics, spanning optical, Synthetic Aperture Radar (SAR), and infrared (IR) imagery. It advances evaluation beyond recognition and monitoring toward cognitive-level geospatial analysis. To facilitate evaluation on TerraLogic, we further propose HieraPlan, a tool-augmented
agent that organizes toolkits into functional hierarchies and performs fault-tolerant
reasoning. HieraPlan enables structured abstraction, robust recovery from tool failures, and stable long-horizon planning. Extensive experiments demonstrate that current approaches struggle with hierarchical geospatial
reasoning, while HieraPlan provides a strong baseline with improved
reasoning, cross-modal generalization, and error handling. The
dataset and
agent code are publicly available at https://github.com/Ireliya/TerraLogic.
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All authors Yuhang Yan, Linchao Mou, Bokang Yang, Qingyu Li
Computer Vision
RealSkin: Spatio-Spectral Partial Neural Adjoint Maps for Image-to-3D Attribute Transfer
Jing Li, Yawei Luo, Xiangze Meng et al. (+3 more)
Creating photorealistic 3D assets requires bridging the appearance gap between real-world observations and synthetic models. A promising approach is to transfer visual attributes from real images onto synthetic 3D surfaces. Traditional methods struggle with resolution mismatch and the inherent discreteness of point correspondences. In contrast, resolution-robust functional maps enable smooth attribute propagation but rely on near-isometry assumptions and topological consistency. To address these limitations, we propose RealSkin, a self-supervised framework that performs correspondence optimization in a learned spectral domain, guided by spatial correspondences. We first introduce a spatial-guided registration
algorithm to establish coarse correspondences under severe topological discrepancies. To relax strict isometric assumptions and handle partial correspondences, we further design a spectral-aware neural adjoint network that incorporates partial correspondences into a neural function space and models non-isometric residuals for correspondence refinement. Experimental results demonstrate that our method achieves state-of-the-art performance on challenging real-to-synthetic scenarios. The code will be publicly released.
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All authors Jing Li, Yawei Luo, Xiangze Meng, Ying Li, Tieru Wu, Rui Ma
Computer Vision
Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence
Zhishan Zou, Guoyan Sun, Zhiwei Wei et al. (+4 more)
Autonomous
UAV systems increasingly rely on
multimodal large
language models (MLLMs) to operate in complex real-world environments. Such
embodied scenarios require not only understanding the surrounding space but also maintaining a coherent representation of the
agent itself. However, existing
UAV-oriented approaches and
benchmarks remain largely environment-centric, primarily focusing on spatial understanding tasks, with the
agent's self-awareness remaining implicit. To address this gap, we introduce SIS-Bench, a
benchmark for evaluating
embodied spatial intelligence in
UAV scenarios under a unified self-in-space formulation. SIS-Bench organizes evaluation along two complementary dimensions, space and self, and a three-level hierarchy of perception,
memory, and
reasoning. It contains 4,856 question–answer pairs across 13 tasks derived from 1,646 real-world
UAV videos through a task-conditioned construction pipeline with expert verification. Extensive evaluations reveal that current MLLMs exhibit fundamental limitations in modeling dynamic and
agent-centered processes. In particular, we observe a clear imbalance between spatial cognition and self-awareness, as well as a progressive performance degradation across cognitive levels. Motivated by these findings, we further explore a motion-aware representation that incorporates self-related dynamics through optical flow and visual feature fusion. Experimental results show that modeling
agent motion consistently improves perception and
memory performance, not only in spatial cognition but also in self-awareness, and generalizes to downstream
UAV decision-making tasks. Our results highlight the importance of self-awareness for advancing
embodied spatial intelligence, and provide both a new
benchmark and empirical evidence for motion-aware self-in-space modeling.
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All authors Zhishan Zou, Guoyan Sun, Zhiwei Wei, Jiancheng Pan, Yujie Li, Mugen Peng, Wenjia Xu
Computer Vision
Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification
Jeeyung Kim, Erfan Esmaeili and Qiang Qiu
When labeled data are scarce, off-the-shelf
diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approaches largely improve synthetic data by increasing realism, diversity, or domain adaptation, while overlooking a more fundamental question: how should sample usefulness for classification be measured and optimized? We address this with Class-Contrastive Influence (C2I), a criterion that quantifies a sample's usefulness through its gradient-based influence on the classifier. We find that effective samples exhibit a strong C2I gap: their loss gradients align with validation gradients from the same class and oppose those from other classes. Our analysis further suggests that such high-C2I samples are hard, boundary-proximal examples that help refine the decision boundary and improve robustness. Building on this insight, we fine-tune
diffusion models with reinforcement learning using a C2I-based reward to steer generation toward class-informative samples. Across several few-shot medical imaging
benchmarks, C2I-guided generation improves downstream accuracy and robustness over
diffusion-based augmentation baselines, showing that synthetic augmentation is most effective when guided by task usefulness rather than image quality alone.
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Computer Vision
Timing Yang, Jinrui Yang, Xinlong Li et al. (+11 more)
This work introduces a unified formulation for
vision models, where diverse forms of visual information beyond natural images, such as masks, depth maps, and other structured visual signals, are all represented as RGB images, while general visual tasks can be converted into a common RGB-to-RGB image editing problem. In this paradigm, different types of visual information internally share the same encoding and decoding architecture and parameters as natural images, enabling a single model to transfer across tasks through a unified visual interface, in a way analogous to how
language models operate over text. We refer to this formulation as RGB In and RGB Out (RINO). Built upon a generic image editing backbone without task-specific fine-tuning, RINO demonstrates robust and competitive zero-shot performance on both dense understanding tasks such as
segmentation and depth estimation (where we unify outputs as RGB), and dense-conditioned generation tasks such as pose-to-image generation (where we unify inputs as RGB). We hope this study provides useful insights toward general unified
vision-
language systems, where diverse visual tasks can be expressed, interpreted, and solved through a shared visual
language. Code is available at https://github.com/yangtiming/RINO.
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All authors Timing Yang, Jinrui Yang, Xinlong Li, Yuhan Wang, Haoran Li, Yanqing Liu, Guoyizhe Wei, Jixuan Ying, Chen Wei, Rama Chellappa, Yuyin Zhou, Cihang Xie, Alan Yuille, Feng Wang