Computer Vision
EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits
Kaifan Zhang, Lihuo He, Yuqi Ji et al. (+4 more)
Recent
EEG-to-image
retrieval models have achieved strong performance in identifying viewed images from semantically diverse candidates. Yet such success does not reveal what visual information supports the match. A model may readily identify a cheetah among tools, plants, and vehicles, but can it still distinguish the viewed cheetah from the same scene with the cheetah replaced by a dog? Motivated by this question, we introduce
EEG-EditBench, a diagnostic
benchmark that examines this question through controlled edits of object identity, attributes, background, and object presence. Built from the 200 THINGS-
EEG2 test images,
EEG-EditBench contains 2,137 quality-controlled edits and evaluates eight representative
EEG visual decoding models. Our results show that strong standard
retrieval does not consistently transfer to edit-based evaluation, with fine-grained attribute changes presenting the greatest challenge.
EEG-EditBench reveals model behavior hidden by aggregate
retrieval accuracy and provides a controlled basis for studying what visual information
EEG-image models preserve. The code and complete
dataset are publicly available.
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All authors Kaifan Zhang, Lihuo He, Yuqi Ji, Junjie Ke, Lukun Wu, Tianhao You, Xinbo Gao
Computer Vision
Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation
Jinghong Liu, Yuchuan Deng, Fanping Liu et al. (+2 more)
Few-shot medical image
segmentation (FS-MIS) aims to segment novel regions of interest (ROIs) from a few annotated support examples. Despite rapid progress, existing FS-MIS solutions span diverse paradigms but are evaluated under inconsistent settings, leaving their relative effectiveness unclear. We introduce FAME, a unified
benchmark for evaluating FS-MIS solutions, covering specialists, SAM-based methods, CLIP-based methods, and MLLM-based methods. FAME contains 14,958 test samples across 7 anatomical sites, 9 imaging modalities, and 14 ROI categories, and evaluates models under zero-shot and ten-shot settings with additional assessment of target-absence recognition and generalization under
covariate and semantic shifts. Our evaluation reveals several findings. First, effective few-shot
segmentation depends on how models exploit support examples: direct visual adaptation generally outperforms
prompt-based strategies. Second, increasing support examples improves performance only when models can effectively utilize them. Third, semantic transfer remains substantially more challenging than imaging-domain adaptation, and strong localization ability does not necessarily imply reliable target-absence recognition. We hope FAME provides a comprehensive understanding of current FS-MIS solutions and facilitates the development of more effective and reliable few-shot medical
segmentation methods.
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All authors Jinghong Liu, Yuchuan Deng, Fanping Liu, Meng Huang, Xirong Li
Computer Vision · Cross-listed from cs.AI
Simplifying Neural Networks During Training
Lorenzo Sciandra, Samuele Fonio and Roberto Esposito
Understanding and exploiting the training dynamics of overparameterized deep neural networks remains a central challenge in modern machine learning. Recent evidence on Neural Collapse (NC) shows that class representations and classifiers exhibit highly structured geometry, while the Tunnel Effect suggests that only a subset of layers is essential for feature extraction. We combine these two perspectives and propose an NC-inspired training framework for simplifying deep networks during training. Our method monitors representation dynamics through the Inverse Fisher Criterion, a stable and efficient proxy for the variability collapse behavior, to identify both the split point between feature extraction and classification and the training stage at which simplification becomes viable. We then replace the trailing layers with a lightweight classification head and continue training the reduced model. Experiments on image-classification
benchmarks across MLP, VGG, and ResNet architectures show that the proposed method achieves substantial parameter reductions while maintaining accuracy comparable to that of the full model. Code to reproduce the experiments can be found at: https://github.com/LorenzoSciandra/NNS.
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Computer Vision
VCP-DCN: Beyond Visual Concealed Property via Depth Collaborative Network for Camouflaged Object Detection
Songsong Duan, Xi Yang and Nannan Wang
Camouflaged Object Detection (COD) aims to identify and segment camouflaged objects in complex environments, which are often concealed because their color and texture are similar to the background. Several existing COD methods introduce depth maps to boost detection performance via learning complementary RGB-D features, ignoring modality-specific characteristics of concealed objects in the depth domain. To address this issue, we propose a depth collaborative network, called VCP-DCN, to mine distinguishable multi-modality features beyond visual concealed prototype in depth domain. Specifically, VCP-DCN progressively performs multi-modality
alignment, interaction, and fusion for the COD task. In the
alignment stage, we propose a Separable Prototype
Embedding (SPE) module to learn modality-consistency and modality-specific RGB/depth prototype
tokens through prototype contrastive learning. Furthermore, we develop a Multi-modality Dual
Attention (MDA) module to enhance the cross-modal feature representation through local response maps between modality-consistency RGB/depth prototype
tokens and visual
tokens on the interaction stage. Finally, we design a Depth Adaptive Injection (DAI) module to adaptively measure contribution of RGB/depth features with a decision-making mechanism, which calculates similarity distance between RGB/depth modality-specific prototype
tokens and modality-consistency ones on the fusion stage. Extensive experiments demonstrate the effectiveness of our VCP-DCN on three authoritative
datasets.
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Computer Vision
FeatFix: Reuse What You Verify through Local Exact-Feature Correction for Faster Cached Diffusion Inference
Hanshuai Cui, Zhiqing Tang, Zhi Yao et al. (+3 more)
Diffusion models are widely used to generate high-quality images and
videos, but their iterative denoising process remains computationally intensive. A growing class of training-free accelerators reduces this cost by reusing cached intermediate features or
forecasting future ones. To control draft drift, these methods sometimes compute an exact block feature for verification. Yet the resulting exact feature is typically used only to measure discrepancy or guide a later decision and is then discarded. We find that this previously computed feature can instead be reused for correction. Forwarding it at the verification site resets the local draft residual and reduces downstream feature error. Based on this observation, we introduce FeatFix, a local exact-feature correction method for cached
diffusion inference. FeatFix operates at a fixed sparse set of layer–timestep sites. At each selected site, it replaces the complete draft block output with the exact output computed from the same incoming state, avoiding
token- or channel-level partial replacement and full-timestep recomputation. Experiments across four image and
video backbones show that FeatFix consistently accelerates generation, achieving a speedup of up to $6.70\times$ over Vanilla while maintaining competitive output quality.
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All authors Hanshuai Cui, Zhiqing Tang, Zhi Yao, Qianli Ma, Fanshuai Meng, Weijia Jia
Computer Vision
Harshit Mittal and Arash Rabbani
Accurate
cell segmentation and classification are foundational to digital pathology, enabling quantitative tissue analysis for diagnosis and treatment planning. Encoder-decoder architectures that fuse multi-scale features through skip connections have become the dominant paradigm for this task, yet standard direct skip connections treat every spatial location equally, which leads to redundant and potentially conflicting information reaching the decoder. To overcome this problem, various gating mechanisms have been introduced, but most of them operate solely on filtering encoder information, neglecting the benefit of global contextual information from the decoder. This study proposes replacing conventional skip connections in a CellViT-based model with a novel Spatial
Attention Fusion (SAF) Gating module. Each SAF gate concatenates the encoder skip and upsampled decoder features, compresses them through two pointwise convolutions with an intermediate ReLU, and applies a channel-wise softmax to produce a per-pixel "heatmap of trust" that sums to unity at every spatial location, allowing the network to learn where each source is most trustworthy. The resulting fused features improve the model's ability to detect the minority "Dead" class, which in turn enhances the multi-class panoptic quality (mPQ) on the PanNuke
dataset. SAF Gating is compared against six gating alternatives including no gating,
attention gates, squeeze-and-excitation, CBAM, cross-
attention, and attentional feature fusion on PanNuke and MoNuSeg
datasets. SAF Gating achieves the highest mPQ (0.471), a gain driven primarily by a 14.5-point improvement in Dead-class F1 score compared to ungated CellViT baseline.
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Computer Vision
Thinking Once Is Enough: Intermediate-Layer Evidence Routing for High-Resolution VQA
Zhongkuan Mao, Xianjie Liu, Tianyu Meng et al. (+9 more)
High-resolution visual question answering (HR-VQA) is often treated as a problem of insufficient evidence acquisition, where failing
multimodal large
language models must inspect images again through cropping, re-encoding, or multi-round search. We show that this view is incomplete: in many cases, fine-grained evidence has already survived visual encoding and become identifiable and influential within an intermediate-layer routing window, but is later diluted before answer generation. We propose Thinking-Once, a training-free, single-visual-pass evidence-routing method that reconstructs question-conditioned
attention at this window, preserves core entity
tokens and compact background context, and routes this evidence to later layers without extra visual encoding. Across five base models, Thinking-Once consistently improves or matches the corresponding base setting, increasing the average scores on V$^*$Bench, HRBench-4K, and HRBench-8K by +3.1, +3.0, and +2.7 points while reducing the average peak
memory by about 4,GB. On Qwen2.5-VL-7B, it improves the three
benchmarks by +9.9, +4.6, and +5.5 points, raising the cross-
benchmark mean from 72.5 to 79.1. With the ZwZ-8B base model, Thinking-Once reaches a mean score of 82.7. Against 11 open-source HR-VQA baselines, it obtains the best or tied-best score on all three
benchmark averages and the best overall mean; for example, compared with DeepScan, it reduces V$^*$Bench inference time by 97.2% while improving the cross-
benchmark mean from 77.8 to 79.1. These results show that HR-VQA can be improved by routing already encoded evidence rather than repeatedly acquiring new visual inputs. Code is available in the appendix.
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All authors Zhongkuan Mao, Xianjie Liu, Tianyu Meng, Yidong Wang, Wenzhuo Zhao, Ronghao Xian, Yao Jiang, Fei Shen, Junfeng Fang, Yong Dai, Yi Zhang, Keren Fu
Computer Vision · Cross-listed from cs.AI
Sign Language Question Answering: A New Task, Benchmark, and Baseline for Sign Language Understanding
Shiwei Gan, Lichen Wang, Xiao Liu et al. (+4 more)
Recent advances in sign
language (SL) understanding (SLU) have led to remarkable progress in tasks such as continuous SL recognition and SL translation. However, these tasks are designed with predefined objectives, requiring models to learn a fixed mapping from sign
videos to glosses or spoken-
language sentences. As a result, they provide only a limited assessment of whether a model truly understands the semantic content of SL
videos. To address this limitation, we first propose a new task, Sign
Language Question Answering (SLQA), which evaluates SL understanding by requiring models to answer arbitrary natural
language questions about SL
videos. Unlike previous SLU tasks, SLQA provides a more flexible and comprehensive evaluation framework that assesses multiple
reasoning capabilities beyond recognition and translation. To facilitate this task, we further construct two SignQA
benchmarks based on PHOENIX14T and CSL-Daily by automatically generating question-answer pairs from existing gloss and sentence annotations using carefully designed templates. The resulting
datasets cover five complementary question categories, including position
reasoning, structural
reasoning, visual search, gloss recognition, and translation understanding. Finally, we propose a simple yet effective baseline model equipped with a Question-Conditioned Modulated Temporal Downsampling module and an in-domain knowledge transfer strategy, enabling effective knowledge transfer from existing SLU tasks while enhancing question-aware temporal feature modeling. Extensive experiments demonstrate that our baseline consistently outperforms representative
vision-
language models across all question categories, establishing a strong
benchmark for future research on SLQA.
Datasets are available at:{https://huggingface.co/
datasets/hulala/SignQA-2026}.
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All authors Shiwei Gan, Lichen Wang, Xiao Liu, Yafeng Yin, Kuizhuang Liu, Sanglu Lu, Lei Xie
Computer Vision · Cross-listed from eess.IV
Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstruction
Gousia Habib and Laura Ruotsalainen
Reconstructing dynamic
surgical scenes is crucial for
robot-assisted minimally invasive surgery; however, it continues to be difficult because of tissue deformation, occlusions, specular reflections, and restricted viewpoints. In this study, we introduce Endo-NeRF++, a neural rendering framework that accounts for uncertainty in the reconstruction of dynamic
surgical scenes. Expanding on EndoNeRF, the suggested approach incorporates multi-resolution hash-grid encoding, temporal feature merging, and uncertainty-informed adaptive sampling to enhance reconstruction accuracy and temporal coherence in deformable endoscopic scenes.The multi-resolution hash-grid representation within the framework effectively captures both coarse and fine anatomical details, while temporal feature blending ensures stable reconstruction during tissue deformation and
surgical tool occlusions. Additionally, uncertainty-driven adaptive sampling assigns more samples to uncertain areas to enhance rendering quality and
geometric coherence. Experiments on robotic
surgical video sequences demonstrate that the proposed uncertainty-guided adaptive sampling improves PSNR by up to 1.22dB (4.3%), increases SSIM by up to 5.3%, and reduces LPIPS by up to 55.1% compared with the EndoNeRF baseline.
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Computer Vision
Hallucinations Leave a Grounding Signature:Verifier-Guided Decoding for Selective Object Correction
Lei Yang, Xinze Liu, Dayan Wu et al. (+7 more)
Large
vision-
language models (LVLMs) often hallucinate objects that are absent from an image. Despite recent progress, existing mitigation methods still lack reliable object-level grounding diagnostics and therefore tend to apply coarse-grained interventions, which can impair visual understanding, shorten responses, and reduce coverage of genuinely grounded objects. The key challenge is thus to detect, during generation, whether each emerging object mention is supported by reliable visual evidence, so that
hallucination can be mitigated selectively. Yet output confidence reflects next-
token plausibility rather than visual support, allowing
language priors to make absent objects appear certain. We show that the missing diagnostic evidence is encoded in an Intrinsic Grounding Signature (IGS), a distributed signed
attention pattern that remains informative for such confident
hallucinations. Based on IGS, we propose Verifier-Guided Decoding (VGD), a decoding framework in which a lightweight verifier examines each emerging object mention, rolls back the KV cache when the mention is identified as high risk, suppresses the object and its synonyms, and regenerates the affected continuation. Because VGD intervenes only on object mentions identified as high risk, it reduces object
hallucination while preserving the model's original visual understanding and grounded object coverage. Experiments on CHAIR and AMBER-G show that VGD achieves state-of-the-art object
hallucination reduction: at @rec90, it cuts AMBER-G CHAIR by 43.6% while retaining 99.6% of grounded-object coverage, and reduces CHAIR-MSCOCO CHAIR$_i$/CHAIR$_s$ by 37.0%/30.4% without shortening captions.
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All authors Lei Yang, Xinze Liu, Dayan Wu, Ding Wang, Hengjie Zhu, Zihao Zhang, Tianzhu Hu, Hanqi Wu, Peng Fu, Zheng Lin
Computer Vision
SPFM-Net: Semantic-Prior-Guided Frequency-Constrained Mamba for Invisible Watermark Attack
Chunpeng Wang, Yanan Shi, Zhiqiu Xia et al. (+3 more)
Existing watermark attacks typically rely on predefined signal-processing operations or locally constrained restoration networks, making it difficult to capture the long-range dependencies of globally distributed watermark signals and resulting in an unfavorable trade-off between removal effectiveness and visual fidelity. In this paper, we propose SPFM-Net, a semantic-prior-guided and frequency-constrained Mamba framework for invisible watermark attack. SPFM-Net first employs high-ratio masking to disrupt the spatial coherence of invisible watermark signals, and then utilizes a partially fine-tuned pretrained Masked Autoencoder to reconstruct semantically consistent image from sparse observations while suppressing watermark-related information. A Multi-scale Residual Frequency Feature Interaction module subsequently aggregates watermark-related residual features across multiple receptive fields, while adaptively suppressing responses from watermark-irrelevant regions. To further capture the long-range dependencies of globally distributed watermark signals, a lightweight Mamba-based Global State-space Feature Modeling (GSFM) unit is introduced to separate watermark-related features from natural image content and suppress the remaining watermark traces. In addition, SPFM-Net is optimized using a multi-level objective that jointly imposes spatial-, frequency-, and edge-domain constraints, enabling effective watermark suppression while preserving perceptual quality. Extensive experiments on representative spatial-domain, transform-domain, orthogonal moment-based, and deep learning-based watermarking schemes demonstrate that SPFM-Net achieves a favorable trade-off between watermark attack effectiveness and perceptual fidelity.
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All authors Chunpeng Wang, Yanan Shi, Zhiqiu Xia, Jidong Yang, Suo Gao, Qi Li
Computer Vision
LoMeVQA: A Comprehensive Benchmark for Longitudinal Medical VQA
Zhilin Wu, Zhangkai Ni, Chengmei Yang et al. (+4 more)
In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal information is crucial for reliable assessment of disease progression and treatment response. However, despite the rapid advancement of
multimodal large
language models (MLLMs), longitudinal medical visual
reasoning remains largely underexplored. To fill this gap, we propose LoMeVQA, a comprehensive
benchmark consisting of 206K longitudinal visual question answering (VQA) pairs for temporal medical image analysis. LoMeVQA covers five tasks: progress classification, progress description, progress report generation, differential region grounding, and differential region description. To construct the
dataset, we develop an automated pipeline that (1) organizes patient records chronologically, (2) extracts clinically meaningful entities via a medical knowledge graph, and (3) models their temporal
evolution to guide large
language models in generating high-quality longitudinal VQA pairs. Extensive evaluations demonstrate that both general-purpose and medical-domain MLLMs perform poorly on LoMeVQA, revealing substantial limitations in temporal
reasoning. To address these limitations, we introduce MedLong-8B, which achieves state-of-the-art performance across all tasks. Beyond benchmarking, we conduct detailed analyses that uncover key failure modes and shed light on how to improve longitudinal medical visual
reasoning. Our data is available at: https://github.com/pepperbubble/LoMeVQA
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All authors Zhilin Wu, Zhangkai Ni, Chengmei Yang, Longzhen Yang, Yihang Liu, Ying Wen, Lianghua He
Computer Vision
FDDWAN: A Frequency-Decoupled Diffusion Network for Watermarking Attack
Chunpeng Wang, Yuxin Li, Xiaoyu Wang et al. (+3 more)
Existing invisible watermark removal methods often struggle to accurately capture the watermark-bearing features, leading to an unfavorable trade-off between watermark suppression and perceptual fidelity. In this paper, we propose the Frequency-Decoupled
Diffusion Watermark Attack Network (FDDWAN), a coarse-to-fine framework that performs watermark removal through wavelet-domain decomposition and residual
diffusion refinement. In the initial stage, the Wavelet-based Frequency-domain Preliminary Attack Module (WFPAM) decomposes the watermarked image into low- and high-frequency subbands and applies frequency-specific attack strategies tailored to their respective contributions to watermark robustness and perceptual quality. In the next stage, the Frequency-domain Residual
Diffusion Attack Module (FRDAM) separately models the residual distributions between the preliminarily attacked outputs and the corresponding watermark-free references during training. Rather than reconstructing the entire image, FRDAM selectively refines frequency-domain residuals, directing the
diffusion process toward the remaining watermark related discrepancies while minimizing modifications to image content. Extensive experiments on CelebA and ImageNet across four representative watermarking schemes demonstrate that FDDWAN achieves a more favorable trade-off between watermark removal effectiveness and visual fidelity than conventional and learning-based attack methods.
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All authors Chunpeng Wang, Yuxin Li, Xiaoyu Wang, Jidong Yang, Suo Gao, Qi Li
Computer Vision
CXR-Retrieve: Compositional Text-to-Image Retrieval in Chest Radiography
Tomer Erez, Moshe Kimhi, Chaim Baskin et al. (+1 more)
Large chest radiography archives are difficult to search because most studies are paired only with free-text reports rather than structured clinical annotations.
Vision-
language models offer a natural interface for text-to-image
retrieval, but current biomedical models are primarily optimized for report-to-image matching rather than for satisfying short clinical search queries. This creates an objective mismatch: a model may retrieve images related to words in the query while failing to satisfy the full clinical constraint, especially for conjunctions and negations such as “atelectasis and no pneumonia.” We introduce CXR-Retrieve, a structured
benchmark for compositional chest X-ray text-to-image
retrieval. The
benchmark contains 5,159 test images from the official test-split of MIMIC-CXR-JPG and 145 textual queries spanning single and conjunction findings, both positive and negative. Relevance is defined by whether a retrieved image satisfies all asserted pathology constraints, rather than by whether it matches a paired report. We further propose a label-aware contrastive fine-tuning objective for clinical
retrieval. Our method attracts image-text pairs with compatible asserted pathology constraints, including shared confirmed absences, while explicitly repelling contradictory pairs. Starting from the in-domain CXR-CLIP checkpoint, our method improves Precision@5 over CXR-CLIP by 8.5 percentage points on two-pathology conjunctions and by 22.0 percentage points on negation queries. These results show that reliable chest X-ray
retrieval requires training objectives that model not only which findings are mentioned, but also how they are clinically asserted.
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All authors Tomer Erez, Moshe Kimhi, Chaim Baskin, Ehud Rivlin
Computer Vision
Private Face Recognition Training Dataset Publication via Identity-Decoupled and Geometry-Preserving Face Distillation
Shuhuan Chen, Xiangyu Zhu, Weisong Zhao et al. (+6 more)
Publishing private face recognition~(FR) training
datasets is
privacy-sensitive because faces expose identity information. Private FR training
dataset publication mitigates this risk by releasing protected proxies as substitutes for private training faces. However, training FR models with such data introduces an identity paradox: the identity cues that make released faces useful for recognition supervision are also the cues that make them linkable to real individuals. A protected face should be decoupled from the original identity, yet still behave as a reliable identity sample for training. Removing these cues too aggressively may destroy the class structure needed for recognition learning, whereas preserving them too faithfully may increase source-identity linkability. We argue that this paradox stems from conflating source-aligned identity semantics with recognition-useful proxy identity geometry. The former should be suppressed to reduce linkage to private individuals, while the latter should be preserved for FR learning. Based on this insight, we propose Private Face
Distillation, an identity-decoupling and geometry-preserving framework. It uses Orthogonal Geometry Preservation to construct decoupled proxy identities from private identity representations while maintaining hyperspherical geometry, and Relational
Topology Alignment to preserve identity relations for recognition learning. Experiments across multiple domain-shifted FR scenarios show that Private Face
Distillation achieves stronger utility than the evaluated publication baselines. On IJB-C surveillance, it improves $\mathrm{TAR}@\mathrm{FAR}{=}1\text{e-}{3}$ by 3.94% over the baseline while reducing source-identity linkability. These results suggest that private FR training
dataset publication should decouple source-identity correspondence while preserving proxy identity geometry.
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All authors Shuhuan Chen, Xiangyu Zhu, Weisong Zhao, Siran Peng, Tianshuo Zhang, Haoyuan Zhang, Haichao Shi, Xiao-Yu Zhang, Zhen Lei
Computer Vision
DS@GT ARC at ImageCLEFmedical 2026: Architectural Diversity for Concept Detection and Foundation-Model Scaling for Caption Prediction in Medical Image Analysis
Bowen Wang, Youwen Zhang and Ritesh Mehta
We describe the DS@GT submissions to the ImageCLEFmedical Caption 2026 challenge, which continues a long-running
benchmark on the ROCOv2
dataset with two tracks: Concept Detection (Task 1), assigning UMLS Concept Unique Identifiers (CUIs) to radiology images, and Caption Prediction (Task 2), generating natural-
language captions. For Task 1, our primary submission was a three-way late-fusion ensemble of ConvNeXt-V2, BiomedCLIP ViT-B/16, and DenseNet-169 with a regularized ”Honest Threshold Tuning” procedure designed to avoid validation overfitting on rare concepts; this submission ranked first on the official submission with a primary $F_1$ of $0.5790$ and a secondary $F_1$ of $0.9657$. In parallel, we submitted a training-free KNN
retrieval pipeline over frozen BiomedCLIP
embeddings, which reached a primary $F_1$ of $0.5780$ and a secondary $F_1$ of $0.9599$-essentially matching the fine-tuned ensemble on the primary track at a fraction of the cost. For Task 2, our submissions included a fine-tuned Gemma-3 27B model (overall $0.3571$, ranking third in the official submission), a fully fine-tuned BLIP pipeline with custom Vizwins merging ($0.3564$), and a zero-shot MedGemma-4B run with a PubMed-style
prompt ($0.3186$), spanning a wide range of model scales and training costs. Code: https://github.com/dsgt-arc/imageclef-caption-2026.
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Computer Vision · Cross-listed from cs.LG
DAS-PMVC: A Framework for Partial Multi-View Clustering via Dual Alignment and Structure Enhancement
Shubin Ma, Liang Zhao, Chuanye He et al. (+4 more)
In recent years, multi-view clustering has attracted widespread research interest. However, due to limitations in data collection devices, data across different views often suffer from misalignment, leading to the partial view
alignment problem (PVAP). To mitigate the impact of view asymmetry and irrelevant samples, this paper proposes a framework for partial multi-view clustering via dual
alignment and structure enhancement (DAS-PMVC), which leverages view structure consistency and semantic relevance. Specifically, DAS-PMVC includes three parts: anchor graph structure
alignment, where sample joint
embedding representations with consistent latent space are derived from anchor point relationships for initial view
alignment; structure-enhanced feature learning, where the model learns view structure information through pretraining and combines multi-view graph convolutional networks to further extract deep latent features from the aligned graph structure to improve the discriminative power of representations; and a dual
alignment strategy, where initial
alignment is performed through the anchor graph in the pretraining phase, and contrastive learning loss and the Hungarian
algorithm are introduced in the training phase to further optimize the
alignment of latent features. Experimental results on various
datasets demonstrate that the DAS-PMVC framework outperforms existing state-of-the-art methods in clustering performance, showcasing its effectiveness and superiority.
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All authors Shubin Ma, Liang Zhao, Chuanye He, Zhenjiao Liu, Liang Zou, Lin Yuanbo Wu, Yu Shao
Computer Vision
EgoGVAE: Ego-body Mesh Reconstruction via Guided Variational Autoencoder
Jaehun Jung and Wonjun Kim
We address the problem of recovering the full-body mesh from only the head pose. This task has become essential for various applications based on head-mounted devices or smart glasses. The challenge of this task lies in estimating the pose information of unobserved body parts based solely on a single joint (i.e., head) trajectory. Several studies have begun to adopt head-conditioned generative models, however, such previous methods are costly and time-consuming due to the
diffusion-based iterative process. As an alternative, we propose a simple yet novel method that leverages the latent space of the guidance network, which is designed as a variational autoencoder taking full-body poses as inputs. By enforcing latent distributions of this guidance network and our head-to-motion network to be similar, latent features sampled from the 'guided' distribution, i.e., distribution learned in our head-to-motion network, can be reliably decoded for natural representations of full-body poses even only with the head pose. One important advantage of the proposed method is that one-step sampling scheme achieves remarkably fast inference (more than 50 times faster) compared to
diffusion-based approaches. Experimental results on
benchmark datasets show that the proposed method efficiently improves the performance of ego-body mesh reconstruction.
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Computer Vision
Articulated Object Reconstruction from Rest-State Observation
Daeun Lee, Jaeah Lee, Woosung Kim et al. (+2 more)
Building interactive digital twins requires recovering both 3D geometry and the kinematic structures that govern how objects articulate. Yet existing methods for articulated object reconstruction require explicitly observable motion from multiple articulation states. We introduce a rest-state formulation that reconstructs articulated objects from a single closed configuration, an inherently ill-posed setting where geometry, semantics, and motion priors compensate for the absence of motion cues. Our framework adopts an explicit mesh as an intermediate representation for cross-model verification and fusion, reconciling noisy outputs from
vision-
language and
segmentation models into spatially consistent part structures. To estimate joint parameters without observed motion, we use a
video diffusion model to synthesize articulation hypotheses and validate them through
geometric consistency. Our approach achieves accurate part decomposition and physically plausible articulation, performing competitively with motion-observing reconstruction-based, generation-based, and modular pretrained-model baselines.
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All authors Daeun Lee, Jaeah Lee, Woosung Kim, Haebeom Jung, Jaesik Park
Computer Vision · Cross-listed from physics.med-ph
Three-Photon Bayesian Imaging of Ortho-Positronium
L. Raczynski, W. Krzemien, A. Coussat et al. (+5 more)
PET provides functional images relying on two-
photon coincidences from positron-electron annihilation. In human tissue, about 40% of annihilations are preceded by Ps formation, of which o-Ps component partially decays into three
photons, with the remainder annihilating via pick-off or
spin-exchange into two
photons. This three-
photon channel carries additional information about the surrounding micro-environment, including the three-to-two-
photon yield ratio as a potential diagnostic marker. We propose the TRIO
algorithm, a novel three-
photon event-by-event image reconstruction
algorithm formulated as a
Bayesian maximum a posteriori inference problem. TRIO unifies time-based trilateration, energy-based reconstruction and, for the first time, a physics-informed prior derived from the QED description of Ps decay within a single probabilistic framework. In contrast to positronium lifetime imaging, which requires a
prompt photon and is therefore restricted to specific radionuclides, TRIO relies solely on the three
photons and is fully compatible with standard radionuclides such as 18F. Monte Carlo simulation modelled after the Siemens Biograph Quadra scanner demonstrates a mean position error of 1.62~cm, improving by approximately a factor of two over the time-based trilateration (3.05 cm) and by about an order of magnitude over energy-based reconstruction alone (18 cm). More importantly, the proposed
Bayesian approach is compatible with existing TOF-PET scanners that can register three-
photon annihilation coincidences.
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All authors L. Raczynski, W. Krzemien, A. Coussat, M. Bala, B.C. Hiesmayr, K. Klimaszewski, M. Obara, R. Y. Shopa
Computer Vision
PrintAnything: Learning an Intermediate Representation for 3D printing G-code Generation
Sangmin Hong, Daniel Sungho Jung, Heewon Kim et al. (+1 more)
Point clouds are one of the most fundamental and widely used 3D representations, serving as the most basic
geometric representation of 3D shapes. Nevertheless, most existing 3D printing pipelines require a watertight mesh as input, preventing the direct use of point clouds for fabrication. A common workaround is to reconstruct meshes from point clouds; however, the resulting meshes often contain
geometric artifacts, such as incorrect faces or topological inconsistencies, that are difficult to repair and may lead to printing failures. To overcome these limitations, we propose PrintAnything, a novel framework that learns to produce executable 3D printing G-code directly from 3D point clouds without requiring mesh reconstruction. To enable point clouds to serve as direct input for slice-wise toolpath generation, we introduce a slice-wise point projection strategy that transforms unstructured 3D point clouds into slice-aligned 2D representations consistent with layer-by-layer nature of fused deposition modeling in 3D printing. To eliminate mesh dependency and provide a unified representation that bridges point clouds and G-code, we propose
Geometric plan (G-plan) map, a compact 2D representation composed of occupancy, region, and flow maps that encode the
geometric and extrusion properties required for toolpath synthesis in 3D printing. As a result, our proposed method accurately generates printable G-code directly from point clouds, enabling a practical and fully mesh-free pipeline for 3D printing. The code is publicly available at \href{https://github.com/Sangminhong/PrintAnything}{https://github.com/Sangminhong/PrintAnything}.
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All authors Sangmin Hong, Daniel Sungho Jung, Heewon Kim, Kyoung Mu Lee
Computer Vision
Calibrate Before Reason: Robust Visual Token Reduction against Semantic Drift in VLMs
Jiasheng Li, Zhong Ji, Yan Zhang et al. (+1 more)
Large
Vision-
Language Models (VLMs) suffer from prohibitive inference overhead due to long sequences of visual
tokens. However, existing visual
token reduction methods mainly improve efficiency by
pruning or compressing redundant
tokens without examining whether the resulting representation remains semantically consistent with the original representation. Mapping the original N-
token visual sequence to K
tokens may discard, dilute, or misassign critical visual cues, triggering severe semantic drift that deviates the VLM's understanding. In this paper, we first introduce the principle of 'Calibrate Before Reason' to visual
token reduction and propose CaRe, a training-free robust framework that calibrates compact visual representations before
reasoning to preserve semantic fidelity in VLMs. CaRe consists of two mutually complementary modules: 1) Perturbation-Robust Calibration Anchoring, which identifies calibration anchors with stable model-side influence under multi-directional perturbations; 2) Confidence-Gated
Token Calibration, which extracts reliable calibration signals from unselected
tokens and injects them into anchors. Extensive evaluations across diverse VLM architectures and
benchmarks verify that CaRe outperforms state-of-the-art
token reduction baselines. While
pruning 94.4% of visual
tokens, our method retains 96.4% of the original full-
token performance, delivering up to 2.30 times faster end-to-end inference speed relative to unpruned vanilla models.
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All authors Jiasheng Li, Zhong Ji, Yan Zhang, Huihui Li
Computer Vision
RefineSVG: Visual Feedback-Driven Reinforcement Learning for Image-to-SVG Generation
Shaobo Liu, Feiqiao Mao, Shuaishuai Zhou et al. (+4 more)
We propose RefineSVG, a single-step closed-loop visual feedback framework that enables
multimodal large
language models (MLLMs) to perform high-fidelity image-to-SVG generation through self-correction. Existing MLLM-based approaches rely on single-pass open-loop inference, where the model receives visual input only once and must generate thousands of SVG code
tokens without intermediate verification. This paradigm inevitably leads to
geometric drift, error accumulation, and visual
hallucination on complex images. RefineSVG overcomes this limitation by invoking an external rendering engine after an initial SVG generation pass to compare the rendered output against the target image. The comparison yields a multi-dimensional visual residual map (Diff-Map) that is fed back to the model as a ReAct-style correction signal, driving a targeted correction step. To support this render-observe-correct interaction, we further introduce an SVG-oriented semantic vocabulary that compresses
token sequences by over 52%. A progressive training pipeline spanning supervised fine-tuning, rejection-sampling cold-start data construction, and end-to-end agentic reinforcement learning aligns the model with closed-loop visual correction. Extensive experiments show that RefineSVG consistently outperforms existing baselines in reconstruction fidelity, structural accuracy, and code efficiency.Code is available at https://github.com/liuxiaobo66/RefineSVG.
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All authors Shaobo Liu, Feiqiao Mao, Shuaishuai Zhou, Yan Zhan, Weiqi Tan, Zhiqiong Lu, Zhengping Liang
Computer Vision
JigShape: Evaluating Visual-Geometric Reasoning in VLMs through Jigsaw Puzzles
Shawn Li, Wei Yang, Jike Zhong et al. (+11 more)
Jigsaw puzzle solving requires jointly
reasoning about visual content and
geometric constraints, yet existing
benchmarks use rectangular cuts that create ambiguous ground truth in texture-repeated regions. We introduce \textit{\ours{}}, a
benchmark with tab-and-blank interlocking pieces where
geometric constraints provide strong local compatibility requirements that, combined with visual content, yield unambiguous ground truth. Across 95K instances at four grid densities (4$\times$4 to 16$\times$16), we find that zero-shot VLMs largely lack
geometric reasoning: only one of five frontier models (GPT-5.5) exceeds random baseline on 4$\times$4 puzzles, while all others perform at chance level. While supervised fine-tuning achieves $>$97% on 4$\times$4, all models collapse on larger grids: GPT-5.5 drops from 70% to near-random on 8$\times$8, and even fine-tuned models fall below 5% on 12$\times$12. This “scaling cliff” suggests current architectures cannot maintain consistent constraint satisfaction as the number of pieces increases. \ours{} establishes scalable
geometric reasoning as an open challenge for
vision-
language models.
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All authors Shawn Li, Wei Yang, Jike Zhong, Jiate Li, Jiawei Yang, You Qin, Ryan Rossi, Franck Dernoncourt, Roger Zimmermann, Yue Wang, Zhengzhong Tu, Vicente Ordonez, Mohit Bansal, Yue Zhao
Computer Vision
Witness Evidence Portfolios: Single-Prefill Risk Detection for Closed Multimodal Answers
Fexiang Liu, Shiye Wang, Qiang Qiu et al. (+1 more)
Reliable deployment of
multimodal large
language models (MLLMs) requires deciding whether a confident visual answer should be trusted, reviewed, or routed to a stronger system. Confidence scores capture candidate margins, but not where the estimated signed visual readouts associated with those margins come from or how they are distributed. We study inference-time risk detection for closed visual answers using the same white-box prefill path that produces the answer. Witness Evidence Portfolios (WEP) first estimates, layer by layer, which visual contributions support or contradict the predicted candidate. It summarizes these contributions through two interpretable route families: question-related evidence provenance and signed evidence concentration. Nested grouped validation chooses the more reliable family and a sparse top-k route portfolio, which is fused with candidate confidence. WEP needs no image perturbation, decoding change, backward pass, or external verifier. Across three MLLMs and four binary-answer
benchmarks, WEP improves mean error AP by 0.134. All 12 model–
dataset gains are positive, and image-cluster bootstrap intervals are strictly positive on 10 pairs. WEP targets white-box closed-answer systems and uses a labeled calibration slice.
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All authors Fexiang Liu, Shiye Wang, Qiang Qiu, Zheng Wang
Computer Vision · Cross-listed from cs.LG
Understanding Submodular Information Measure Based Objectives for Representation Learning: A Variance and Separation Perspective
Rishabh Iyer, Truong Pham and Anay Majee
Submodular Information Measures (SIMs) have recently emerged as a powerful framework for representation learning and
multimodal learning. In particular, the SCORE framework~\cite{majee2024score} demonstrated that SIMs can serve as effective objectives for supervised contrastive learning. Despite their empirical success, however, the
geometric and statistical properties induced by different submodular information measures remain poorly understood. In this work, we develop a unified theoretical framework connecting SIMs to classical concepts in representation learning and statistical pattern recognition. We show that Total Information (TI) objectives characterize intra-class structure: Graph Cut TI recovers within-class variance, LogDet TI recovers generalized variance and covariance volume, and Facility Location TI induces imbalance-aware separation that emphasizes rare and confusable classes. We further show that Mutual Information (MI) objectives capture complementary notions of inter-class structure: Graph Cut MI is closely related to centroid separation and Fisher-style discrimination, LogDet MI captures covariance-aware separation through Mahalanobis distance, and Facility Location MI measures nearest-mode representational overlap. We validate these theoretical characterizations using controlled synthetic experiments that independently vary variance, covariance, class imbalance, class separation, and
multimodal overlap. Across all settings, the empirical behavior closely matches the proposed theory. Our results provide the first unified
geometric and statistical understanding of submodular information measures and offer principled guidance for selecting and designing SIM-based objectives for representation learning.
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Computer Vision
Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement
Chuanzhi Xu, Ziyuan Tao, Jean Julien KNell et al. (+5 more)
Personalized image enhancement should reflect individual aesthetic taste, yet learning such preferences commonly depends on private photos and ratings that are unsuitable for centralized collection. The task must infer preference from sparse, heterogeneous feedback and translate it into natural-looking color transformations on resource-constrained user devices. We introduce FedPAIE, a
federated personalized aesthetic image enhancement framework for user-adaptive color grading without centralizing raw photos or ratings. FedPAIE trains a lightweight dual-cue aesthetic scorer, calibrates it into a personalized scorer on a small local support set, and freezes it to guide regularized adaptation of a lightweight CLUT enhancer from unpaired local photographs. Fidelity constraints and an excess-gap penalty regularize scorer-guided adaptation to limit proxy-score over-optimization while preserving content and natural appearance. Training remains lightweight throughout the pipeline: scorer learning updates at most 0.787M parameters, enhancer adaptation updates 0.265M, and inference retains only a 0.293M-parameter personalized enhancer. Experiments on MIT-Adobe FiveK and Flickr-AES demonstrate effective open-world personalization and a favorable balance between user preference and image fidelity. FedPAIE thus connects decentralized preference learning with efficient personalized image transformation without requiring paired user retouches.
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All authors Chuanzhi Xu, Ziyuan Tao, Jean Julien KNell, Yanrong Chen, Haolan Guo, Xuanhua Yin, Adnan Mahmood, Weidong Cai
Computer Vision
MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models
Wenjie Zhu, Yabin Zhang, Wenjun Zeng et al. (+1 more)
Multimodal Large
Language Models (MLLMs) have achieved strong performance on a wide range of
vision-
language tasks, but often fail under imperfect or shifted contexts. A reliable MLLM should refuse truly out-of-context (OOC) questions with subject-level context shifts while still answering shifted in-context (Shifted IC) questions with non-subject context shifts. Existing
benchmarks mainly target OOC or visually unanswerable questions, but overlook answerable Shifted IC cases and cover limited OOC shifts. To fill this gap, we present MMOOC, a large-scale
benchmark for evaluating refusal and robust answering abilities of MLLMs. MMOOC contains over 41K image-question pairs, including answerable Shifted IC cases and unanswerable OOC cases, spanning three question formats, eight shift types and six visual scenarios, with data quality ensured through MLLM-based filtering and human verification. We evaluate model responses using Accuracy and Refusal Rate, and further introduce an LLM-as-a-Judge metric to assess the correctness of model
reasoning. Experiments on diverse MLLMs show that current models still struggle to balance answer-ability and refusal under shifted contexts. We further analyze key failure patterns and show that post-training can improve robustness. MMOOC will be made publicly available.
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All authors Wenjie Zhu, Yabin Zhang, Wenjun Zeng, Lei Zhang
Computer Vision
4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans
Renlong Wu, Haoran Chen, Yuxiang Wei et al. (+3 more)
Generating high-quality 360-degree dynamic human assets from text
prompts is challenging. Existing methods usually synthesize
monocular or multi-view
videos first and then fit a 4D representation, which is expensive and often causes incomplete geometry or view-inconsistent renderings. We present 4DHumanDiff, a
diffusion framework that directly generates dynamic humans represented by 4D Gaussian Splatting (4DGS) from text
prompts. By modeling the structured 4D representation space end-to-end, 4DHumanDiff avoids
video pre-generation and per-scene reconstruction, making it better suited for view-consistent and temporally coherent asset generation. The model uses a 3D U-Net backbone with temporal
attention for motion-aware generation. We further construct a large-scale text-to-4DGS
dataset with 60,000 high-quality pairs, and introduce 2D regularization and training-free 4D interpolation to improve rendering quality and motion smoothness. Experiments show that 4DHumanDiff generates consistent 360-degree dynamic humans within one minute, achieves better temporal and multi-view consistency, and reduces inference time by more than 10x.
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All authors Renlong Wu, Haoran Chen, Yuxiang Wei, Xiaowei Jin, Wangmeng Zuo, Hui Li
Computer Vision
BlindPSNR: A No-Reference Fidelity Predictor for Low-Light Image Enhancement
Mingzhe Lyu, Jinqiang Cui and Hong Zhang
Low-light image enhancement (LLIE) methods involve tunable parameters that are typically fixed, often leading to performance degradation when applied across scenes. Manually selecting the best configuration, however, can be time-consuming and not always practical. Peak signal-to-noise ratio (PSNR) is the natural fidelity criterion for automating parameter selection, yet it requires a ground-truth reference that is typically unavailable. To our knowledge, no learning-based method addresses no-reference PSNR prediction for low-light image enhancement; the natural surrogate, no-reference image quality assessment (NR-IQA), targets perceptual quality rather than signal fidelity, and all seven baselines we test achieve 0% top-1 selection accuracy on our
benchmark. With paired training data, the ground-truth PSNR is analytically computable, providing exact supervision without a separate teacher network. Building on this, we propose BlindPSNR, a lightweight no-reference network that fuses the enhanced image with the degraded low-light input via windowed cross-
attention and estimates PSNR through heteroscedastic regression. While a scalar-regression baseline achieves top-1 accuracy of 54.4%, BlindPSNR raises this to 89.5% with regret dropping from 1.62 dB to 0.026 dB, and generalizes to unseen
datasets (SRCC = 0.61-0.67).
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