Computer Vision
Delineating Knowledge Boundaries for Honest Large Vision-Language Models
Junru Song, Yimeng Hu, Yijing Chen et al. (+4 more)
Large
Vision-
Language Models (VLMs) have achieved remarkable
multimodal performance yet remain prone to factual
hallucinations, particularly in long-tail or specialized domains. Moreover, current models exhibit a weak capacity to refuse queries that exceed their parametric knowledge. In this paper, we propose a systematic framework to enhance the refusal capability of VLMs when facing such unknown questions. We first curate a model-specific "Visual-Idk" (Visual-I don't know)
dataset, leveraging multi-sample consistency probing to distinguish between known and unknown facts. We then align the model using supervised fine-tuning followed by preference-aware optimization (e.g., DPO, ORPO) to effectively delineate its knowledge boundaries. Results on the Visual-Idk
dataset show our method improves the Truthful Rate from 57.9% to 67.3%. Additionally, internal probing also demonstrates that the model genuinely recognizes its boundaries instead of just memorizing refusal patterns. Our framework further generalizes to out-of-distribution medical and perceptual domains, providing a robust path toward more trustworthy and prudent visual assistants.
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All authors Junru Song, Yimeng Hu, Yijing Chen, Huining Li, Qian Li, Lizhen Cui, Yuntao Du
Computer Vision
Sparsity as a Key: Unlocking New Insights from Latent Structures for Out-of-Distribution Detection
Ahyoung Oh, Wonseok Shin and Songkuk Kim
Sparse Autoencoders (SAEs) have demonstrated significant success in interpreting Large
Language Models (LLMs) by decomposing dense representations into sparse, semantic components. However, their potential for analyzing
Vision Transformers (ViTs) remains largely under-explored. In this work, we present the first application of SAEs to the ViT [CLS]
token for out-of-distribution (OOD) detection, addressing the limitation of existing methods that rely on entangled feature representations. We propose a novel framework utilizing a Top-k SAE to disentangle the dense [CLS] features into a structured latent space. Through this analysis, we reveal that in-distribution (ID) data exhibits consistent, class-specific activation patterns, which we formalize as Class Activation Profiles (CAPs). Our study uncovers a key structural invariant: while ID samples preserve a stable pattern within CAPs, OOD samples systematically disrupt this structure. Leveraging this insight, we introduce a scoring function based on the divergence of core energy profiles to quantify the deviation from ideal activation profiles. Our method achieves strong results on the FPR95 metric, critical for safety-sensitive applications across multiple
benchmarks, while also achieving competitive AUROC. Overall, our findings demonstrate that the sparse, disentangled features revealed by SAEs can serve as a powerful, interpretable tool for robust OOD detection in
vision models.
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Computer Vision
Decoupled Prototype Matching with Vision Foundation Models for Few-Shot Industrial Object Detection
Hari Prasanth S. M., Nilusha Jayawickrama and Risto Ojala
Industrial object detection systems typically rely on large annotated
datasets, which are expensive to collect and challenging to maintain in industrial scenarios where the inventory of objects changes frequently. This work addresses the challenge of few-shot object detection in such industrial scenarios, where only a limited number of labeled samples are available for newly introduced objects. We present a detection framework that leverages
vision foundation models to recognize objects with minimal supervision. The method constructs class prototypes from a small set of reference samples by extracting feature representations. For a given query scene during inference, object regions are generated using a
segmentation model, and feature
embeddings are extracted and matched with class prototypes using similarity matching. We evaluate the detection method on three established industrial
datasets from the
Benchmark for 6D Object Pose Estimation
benchmark following the official 2D object detection evaluation protocol. We demonstrate competitive detection performance, improving AP by 6.9% compared to the state-of-the-art training-free detection methods. Furthermore, the presented method is able to onboard new objects using only a few reference images, without requiring any CAD models or large annotated
datasets. These properties make the approach well-suited for real-world industrial applications.
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Computer Vision
A Multimodal Pre-trained Network for Integrated EEG-Video Seizure Detection
Tong Lu, Ke Xu, Zimo Zhang et al. (+12 more)
Reliable seizure detection in mouse models is essential for preclinical epilepsy research, yet manual review of synchronized
video-
EEG recordings is labor-intensive and single-modality systems fail for complementary reasons:
video-based methods are easily confounded by benign behaviors, whereas
EEG-based methods are vulnerable to ictal motion artifacts. We present EEGVFusion, a
multimodal framework that combines self-supervised
EEG representation learning, spatio-temporal
video encoding, optimal-transport
alignment, and bidirectional cross-
attention to integrate neural and behavioral evidence. We also curate an expert-annotated
dataset of synchronized
EEG and
video recordings comprising 93 sessions from 15 mice for training and evaluation. In the random-session split, EEGVFusion achieved a Balanced Accuracy of 0.9957 with perfect event sensitivity and an Event FAR of 0.6250 FP/h, indicating strong seizure detection performance with a low false-alarm burden. In a single held-out-subject evaluation with Subject 110 reserved for testing, EEGVFusion achieved a Balanced Accuracy of 0.9718 and reduced Event FAR from 2.7250 FP/h for the
EEG-only counterpart to 0.4833 FP/h while preserving perfect event sensitivity. Targeted ablations further showed that
EEG pre-training and OT
alignment help reduce false alarms while preserving event sensitivity.
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All authors Tong Lu, Ke Xu, Zimo Zhang, Zitong Zhao, Danwei Weng, Ruiyu Wang, Miao Liu, Zizuo Zhang, Jingyi Yao, Yixuan Zhao, Wenchao Zhang, Min Wang, Guoming Luan, Minmin Luo, Zhifeng Yue
Computer Vision
Junwon You, Mihyun Jang, Sangwoo Mo et al. (+1 more)
Vision-
language models have shown strong performance, but they often generalize poorly to specialized domains. While semi-supervised
vision-
language learning mitigates this limitation by leveraging a small set of labeled image-text pairs together with abundant unlabeled images, existing methods remain fundamentally pairwise and fail to model the global structure of
multimodal representation
manifolds. Most existing
topology-based
alignment methods rely on persistence diagram matching, which neither guarantees
geometric alignment nor uses the image-text pairing information central to
vision-
language learning. We propose
Topology-Aware Representation
Alignment for Semi-Supervised
Vision-
Language Learning (ToMA), a framework that uses persistent homology to decompose pairwise relations into $H_0$-death and $H_1$-birth relations and aligns them across modalities through available cross-modal correspondences. This lightweight decomposition captures both connectivity and cycle-closing structure without constructing 2-simplices. Experiments show clear improvements on remote sensing, while fashion results reveal strong gains in classification and particularly strong
retrieval improvements for difficult queries. Additional analysis shows that ToMA provides more consistent gains than alternative
topology-based objectives and that lightweight $H_1$-birth edges provide useful higher-order structural signals.
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All authors Junwon You, Mihyun Jang, Sangwoo Mo, Jae-Hun Jung
Computer Vision
Seamless Indoor-Outdoor Mapping for INGENIOUS First Responders
Jürgen Wohlfeil, Henry Meißner, Adrian Schischmanow et al. (+4 more)
In several applications it is desired to have 3D models not only from the outdoor spaces but also from inside the building. In the context of First Responder enhancement in large scale natural and man-made disasters, a method is presented to achieve this goal with a high degree of automation. Therefore an autonomously flying aerial mapping system is combined with a person-carried indoor positioning system. Automatically recognized markers (AprilTags) are geo-referenced by the aerial system and their coordinates are sent to the ground-based system. By looking at the AprilTags before entering the building, the ground-based system is registered to world coordinates. Without the further need of any global positioning, it creates a point cloud from the indoor spaces that fits with the point could from the aerial view. This allows a co-visualization of both point-clouds as a seamless indoor-outdoor 3D model in real time.
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All authors Jürgen Wohlfeil, Henry Meißner, Adrian Schischmanow, Thomas Kraft, Dirk Baumbach, Ines Ernst, Dennis Dahlke
Computer Vision
Beyond Fixed Formulas: Data-Driven Linear Predictor for Efficient Diffusion Models
Zhirong Shen, Rui Huang, Jiacheng Liu et al. (+6 more)
To address the high sampling cost of
Diffusion Transformers (DiTs), feature caching offers a training-free acceleration method. However, existing methods rely on hand-crafted
forecasting formulas that fail under aggressive skipping. We propose L2P (Learnable Linear Predictor), a simple data-driven caching framework that replaces fixed coefficients with learnable per-timestep weights. Rapidly trained in ~20 seconds on a single GPU, L2P accurately reconstructs current features from past trajectories. L2P significantly outperforms existing baselines: it achieves a 4.55x FLOPs reduction and 4.15x latency speedup on FLUX.1-dev, and maintains high visual fidelity under up to 7.18x acceleration on Qwen-Image models, where prior methods show noticeable quality degradation. Our results show learning linear predictors is highly effective for efficient DiT inference. Code is available at https://github.com/Aredstone/L2P-Cache.
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All authors Zhirong Shen, Rui Huang, Jiacheng Liu, Chang Zou, Peiliang Cai, Shikang Zheng, Zhengyi Shi, Liang Feng, Linfeng Zhang
Computer Vision
CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID
Fengchun Zhang, Qiang Ma, Liuyu Xiang et al. (+3 more)
Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian
retrieval model across multiple decentralized source domains such that it can generalize to unseen target environments without compromising raw data
privacy. However, this task is significantly challenged by the inherent stylistic gaps across decentralized clients. Without global supervision, models easily succumb to shortcut learning where representations overfit to domain specific camera biases rather than universal identity features. We propose CO-EVO, a novel
federated framework that resolves this semantic-style conflict through a co-evolutionary mechanism. On the semantic side, Camera-Invariant Semantic Anchoring (CSA) learns identity
prompts with cross-camera consistency to establish purified and domain-agnostic anchors that filter out local imaging noise. On the visual side, Global Style Diversification (GSD), powered by a Global Camera-Style Bank (GCSB), synthesizes realistic perturbations to expand the visual boundaries of training data. The core of CO-EVO is its co-evolutionary loop where purified anchors act as gravitational centers to guide the image encoder toward robust anatomical attributes amidst diverse style variations. Extensive experiments demonstrate that CO-EVO achieves state-of-the-art (SOTA) performance, proving that the synergy between semantic purification and style expansion is essential for robust cross-domain generalization. Our code is available at: https://github.com/NanYiyuzurn/ACL-LGPS-2026.
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All authors Fengchun Zhang, Qiang Ma, Liuyu Xiang, Jinshan Lai, Tingxuan Huang, Jianwei Hu
Computer Vision
GateMOT: Q-Gated Attention for Dense Object Tracking
Mingjin Lv, Zelin Liu, Feifei Shao et al. (+4 more)
While large models demonstrate the strong representational power of vanilla
attention, this core mechanism cannot be directly applied to Dense Object Tracking: its quadratic all-to-all interactions are computationally prohibitive for dense motion estimation on high-resolution features. This mismatch prevents Dense Object Tracking from fully leveraging
attention-based modeling in crowded and occlusion-heavy scenes. To address this challenge, we introduce GateMOT, an online tracking framework centered on Q-Gated
Attention (Q-
Attention), an efficient and spatially aware
attention variant. Our key idea is to repurpose the Query from a similarity-conditioning term into a learnable gating unit. This Gating-Query (Gating-Q) produces a probabilistic gate that modulates Key features in an element-wise manner, enabling explicit relevance selection instead of costly global aggregation. Built on this mechanism, parallel Q-
Attention heads transform one shared feature map into task-specific yet consistent representations for detection, motion, and re-identification, yielding a tightly coupled multi-task decoder with linear-complexity gating operations. GateMOT achieves state-of-the-art HOTA of 48.4, MOTA of 67.8, and IDF1 of 64.5 on BEE24, and demonstrates strong performance on additional Dense Object Tracking
benchmarks. These results show that Q-
Attention is a simple, effective, and transferable building block for
attention-based tracking in dense tracking scenarios.
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All authors Mingjin Lv, Zelin Liu, Feifei Shao, Yi-Ping Phoebe Chen, Junqing Yu, Wei Yang, Zikai Song
Computer Vision
ACPO: Anchor-Constrained Perceptual Optimization for Diffusion Models with No-Reference Quality Guidance
Yang Yang, Feifan Meng, Han Fang et al. (+1 more)
Diffusion models have achieved remarkable success in image generation, yet their training is predominantly driven by full-reference objectives that enforce pixel-wise similarity to ground-truth images.Such supervision, while effective for fidelity, may insufficient in terms of subjective visual perception quality and text-image semantic consistency. In this work, we investigate the problem of incorporating no-reference perceptual quality into
diffusion training. A key challenge is that directly optimizing perceptual signals, such as those provided by no-reference image quality assessment (NR-IQA) models, introduces a mismatch with the original
diffusion objective, leading to training instability and distributional drift during fine-tuning. To address this issue, we propose an anchor-constrained optimization framework that enables stable perceptual adaptation. Specifically, we leverage a learned NR-IQA model as a perceptual guidance signal, while introducing an anchor-based regularization that enforces consistency with the base
diffusion model in terms of noise prediction. This design effectively balances perceptual quality improvement and generative fidelity, allowing controlled adaptation toward perceptually favorable outputs without compromising the original generative behavior. Extensive experiments demonstrate that our method consistently enhances perceptual quality while preserving generation diversity and training stability, highlighting the effectiveness of anchor-constrained perceptual optimization for
diffusion models.
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All authors Yang Yang, Feifan Meng, Han Fang, Weiming Zhang
Computer Vision
Whether, Which, and Whose: Solving the Triple Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios
Lei Zhang, Zhiqing Guo, Dan Ma et al. (+2 more)
Unlike single-face forgeries, deepfakes in complex multi-person interaction scenarios (such as group photos and multi-person meetings) more closely reflect real-world threats. Although existing proactive forensics solutions demonstrate good performance, they heavily rely on a "single-face" setting, making it difficult to effectively deal with the problems of deepfake detection, localization, and source tracing in complex multi-person environments. In this paper, we propose a Deep Attributable Watermarking Framework (DAWF) tailored to multi-face proactive forensics, which establishes an isolated identity attribution space. This spatial isolation ensures that multiple independent tracing signals can coexist within a single image and be successfully anchored to their respective identity instances. Crucially, we propose a selective regional supervision loss to suppress cross-face interference, guiding the decoder to focus exclusively on the manipulated facial regions. DAWF unifies image-level detection, instance-level localization, and identity-level source tracing, successfully achieving the "whether, which, and whose" forensic goals of determining whether an image is manipulated, which face is forged, and whose identity is tampered with. Extensive experiments on challenging multi-face
datasets demonstrate robust triple-forensic performance, achieving an AUC of 0.91, an F1-score of 0.87, and a BER of 0.54%. The code is available at https://github.com/vpsg-research/DAWF.
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All authors Lei Zhang, Zhiqing Guo, Dan Ma, Wenzhong Yang, Gaobo Yang
Computer Vision
SpatialFusion: Endowing Unified Image Generation with Intrinsic 3D Geometric Awareness
Haiyi Qiu, Kaihang Pan, Jiacheng Li et al. (+3 more)
Recent unified image generation models have achieved remarkable success by employing MLLMs for semantic understanding and
diffusion backbones for image generation. However, these models remain fundamentally limited in spatially-aware tasks due to a lack of intrinsic spatial understanding and the absence of explicit
geometric guidance during generation. In this paper, we propose SpatialFusion, a novel framework that internalizes 3D
geometric awareness into unified image generation models. Specifically, we first employ a Mixture-of-
Transformers (MoT) architecture to augment the MLLM with a parallel spatial
transformer to enhance 3D
geometric modeling capability. By sharing self-
attention with the MLLM, the spatial
transformer learns to derive metric-depth maps of target images from rich semantic contexts. These explicit
geometric scaffolds are then injected into the
diffusion backbone through a specialized depth adapter, providing precise spatial constraints for spatially-coherent image generation. Through a progressive two-stage training strategy, SpatialFusion significantly enhances performance on spatially-aware
benchmarks, notably outperforming leading models such as GPT-4o. Additionally, it achieves generalized performance gains across both text-to-image generation and image editing scenarios, all while maintaining negligible inference overhead.
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All authors Haiyi Qiu, Kaihang Pan, Jiacheng Li, Juncheng Li, Siliang Tang, Yueting Zhuang
Computer Vision
Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation
Martina Pavan, Matteo Caligiuri, Francesco Barbato et al. (+1 more)
Exploiting deep learning in medical imaging faces critical challenges, including strict
privacy constraints, heterogeneous imaging devices with varying acquisition properties, and class imbalance due to the uneven prevalence of pathologies. In this work, we propose FedSSG, a novel
Federated Learning framework that addresses domain shifts caused by diverse imaging devices while mitigating the under-representation of rare pathologies. The key contribution is a strategy for generating synthetic samples and distributing them across clients to improve coverage of both underrepresented pathologies and imaging devices. Experimental results demonstrate that our approach significantly enhances model performance and generalization across heterogeneous institutions, with minimal computational overhead at the client side.
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All authors Martina Pavan, Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh
Computer Vision
Motion-Driven Multi-Object Tracking of Model Organisms in Space Science Experiments
Jianing You, Han Wang, Kang Liu et al. (+4 more)
Automated animal behavior analysis relies on long-term, interpretable individual trajectories; however, multi-animal tracking in space science experimental
videos remains highly challenging due to weak appearance cues, low-quality imaging, complex maneuvering behaviors, and frequent interactions. To address this problem, we first construct the SpaceAnimal-MOT
dataset to characterize the motion complexity and long-term identity preservation challenges in biological
videos acquired under microgravity conditions. We then propose ART-Track (Adaptive Robust Tracking), a motion-driven tracking framework tailored to this setting. Specifically, multi-model motion estimation is introduced to handle abrupt maneuvers and nonlinear motion, motion-state-driven association is designed to reduce identity switches under dense interactions and temporary mismatch, and uncertainty-adaptive fusion is used to dynamically balance spatial and motion cues when prediction reliability varies. Experimental results show that ART-Track significantly reduces identity switches on zebrafish and fruitfly sequences, while maintaining more stable association under occlusion, deformation, and high-density interactions, thereby providing a more reliable tracking foundation for downstream quantitative behavior analysis. The code is publicly available at https://github.com/yyy7777777/ART_TRACK/tree/main.
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All authors Jianing You, Han Wang, Kang Liu, Jiale Ding, Fengjie Chu, Zihan Guo, Shengyang Li
Computer Vision
Point Cloud Registration via Probabilistic Self-Update Local Correspondence and Line Vector Sets
Kuo-Liang Chung, Yu-Cheng Lin and Wu-Chi Chen
Point cloud registration (PCR) is a fundamental task for integrating 3D observations in remote sensing applications. This paper proposes a fast and effective PCR
algorithm utilizing probabilistic self-updating local correspondence and line vector sets. Our dual RANSAC interaction model comprises a global RANSAC evaluating the global correspondence set and a local RANSAC operating on dynamically updated local sets. Initially, these local sets are constructed using angle histogram statistics and line vector length preservation techniques. To improve accuracy, a probabilistic self-updating strategy refines the local sets after each interaction round. To reduce runtime, we introduce a global early termination condition that optimally balances accuracy and efficiency. Finally, a weighted singular value decomposition estimates the registration solution. Evaluations on public
datasets demonstrate our
algorithm achieves superior time efficiency and at least a 10% root mean square error improvement over state-of-the-art methods. The C++ source code is publicly available at https://github.com/ivpml84079/Probabilistic-Self-Update-Line-Vector-Set-Based-Point-Cloud-Registration.
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Computer Vision
The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches
Vishesh Kumar and Akshay Agarwal
The vulnerabilities of deep neural networks against singularities have raised serious concerns regarding their deployment in the physical world. One of the most prominent and impactful physical-world
adversarial perturbations is the attachment of patches to clean images, known as an
adversarial patch attack. Similarly, natural noises such as Gaussian and Salt&Pepper are highly prevalent in the real world. The current research need arises from the above vulnerabilities and the lack of efforts to tackle these two singularities independently and, especially, in combination. In this research, we have, for the first time, combined these two prominent singularities and proposed a novel
dataset. Using this
dataset, we have conducted a
benchmark study of singularity data-point detection using features from several convolutional neural networks. For classification, rather than the popular neural network-based parameter tuning, we have used traditional yet effective machine learning classifiers. The extensive experiments across various in- and out-of-distribution (OOD) singularities reveal several interesting findings about the effectiveness of classifiers and show that it is hard to defend against adversaries when they are treated independently, and inefficient classifiers are selected.
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Computer Vision
CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation
Sonali Sharma, Jin Long, George Shih et al. (+7 more)
Chest X-ray interpretation is one of the most frequently performed diagnostic tasks in medicine and a primary target for AI development, yet current
vision-
language models are primarily trained on
datasets of paired images and reports, not the cognitive processes and visual
attention that underlie clinical
reasoning. Here, we present CheXthought, a global,
multimodal resource containing 103,592 chain-of-thought
reasoning traces and 6,609,082 synchronized visual
attention annotations across 50,312 multi-read chest X-rays from 501 radiologists in 71 countries. Our analysis reveals clinical
reasoning patterns in how experts deploy distinct visual search strategies, integrate clinical context, and communicate uncertainty. We demonstrate the clinical utility of CheXthought across four dimensions. First, CheXthought
reasoning significantly outperforms state-of-the-art
vision-
language model chain-of-thought in factual accuracy and spatial grounding. Second, visual
attention data used as an inference-time hint recovers missed findings and significantly reduces
hallucinations. Third,
vision-
language models trained on CheXthought data achieve significantly stronger pathology classification, visual faithfulness, temporal
reasoning and uncertainty communication. Fourth, leveraging CheXthought's multi-reader annotations, we predict both human-human and human-AI disagreement directly from an image, enabling transparent communication of case difficulty, uncertainty and model reliability. These findings establish CheXthought as a resource for advancing
multimodal clinical
reasoning and the development of more transparent, interpretable
vision-
language models.
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All authors Sonali Sharma, Jin Long, George Shih, Sarah Eid, Christian Bluethgen, Francine L. Jacobson, Emily B. Tsai, Global Radiology Consortium, Ahmed M. Alaa, Curtis P. Langlotz
Computer Vision
Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach
Nicolas Mastropasqua, Ignacio Bugueno-Cordova, Rodrigo Verschae et al. (+2 more)
Face liveness detection has been extensively studied using RGB cameras, achieving strong performance under controlled conditions but often failing to generalize across sensors and attack scenarios. In this work, we explore event cameras as an alternative sensing modality for liveness detection based on temporal ocular dynamics. Event cameras capture sparse, asynchronous changes in brightness with microsecond resolution, enabling precise analysis of fast eye movements such as saccades. Replay attacks cannot faithfully reproduce these dynamics due to temporal resampling and display artifacts, leading to distinctive spatio-temporal patterns in the event domain. We design a data collection protocol to extend RGBE-Gaze with replay-attack recordings, yielding an event-based fake counterpart for liveness detection. We analyze event-driven temporal features from eye regions and evaluate their effectiveness for ocular motion
segmentation and liveness classification. Our results show that event-based representations enable reliable discrimination between genuine and replayed sequences, achieving up to 95.37% top-1 accuracy with a spiking convolutional neural network. These preliminary findings highlight the potential of event-based sensing for robust and low-latency liveness detection.
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All authors Nicolas Mastropasqua, Ignacio Bugueno-Cordova, Rodrigo Verschae, Daniel Acevedo, Pablo Negri
Computer Vision
MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
Chunzheng Zhu, Jiaqi Zeng, Junyu Jiang et al. (+2 more)
High-precision medical diagnosis relies not only on static imaging features but also on the implicit diagnostic
memory experts instantly invoke during image interpretation. We pinpoint a fundamental cognitive misalignment in medical VLMs caused by discrete tokenization, leading to quantization loss, long-range information dissipation, and missing case-adaptive expertise. To bridge this gap, we propose ours, a framework for latent diagnostic
memory evolution that simulates the experiential invocation of clinicians by dynamically synthesizing implicit diagnostic
memories within the model's hidden stream. Specifically, it begins with a Meta Query for Prior Memorization mechanism, where learnable probes retrieve structured priors from an anatomical prior encoder to generate condensed implicit
memories. To ensure clinical fidelity, we introduce Causal Counterfactual Refinement (CCR), which leverages reinforcement learning and counterfactual rewards derived from region-level feature masking to quantify the causal contribution of each
memory, thereby
pruning redundancies and aligning latent representations with diagnostic logic. This evolutionary process culminates in Intrinsic
Memory Transition (IMT), a privileged-autonomous dual-branch paradigm that internalizes teacher-branch diagnostic patterns into the student-branch via full-vocabulary divergence
alignment. Comprehensive empirical evaluations across multiple
datasets demonstrate that ours, by transferring external expertise into endogenous parameters, significantly outperforms existing state-of-the-art methods, particularly chain-of-thought paradigms, in diagnostic accuracy. The code is available at https://github.com/zhcz328/MedSynapse-V.
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All authors Chunzheng Zhu, Jiaqi Zeng, Junyu Jiang, Jianxin Lin, Yijun Wang
Computer Vision
High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification
Boxiang Yang, Ning Chen, Xia Yue et al. (+6 more)
Recently, Hyperspectral Image (HSI) classification has attracted increasing
attention in remote sensing. However, HSI data are inherently high-dimensional but low-rank, with discriminative information concentrated on a low-dimensional latent
manifold. In real-world remote sensing scenarios, the superposition of multiple degradation factors disrupts this intrinsic
manifold structure, driving samples away from their original low-dimensional distribution and introducing substantial redundant and non-discriminative variations. To better handle this challenge, this paper proposes a
manifold-space
diffusion framework (MSDiff) for robust hyperspectral classification under complex degradation conditions. Specifically, the proposed method first maps high-dimensional, degradation-affected HSI data into a compact low-dimensional
manifold through a discriminative spectral-spatial reconstruction task, preserving class semantics and reducing redundant variations. A
diffusion-based generative model is then applied to regularize the spectral-spatial distribution within the
manifold, enabling progressive refinement and stabilization of latent features against residual degradations. The key advantage of the proposed framework lies in performing
diffusion-based distribution modeling directly on the low-dimensional
manifold, effectively decoupling degradation-induced disturbances from intrinsic discriminative structures and enhancing representation stability under complex degradations. Experimental results on multiple hyperspectral
benchmarks demonstrate consistent performance improvements over state-of-the-art methods under diverse composite degradation settings. The code will be available at https://github.com/yangboxiang1207/MSDiff
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All authors Boxiang Yang, Ning Chen, Xia Yue, Yichang Luo, Yingbo Fan, Haoyuan Zhang, Haoyu Ma, Jun Yue, Shanjun Mao
Computer Vision
Semantic Foam: Unifying Spatial and Semantic Scene Decomposition
Amr Sharafeldin, Shrisudhan Govindarajan, Thomas Walker et al. (+4 more)
Modern scene reconstruction methods, such as 3D Gaussian Splatting, deliver photo-realistic novel view synthesis at real-time speeds, yet their adoption in interactive graphics applications has been limited. A major bottleneck is the difficulty of interacting with these representations compared to traditional, human-authored 3D assets. While previous research has attempted to impose semantic decomposition on these models, significant challenges remain regarding
segmentation quality and consistency. To address this, we introduce Semantic Foam, extending the recently proposed Radiant Foam representations to semantic decomposition tasks. Our approach integrates the natural spatial volumetric decomposition of Radiant Foam's Voronoi mesh with an explicit semantic feature field parameterized at the
cell level. This explicit structure enables direct spatial regularization, which prevents artifacts caused by occlusion or inconsistent supervision across views - common pitfalls for other point-based representations. Experimental results show that our method achieves comparable or superior object-level
segmentation performance compared to state-of-the-art methods like Gaussian Grouping and SAGA.
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All authors Amr Sharafeldin, Shrisudhan Govindarajan, Thomas Walker, Aryan Mikaeili, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi
Computer Vision
Multiple Consistent 2D-3D Mappings for Robust Zero-Shot 3D Visual Grounding
Yufei Yin, Jie Zheng, Qianke Meng et al. (+7 more)
Zero-shot 3D Visual Grounding (3DVG) is a critical capability for open-world
embodied AI. However, existing methods are fundamentally bottlenecked by the poor quality of open-vocabulary 3D proposals, suffering from inaccurate categories and imprecise geometries, as well as the spatial redundancy of exhaustive multi-view
reasoning. To address these challenges, we propose MCM-VG, a novel framework that achieves robust zero-shot 3DVG by explicitly establishing Multiple Consistent 2D-3D Mappings. Instead of passively relying on noisy 3D segments, MCM-VG enforces 2D-3D consistency across three fundamental dimensions to achieve precise target localization and reliable
reasoning. First, a Semantic
Alignment module corrects category mismatches via LLM-driven query parsing and coarse-to-fine 2D-3D matching. Second, an Instance Rectification module leverages VLM-guided 2D
segmentations to reconstruct missing targets, back-projecting these reliable visual priors to establish accurate 3D geometries. Finally, to eliminate spatial redundancy, a Viewpoint
Distillation module clusters 3D camera directions to extract optimal frames. By pairing these optimal RGB frames with Bird's Eye View maps into concise visual
prompt sets, we formulate the final target disambiguation as a multiple-choice
reasoning task for
Vision-
Language Models. Extensive evaluations on ScanRefer and Nr3D
benchmarks demonstrate that MCM-VG sets a new state-of-the-art for zero-shot 3D visual grounding. Remarkably, it achieves 62.0% and 53.6% in Acc@0.25 and Acc@0.5 on ScanRefer, outperforming previous baselines by substantial margins of 6.4% and 4.0%.
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All authors Yufei Yin, Jie Zheng, Qianke Meng, Zhou Yu, Minghao Chen, Jiajun Ding, Min Tan, Yuling Xi, Zhiwen Chen, Chengfei Lv
Computer Vision
GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition
Yuqi Li, Qian Zhou, Huiran Duan et al. (+5 more)
Gait recognition is an attractive biometric modality for long-range and contact-free identification, but high-performing gait models often rely on deep and computationally expensive architectures that are difficult to deploy in practice. Knowledge
distillation (KD) offers a natural way to transfer knowledge from a powerful teacher to an efficient student; however, standard KD is often less effective for part-structured gait models, where supervision is formed from both part-wise classification logits and part-wise
retrieval embeddings. In this paper, we propose GaitKD, a
distillation framework that decouples gait knowledge transfer into two complementary components: decision-level
distillation and boundary-level
distillation. Specifically, GaitKD aligns the teacher and student through part-calibrated logit
distillation to transfer inter-class decision relations, while preserving the teacher-induced partitioning of the
embedding space through an activation-boundary objective instead of direct feature regression. With a simple aligned part-wise design, GaitKD supports heterogeneous teacher-student gait models without introducing additional inference cost. Experimental results across multiple gait recognition
benchmarks and teacher-student configurations show consistent improvements over strong gait baselines. Our study demonstrates that the two transfer components are complementary, and boundary-preserving
distillation provides more stable performance than direct feature regression. Source code is available at https://github.com/liyiersan/GaitKD/
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All authors Yuqi Li, Qian Zhou, Huiran Duan, Jingjie Wang, Shunli Zhang, Chuanguang Yang, Guoying Zhao, Yingli Tian
Computer Vision
OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction
Liliang Ye, Guiyi Zeng, Yunyao Zhang et al. (+3 more)
Predicting social media popularity requires understanding both the intrinsic appeal of content and the external context that determines how it is exposed to users. Existing methods focus on content signals but do not separate them from exposure-related patterns, which causes the learned representations to absorb platform-specific visibility effects and weakens both interpretability and cross-platform transfer. This paper introduces OmniTrend, a unified framework that models popularity as the joint outcome of content attractiveness and contextual exposure. The content module learns cross-modal representations from visual, audio, and textual cues to quantify intrinsic appeal, while the context module estimates exposure from exogenous signals such as posting time, author activity, topical trends, and
retrieval-based neighborhood statistics. OmniTrend learns separate predictors for content attractiveness and contextual exposure and integrates them in the final popularity estimate, which makes the role of each factor explicit and supports robust transfer across image and
video platforms.
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All authors Liliang Ye, Guiyi Zeng, Yunyao Zhang, Yi-Ping Phoebe Chen, Junqing Yu, Zikai Song
Computer Vision
Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models
Hao Wen and Jingsu Kang
We report our multi-stage framework designed for the problem of multi-class bi-atrial
segmentation from 3D late gadolinium-enhanced (LGE) MRI of the human heart. The pipeline consists of a preprocessing step using multidimensional contrast limited adaptive histogram equalization (MCLAHE); coarse region
segmentation from MCLAHE-enhanced and down-sampled MRI using a V-Net family model; and fine
segmentation from the coarse region using another V-Net model. Asymmetric loss is adopted to optimize the model weights.
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Computer Vision
Beyond Shortcuts: Mitigating Visual Illusions in Frozen VLMs via Qualitative Reasoning
Hao Guo, Fei Wang, Junjie Chen et al. (+4 more)
While
Vision-
Language Models (VLMs) have achieved state-of-the-art performance in general visual tasks, their perceptual robustness remains remarkably brittle when confronted with optical illusions. These failures are often attributed to shortcut heuristics, where models prioritize linguistic priors and memorized prototypes over direct visual evidence. In this work, we propose Structured Qualitative Inference (SQI), a training-free, data-centric framework designed to fortify visual grounding in frozen VLMs. SQI addresses perceptual anomalies through three systematic modules: (1) Axiomatic Constraint Injection, which suppresses erroneous metric estimations and quantitative
hallucinations; (2) Hierarchical Scene Decomposition, which decouples target visual
manifolds from complex background distractors; and (3) Counterfactual Self-Verification, an
adversarial reasoning step that mitigates confirmation bias. By orchestrating these qualitative constraints at inference time, SQI effectively aligns high-level linguistic
reasoning with low-level visual perception. Our framework was evaluated on the DataCV 2026 Challenge (Task I: Classic Illusion Understanding), where it ranked 2nd place overall. Experimental results demonstrate that SQI not only significantly enhances accuracy across diverse illusion categories but also provides superior diagnostic interpretability without any model fine-tuning. Our success underscores the potential of structured qualitative grounding as a robust paradigm for developing next-generation, illusion-resistant
vision-
language systems.
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All authors Hao Guo, Fei Wang, Junjie Chen, Yiqi Nie, Jiaqi Zhao, Qiankun Li, Subin Huang
Computer Vision
MetaSR: Content-Adaptive Metadata Orchestration for Generative Super-Resolution
Jiaqi Guo, Mingzhen Li, Haohong Wang et al. (+1 more)
We study generative super-resolution (SR) in real-world scenarios where content and degradations vary across domains, genres, and segments. For example, images and
videos may alternate between text overlays, fast motion, smooth cartoons, and low-light faces, each benefiting from different forms of side information. Existing metadata-guided SR methods typically use a fixed conditioning design, which is suboptimal when useful cues are content dependent and transmission budgets are limited. We propose MetaSR, a
Diffusion Transformer (DiT)-based framework that selects and injects task-relevant metadata to guide SR under resource constraints. Specifically, we use the DiT's own VAE and
transformer backbone to fuse heterogeneous metadata, and adopt an efficient
distillation strategy that enables one-step
diffusion inference. Experiments across diverse content buckets and degradation regimes show that MetaSR outperforms reference solutions by up to 1.0~dB PSNR while achieving up to 50% transmission bitrate saving at matched quality. We assess these gains under a rate–distortion optimization (RDO) framework that jointly accounts for sender-side bitrate and receiver/display quality metrics (e.g., PSNR and SSIM).
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All authors Jiaqi Guo, Mingzhen Li, Haohong Wang, Aggelos K. Katsaggelos
Computer Vision
Camera-RFID Fusion for Robust Asset Tracking in Forested Environments
John Hateley, Sriram Narasimhan and Omid Abari
Passive RFID tags offer a cost-effective and scalable solution for tracking numerous deployed assets. However, in forested environments, signal attenuation and multipath effects generally limit RFID spatial accuracy to the meter level. Conversely, while cameras employing stereo
vision can achieve centimeter-level precision, relying solely on computer
vision fails to resolve issues arising from spatial association ambiguity and partial occlusions in dense settings. Fusing these modalities allows systems to harness the high-accuracy benefits of
vision while retaining the robust, non-line-of-sight identification advantages of RFID. Yet, a primary challenge in achieving this, which is the central focus of this paper, lies in accurately associating the disparate trajectories generated by these two sensors. To overcome this limitation, we introduce a novel camera–RFID fusion framework that integrates depth and object information with advanced trajectory-matching
algorithms. By successfully bridging the meter-to-centimeter accuracy gap, the proposed approach helps achieve reliable tag localization even when assets temporarily leave the camera's field of view. To the best of our knowledge, this represents the first application of camera–RFID fusion for asset tracking in natural forested environments.
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Computer Vision
EnerGS: Energy-Based Gaussian Splatting with Partial Geometric Priors
Rui Song, Tianhui Cai, Markus Gross et al. (+5 more)
3D Gaussian Splatting (3DGS) has been widely adopted for scene reconstruction, where training inherently constitutes a highly coupled and non-convex optimization problem. Recent works commonly incorporate
geometric priors, such as
LiDAR measurements, either for initialization or as training constraints, with the goal of improving photometric reconstruction quality. However, in large-scale outdoor scenarios, such
geometric supervision is often spatially incomplete and uneven, which limits its effectiveness as a reliable prior and can even be detrimental to the final reconstruction. To address this challenge, we model partially observable geometry as a continuous energy field induced by
geometric evidence and propose EnerGS. Rather than enforcing geometry as a hard constraint, EnerGS provides a soft
geometric guidance for the optimization of Gaussian primitives, allowing
geometric information to steer the optimization process without directly restricting the solution space. Extensive experiments on large-scale outdoor scenes demonstrate that, under both sparse multi-view and
monocular settings, EnerGS consistently improves photometric quality and
geometric stability, while effectively mitigating overfitting during 3DGS training.
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All authors Rui Song, Tianhui Cai, Markus Gross, Yun Zhang, Walter Zimmer, Zhiyu Huang, Olaf Wysocki, Jiaqi Ma
Computer Vision
DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation
Junhu Fu, Ke Chen, Weidong Guo et al. (+9 more)
Controllable medical
video generation has achieved remarkable progress, but it still lacks interpretability, which requires the
alignment of generated contents with physical priors and faithful clinical manifestations. To push the boundaries from mere controllability to interpretability, we propose DepthPilot, the first interpretable framework for colonoscopy
video generation. This work takes a step toward trustworthy generation through two synergistic paradigms. To achieve explicit
geometric grounding, DepthPilot devises a prior distribution
alignment strategy, injecting depth constraints into the
diffusion backbone via parameter-efficient fine-tuning to ensure anatomical fidelity. To enhance intrinsic nonlinear modeling under these
geometric constraints, DepthPilot employs an adaptive spline denoising module, replacing fixed linear weights with learnable spline functions to capture complex spatio-temporal dynamics. Extensive evaluations across three public
datasets and in-house clinical data confirm DepthPilot's robust ability to produce physically consistent
videos. It achieves FID scores below 15 across all
benchmarks and ranks first in clinician assessments, bridging the gap between "visually realistic" and "clinically interpretable". Moreover, DepthPilot-generated
videos are expected to enable reliable 3D reconstruction, facilitating
surgical navigation and blind region identification, and serve as a foundation toward the colorectal world model.
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All authors Junhu Fu, Ke Chen, Weidong Guo, Shuyu Liang, Jie Xu, Chen Ma, Kehao Wang, Shengli Lin, Zeju Li, Yuanyuan Wang, Yi Guo, Shuo Li