Computer Vision · Cross-listed from cs.LG
Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering
Tianyi Chen, Haobo Wang, Kai Tang et al. (+5 more)
Incomplete Multi-View Clustering (IMVC) faces the challenge of learning discriminative representations from fragmentary observations while maintaining robustness against missing views. However, prevalent Euclidean-based methods suffer from a
geometric mismatch when modeling real-world data with intrinsic hierarchies, leading to semantic blurring where representations drift towards spatially proximal but semantically distinct neighbors. To bridge this gap, we propose HERL, a
Hyperbolic Enhanced Representation Learning framework for IMVC. Operating within the Poincaré ball, HERL constructs a structure-aware latent space to enhance representation learning. Specifically, we design a dual-constraint
hyperbolic contrastive mechanism optimizing: an angular-based loss to preserve semantic identity via directional
alignment, and a distance-based loss to enforce hierarchical compactness. Furthermore, a
hyperbolic prototype head is introduced to rectify global structural drift by aligning cross-view hierarchy-aware prototype distributions. Consequently, HERL disentangles fine-grained semantic correlations to sharpen cluster boundaries and imposes
geometric constraints to rectify the data recovery process. Extensive experimental results demonstrate that HERL consistently outperforms state-of-the-art approaches.
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All authors Tianyi Chen, Haobo Wang, Kai Tang, Gengyu Lyu, Tianlei Hu, Gang Chen, Hong Ma, Meixiang Xiang
Computer Vision
Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation
Minyan Luo, Yuxin Zhang, Yifei Li et al. (+5 more)
Narrative-driven product photography has become a prevalent paradigm in modern marketing, as coherent visual storytelling helps convey product value and establishes emotional engagement with consumers. However, existing image generation methods do not support structured narrative planning or cross-panel coordination, often resulting in weak storytelling and visual incoherence. In practice, narrative product photography is commonly presented as multi-grid collages, where multiple views or scenes jointly communicate a product narrative. To ensure visual consistency across grids and aesthetic harmony of the overall composition, we generate the collage as a single unified image rather than composing independently synthesized panels. We propose a self-
reasoning agentic framework for narrative product grid collage generation. Given a product packshot and its name, the system first constructs a Product Narrative Framework that explicitly represents the product's identity, usage context, and situational environment, and translates it into complementary grids governed by a shared visual style. Constraint-aware
prompts are then compiled and fed to a generation model that synthesizes the collage jointly. The generated output is evaluated on both content validity and photography quality, with explicit gates determining whether to proceed or refine. When evaluation fails, the system performs failure attribution and applies targeted refinement, enabling progressive improvement through iterative self-reflection. Experiments demonstrate that our framework consistently improves aesthetic quality, narrative richness, and visual coherence, compared to direct prompting baselines.
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All authors Minyan Luo, Yuxin Zhang, Yifei Li, Xincan Wang, Fuzhang Wu, Tong-Yee Lee, Oliver Deussen, Weiming Dong
Computer Vision
Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
Liyin Chen, Souvick Mukherjee, Ines Maria De Carvalho Lains et al. (+4 more)
Stochastic generative models are increasingly used for longitudinal imaging, but their added complexity may provide limited benefit when predictable disease-related change is small relative to technical variability. We treat model-class selection (
stochastic vs deterministic) as an empirical step determined by a task-adaptive diagnostic. For a longitudinal image prediction task complicated by irregular follow-up, acquisition variability, and device heterogeneity, the diagnostic asks whether inter-visit image change is driven by time-dependent disease progression signals or time-independent acquisition variability. If a
stochastic approach fails to yield useful predictive diversity, a more parsimonious, deterministic model class is selected. We applied this strategy to fundus autofluorescence (FAF) future image prediction. Applied to a heterogeneous Optos FAF archive (24,335 images from 9,708 eyes), the diagnostic indicated that global inter-visit FAF change was weakly associated with elapsed time, and source-anchored
stochastic configurations produced little endpoint-resolvable sample variation. Guided by the diagnostic, we developed Temporal Retinal U-Net (TRU), a deterministic single-pass predictor conditioned on irregular imaging history and prediction horizon. Evaluated on a held-out cohort and two independent zero-shot transfer cohorts spanning rare-disease and cross-vendor shift, TRU achieved the strongest overall performance among the evaluated classical and deep-learning comparators on image-level and eye-specific progression measures, with lower precision in the smaller cross-vendor cohort. These findings support a diagnostic-guided strategy for longitudinal image prediction and demonstrate an example where deterministic predictions outperform
stochastic models.
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All authors Liyin Chen, Souvick Mukherjee, Ines Maria De Carvalho Lains, Nazlee Zebardast, Mengyu Wang, Tobias Elze, Jason I. Comander
Computer Vision
TSM-Pose: Topology-Aware Learning with Semantic Mamba for Category-Level Object Pose Estimation
Jinshuo Liu, Bingtao Ma, Junlin Su et al. (+6 more)
Category-level object pose estimation is fundamental for
embodied intelligence, yet achieving robust generalization to unseen instances remains challenging. However, existing methods mainly rely on simple feature extraction and aggregation, which struggle to capture category-shared topological structures and conduct semantic keypoint modeling, limiting their generalization. To address these, we propose a
Topology-Aware Learning with Semantic Mamba for Category-Level Pose Estimation framework (TSM-Pose). Specifically, we introduce a
Topology Extractor to capture the global topological representation of the point cloud, which is integrated into local geometry features and enables robust category-level structural representation. Simultaneously, we propose a Mamba-based Global Semantic Aggregator that injects semantics priors into keypoints to enhance their expressiveness and leverages multiple TwinMamba blocks to model long-range dependencies for more effective global feature aggregation. Extensive experiments on three
benchmark datasets (REAL275, CAMERA25, and HouseCat6D) demonstrate that TSM-Pose outperforms existing state-of-the-art methods.
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All authors Jinshuo Liu, Bingtao Ma, Junlin Su, Guanyuan Pan, Beining Wu, Cheng Yang, Jiaxuan Lu, Chenggang Yan, Shuai Wang
Computer Vision · Cross-listed from quant-ph
Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach
Riza Alaudin Syah, Irwan Alnarus Kautsar, Gunawan Witjaksono et al. (+1 more)
Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI, with classical deep learning approaches facing limitations in complex thermal pattern classification tasks. This paper presents a novel Hybrid
Quantum Neural Network (HQNN) architecture that integrates
quantum computing principles with classical convolutional neural networks for enhanced breast cancer classification. Our approach employs parameterized
quantum circuits with multi-head
attention mechanisms for
quantum-aware feature encoding, coupled with classical convolutional layers for comprehensive pattern recognition. The
quantum component utilizes a 4
qubit variational
circuit with strongly entangling layers, while the classical component incorporates advanced
attention mechanisms for feature fusion. Experimental validation on breast cancer thermographic data demonstrates substantial performance improvements over state-of-the-art classical architectures, with the
quantum-enhanced approach exhibiting superior convergence dynamics and enhanced feature representation capabilities. Our findings provide evidence for
quantum advantage in medical image classification through classical simulation, establishing a framework for
quantum-classical hybrid systems in healthcare applications. The methodology addresses key challenges in
quantum machine learning deployment while maintaining computational feasibility on near-term
quantum devices.
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All authors Riza Alaudin Syah, Irwan Alnarus Kautsar, Gunawan Witjaksono, Haza Nuzly bin Abdull Hamed
Computer Vision
MMATR-X: High-Resolution Optical-SAR Joint Pretraining via Conditioned Alignment and Degraded-Target Prediction
Bowen Peng, Yongxiang Liu, Jie Zhou et al. (+4 more)
Joint optical and Synthetic Aperture Radar (SAR) pretraining seeks cross-modal gains over representations learned from either modality alone. This is challenging because the modalities share only partial information, and their correspondence varies across pairs with scene context, acquisition timing, sensor configuration, and spatial resolution. In particular, finer resolution exposes more detailed modality-specific optical appearance and SAR backscatter, further complicating cross-modal supervision. Existing methods encourage cross-modal agreement through representation
alignment or cross-modal prediction. Under simplified models of partial, pair-dependent correspondence, we show why a fixed
alignment relation can be insufficient and characterize prediction ambiguity arising from target variation unresolved by the source representation. These analyses motivate CoDe-MAE, a masked-autoencoding framework for high-resolution optical-SAR joint pretraining. Its Conditioned Contrastive Learning (CCL) aligns cross-
attention-conditioned representations, while Cross-modal Degraded-Target Prediction (CDP) predicts transformed targets to balance conditional predictability and retained supervision. We curate OSPretrain-1M, a 1M-sample optical-SAR corpus, and use CoDe-MAE to pretrain MMATR-X, which achieves leading transfer performance across optical-only, SAR-only, and dual-modal tasks. Controlled experiments on remote-sensing optical-SAR, optical-DEM, and indoor RGB-depth pairs further support these design principles across heterogeneous modality pairs. Code: \url{https://github.com/scenarri/MMATR-X}.
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All authors Bowen Peng, Yongxiang Liu, Jie Zhou, Xiaodong Chen, Tianpeng Liu, Xiaogang Yu, Li Liu
Computer Vision · Cross-listed from eess.IV
Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images
Mario Aragonés Lozano, Oscar Romero and Antonio León
This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of
matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain and supports progressive reconstruction through ordered quasi-singular coeffients. The experimental evaluation was carried out on two biological volumetric
datasets: one full-volume scan of a fish and another of a
brain. The results show that Structured 3D-SVD achieves reconstruction quality close to that of Tucker decomposition while requiring shorter computation times and outperforms canonical polyadic decomposition (CPD) in both accuracy and runtime. In addition, a progressive reconstruction analysis shows that relatively low truncation levels are sufficient to preserve the main volumetric structures, while higher truncation levels lead to more detailed reconstructions.
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Computer Vision
Adaptive receptive field-based spatial-frequency feature reconstruction network for fine-grained few-shot image classification
Linyue Zhang, Wenyi Zeng, Zicheng Pan et al. (+6 more)
Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature-based FSFGIC methods is how to choose the size of the receptive field to extract feature descriptors (including spatial and frequency feature descriptors) from different category input images, thereby better performing the FSFGIC tasks. To address this, an adaptive receptive field-based spatial-frequency feature reconstruction network (ARF-SFR-Net) is proposed. The designed ARF-SFR-Net has the capability to adaptively determine receptive field sizes for obtaining spatial and frequency features, and effectively fuse them for reconstruction and FSFGIC tasks. The designed ARF-SFR-Net can be easily embedded into a given episodic training mechanism for end-to-end training from scratch. Extensive experiments on multiple FSFGIC
benchmarks demonstrate the effectiveness and superiority of the proposed ARF-SFR-Net over state-of-the-art approaches. The code is available at: https://github.com/ICL-SUST/ARF-SFR-Net.
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All authors Linyue Zhang, Wenyi Zeng, Zicheng Pan, Yongsheng Gao, Changming Sun, Jun Hu, Lixian Liu, Weichuan Zhang, Tuo Wang
Computer Vision
CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering
Xiyin Zeng, Yi Lu and Hao Wang
Visual Question Answering (VQA) requires models to identify the correct answer options based on both visual and textual evidence. Recent Mixture-of-Experts (MoE) methods improve option
reasoning by grouping similar concepts or routing based on examples. However, unstable routing can lead to inconsistent expert selection in the same question type, while overly stable routing may reduce flexibility. To address this, we propose Concept-Guided Routing framework (CoGR-MoE), which incorporates semantics of the answer options to guide expert selection in the training phase. Next, option features are used to reweight the selected experts, producing discriminative representations for each candidate option. These option-level representations are further used for option comparison and optimized via contrastive learning. The experimental results indicate that CoGR-MoE delivers strong performance across multiple VQA tasks, demonstrating the effectiveness of our approach.
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Computer Vision
Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning
Yichao Liu, Zongru Shao, Yueyang Teng et al. (+1 more)
Cross-dose denoising for low-dose positron emission
tomography (LDPET) has been proposed to address the limited generalization of models trained at a single noise level. However, neural networks trained on a specific dose level often fail to generalize to other dose conditions due to variations in noise magnitude and statistical properties. Conventional "one-size-for-all" models attempt to mitigate this variability but tend to learn averaged representations across noise levels, resulting in degraded performance. In this work, we analyze this limitation and show that standard training formulations implicitly optimize an expectation over heterogeneous noise distributions, causing the network to learn an averaged denoising mapping that cannot accurately model dose-specific noise characteristics. We propose a unified residual noise learning framework that estimates noise directly from low-dose PET images rather than predicting full-dose images. Experiments on large-scale multi-dose PET
datasets from two medical centers demonstrate that the proposed method outperforms the "one-size-for-all" model, individual dose-specific U-Net models, and dose-conditioned approaches, achieving improved denoising performance. These results indicate that residual noise learning effectively mitigates the averaging effect and enhances generalization for cross-dose PET denoising.
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All authors Yichao Liu, Zongru Shao, Yueyang Teng, Junwen Guo
Computer Vision · Cross-listed from cs.LG
Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning
Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen et al. (+1 more)
Diffusion models (DMs) have recently shown remarkable performance on inverse problems (IPs). Optimization-based methods can fast solve IPs using DMs as powerful regularizers, but they are susceptible to local minima and noise overfitting. Although DMs can provide strong priors for
Bayesian approaches, enforcing measurement consistency during the denoising process leads to
manifold infeasibility issues. We propose Noise-space
Hamiltonian Monte Carlo (N-HMC), a posterior sampling method that treats reverse
diffusion as a deterministic mapping from initial noise to clean images. N-HMC enables comprehensive exploration of the solution space, avoiding local optima. By moving inference entirely into the initial-noise space, N-HMC keeps proposals on the learned data
manifold. We provide a comprehensive theoretical analysis of our approach and extend the framework to a noise-adaptive variant (NA-NHMC) that effectively handles IPs with unknown noise type and level. Extensive experiments across four linear and three nonlinear inverse problems demonstrate that NA-NHMC achieves superior reconstruction quality with robust performance across different hyperparameters and initializations, significantly outperforming recent state-of-the-art methods. The code is available at https://github.com/NA-HMC/NA-HMC.
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All authors Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen, Zaiwang Gu
Computer Vision
KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains
Parthaw Goswami and Jaynto Goswami Deep
Retrieval augmented generation (RAG) has transformed text based question answering, yet its extension to visual domains remains hindered by fundamental challenges: bridging the modality gap between image queries and text heavy knowledge bases, constructing semantically meaningful visual knowledge bases, performing multihop
reasoning over retrieved images, and verifying that generated answers are faithfully grounded in visual evidence. We present KIRA (Knowledge Intensive Image
Retrieval and
Reasoning Architecture), a unified five stage framework that addresses ten core problems in visual RAG for specialized domains. KIRA introduces: (1) hierarchical semantic chunking with DINO based region detection for multi granularity knowledge base construction, (2) domain adaptive contrastive encoders with fewshot adaptation for rare visual concepts, (3) dualpath crossmodal
retrieval with chainOfThought query expansion, (4) chainOfRetrieval for multihop visual
reasoning with temporal and multiview support, and (5) evidence conditioned grounded generation with posthoc
hallucination verification. We also propose DOMAINVQAR, a
benchmark suite that evaluates visual RAG along three axes (
retrieval precision,
reasoning faithfulness, and domain correctness) going beyond standard recall metrics. Experiments across four specialized domains (medical Xray,
circuit diagrams, satellite imagery, and histopathology) with a progressive six variant ablation demonstrate that KIRA achieves 0.97
retrieval precision, 1.0 grounding scores, and 0.707 domain correctness averaged across domains, while the ablation reveals actionable insights about when each component helps and when components introduce precision diversity tradeoffs that must be managed. Code will be released upon acceptance.
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Computer Vision
Unified Ultrasound Intelligence Toward an End-to-End Agentic System
Chen Ma, Yunshu Li, Junhu Fu et al. (+3 more)
Clinical ultrasound analysis demands models that generalize across heterogeneous organs, views, and devices, while supporting interpretable workflow-level analysis. Existing methods often rely on task-wise adaptation, and joint learning may be unstable due to cross-task interference, making it hard to deliver workflow-level outputs in practice. To address these challenges, we present USTri, a tri-stage ultrasound intelligence pipeline for unified multi-organ, multi-task analysis. Stage I trains a universal generalist USGen on different domains to learn broad, transferable priors that are robust to device and protocol variability. To better handle domain shifts and reach task-aligned performance while preserving ultrasound shared knowledge, Stage II builds USpec by keeping USGen frozen and finetuning
dataset-specific heads. Stage III introduces USAgent, which mimics clinician workflows by orchestrating USpec specialists for multi-step inference and deterministic structured reports. On the FMC\_UIA validation set, our model achieves the best overall performance across 4 task types and 27
datasets, outperforming state-of-the-art methods. Moreover, qualitative results show that USAgent produces clinically structured reports with high accuracy and interpretability. Our study suggests a scalable path to ultrasound intelligence that generalizes across heterogeneous ultrasound tasks and supports consistent end-to-end clinical workflows. The code is publicly available at: https://github.com/MacDunno/USTri.
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All authors Chen Ma, Yunshu Li, Junhu Fu, Shuyu Liang, Yuanyuan Wang, Yi Guo
Computer Vision
LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
Yikun Wang, Yujie Wan, Wei Zuo et al. (+4 more)
Low-altitude Gaussian splatting (LAGS) facilitates 3D scene reconstruction by aggregating aerial images from distributed drones. However, as LAGS prioritizes maximizing reconstruction quality over communication throughput, existing low-altitude resource allocation schemes become inefficient. This inefficiency stems from their failure to account for image diversity introduced by varying viewpoints. To fill this gap, we propose a groupwise heterogeneous graph neural network (GW-HGNN) for LAGS resource allocation. GW-HGNN explicitly models the non-uniform contribution of different image groups to the reconstruction process, thus automatically balancing data fidelity and transmission cost. The key insight of GW-HGNN is to transform LAGS losses and communication constraints into graph learning costs for dual-level message passing. Experiments on real-world LAGS
datasets demonstrate that GW-HGNN significantly outperforms state-of-the-art
benchmarks across key rendering metrics, including PSNR, SSIM, and LPIPS. Furthermore, GW-HGNN reduces computational latency by approximately 100x compared to the widely-used MOSEK solver, achieving millisecond-level inference suitable for real-time deployment.
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All authors Yikun Wang, Yujie Wan, Wei Zuo, Shuai Wang, Yik-Chung Wu, Chengzhong Xu, Huseyin Arslan
Computer Vision
Physics-Informed Tracking (PIT)
Emil Hovad and Allan Peter Engsig-Karup
We propose Physics-Informed Tracking (PIT), a
video-based framework for tracking a single particle from
video, where a neural network autoencoder localizes a particle as a heatmap peak (landmark) and a differentiable physics module embedded in the autoencoder constrains several landmarks over time (a trajectory) to satisfy known dynamics. The novel Physics-Informed Landmark Loss (PILL) compares this predicted trajectory back against the landmarks, enforcing physical consistency without labels. Its supervised variant (PILLS) instead compares the prediction against ground-truth position, velocity, and bounce from simulation, enabling end-to-end backpropagation. To support supervised and unsupervised learning, we use an autoencoder with a split bottleneck that separates A) tracking-related structure via landmark heatmaps from B) background noise and subsequent image reconstruction. We evaluate a replicated 26 factorial design (n = 4 replicates, 64 configurations), showing that PILLS consistently achieves sub-pixel tracking accuracy for the bilinear and physics-refined decoder outputs under both clean and noisy conditions.
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Computer Vision
EasyVideoR1: Easier RL for Video Understanding
Chuanyu Qin, Chenxu Yang, Qingyi Si et al. (+6 more)
Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the
reasoning capabilities of large
language models. As models evolve into natively
multimodal architectures, extending RLVR to
video understanding becomes increasingly important yet remains largely unexplored, due to the diversity of
video task types, the computational overhead of repeatedly decoding and preprocessing high-dimensional visual inputs, and the difficulty of reproducible evaluation across numerous sensitive hyperparameters. Existing open-source RL training frameworks provide solid infrastructure for text and image scenarios but lack systematic optimizations tailored for
video modality. In this work, we present EasyVideoR1, a complete and efficient reinforcement learning framework specifically designed for training large
vision-
language models on
video understanding tasks. EasyVideoR1 makes the following contributions: (1) a full
video RL training pipeline with offline preprocessing and tensor caching that eliminates redundant
video decoding and yields a 1.47 $\times$ throughput improvement; (2) a comprehensive, task-aware reward system covering 11 distinct
video and image problem types with unified routing and modular extension; (3) a mixed offline-online data training paradigm that combines curated high-quality trajectories with on-policy exploration, benefiting the learning of more challenging tasks; (4) joint image-
video training with independently configurable pixel budgets, allowing the two modalities to mutually reinforce each other; and (5) an asynchronous multi-
benchmark evaluation framework covering 22 mainstream
video understanding
benchmarks, with reproduced accuracy closely aligned with officially reported scores.
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All authors Chuanyu Qin, Chenxu Yang, Qingyi Si, Naibin Gu, Dingyu Yao, Zheng Lin, Peng Fu, Nan Duan, Jiaqi Wang
Computer Vision
CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization
Antonios Kritikos, Nikolaos Spanos and Athanasios Voulodimos
Domain generalization (DG) aims to maintain performance under domain shift, which in computer
vision appears primarily as stylistic variations that cause models to overfit to domain-specific appearance cues rather than class semantics. To overcome this, recent methods use textual representations as stable, domain-invariant anchors. However,
multimodal approaches that rely on cosine similarity-based contrastive
alignment leave a modality gap where image and text
embeddings remain geometrically separated despite semantic correspondence. We propose CrossFlowDG, a novel DG framework that addresses this residual gap using noise-free, cross-modal flow matching. By learning a continuous transformation in the joint Euclidean latent space, our framework explicitly transports domain-biased image
embeddings toward domain-invariant text
embeddings of the correct class. Using the efficient VMamba image encoder and CLIP's text encoder, CrossFlowDG is tested against four common DG
benchmarks, and achieves competitive performance on several
benchmarks and state-of-the-art on TerraIncognita. Code is available at: https://github.com/ajkrit/CrossFlowDG
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Computer Vision
Bias-constrained multimodal intelligence for equitable and reliable clinical AI
Cheng Li, Weijian Huang, Jiarun Liu et al. (+6 more)
The integration of medical imaging and clinical text has enabled the emergence of generalist artificial intelligence (AI) systems for healthcare. However, pervasive biases, such as imbalanced disease prevalence, skewed anatomical region distributions, heterogeneous imaging protocols, and demographic disparities, pose significant challenges to the
fairness and reliability of
vision-
language systems in real-world clinical settings. Here we present BiasCareVL, a bias-aware
multimodal learning framework that introduces bias control directly into model design, rather than treating it as a post hoc correction. BiasCareVL incorporates adaptive uncertainty modeling with optional human-in-the-loop refinement to regulate the influence of dominant data patterns and to promote equitable
reasoning under distributional imbalance. Trained on 3.44 million samples spanning over 15 imaging modalities, the framework supports diverse clinical tasks, including visual question answering, disease classification,
segmentation, and report generation within a unified representation space. Across eight public
benchmarks covering dermatology, oncology, radiology, and pathology, BiasCareVL consistently outperforms 20 state-of-the-art methods, with pronounced gains in clinically challenging scenarios, including over 10% accuracy improvement in multi-class skin lesion diagnosis and more than 20% Dice improvement in small tumor
segmentation. Furthermore, BiasCareVL achieves diagnostic performance exceeding human accuracy with substantially reduced time requirements when evaluated with board-certified radiologists. By open-sourcing BiasCareVL, we aim to promote a transparent, reproducible, and equitable future for AI in healthcare, paving the way for general-purpose, trustworthy, and clinically reliable AI systems.
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All authors Cheng Li, Weijian Huang, Jiarun Liu, Hao Yang, Qi Yang, Song Wu, Ye Li, Hairong Zheng, Shanshan Wang
Computer Vision
Adaptive Forensic Feature Refinement via Intrinsic Importance Perception
Jiazhen Yang, Junjun Zheng, Kejia Chen et al. (+5 more)
With the rapid development of generative models and
multimodal content editing technologies, the key challenge in synthetic image detection (SID) lies in cross-distribution generalization to unknown generation sources. Visual foundation models (VFM), which acquire rich visual priors through large scale image-text
alignment pretraining, have become a promising approach to improving SID generalization. However, existing VFM-based methods remain coarse-grained in their adaptation strategies. They typically either directly use the final layer representations of VFM or simply fuse multi layer features, lacking explicit modeling of the optimal representational hierarchy for transferable forgery cues. Although fine-tuning VFM can enhance task adaptation, it may also damage the cross-modal pretrained structure that supports open-set generalization. To address this task specific tension, we reformulate VFM adaptation for SID as a joint optimization problem: it is necessary both to identify the critical representational layer that is more suitable for carrying forgery discriminative information and to constrain the disturbance caused by task knowledge injection to the pretrained structure. Based on this, we propose I2P, an SID framework centered on intrinsic importance perception. I2P first adaptively identifies the critical layer representations that are most discriminative for SID, and then constrains task-driven parameter updates within a low sensitivity parameter subspace, thereby improving task specificity while preserving the transferable structure of pretrained representations as much as possible. We conduct systematic experiments and ablation analyses on multiple cross-domain
benchmarks, and the results show that I2P achieves stable and competitive generalization performance.
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All authors Jiazhen Yang, Junjun Zheng, Kejia Chen, Xiangheng Kong, Jie Lei, Zunlei Feng, Bingde Hu, Yang Gao
Computer Vision · Cross-listed from cs.LG
CCAR: Intrinsic Robustness as an Emergent Geometric Property
Akash Samanta, Manish Pratap Singh and Debasis Chaudhuri
Standard supervised learning optimizes for predictive accuracy but remains agnostic to the internal geometry of learned features, often yielding representations that are entangled and brittle. We propose Class-Conditional Activation Regularization (CCAR) to explicitly engineer the feature space, imposing a block-diagonal structure via a soft inductive bias. By shaping the latent representation to confine class energy to orthogonal subspaces, we create an intrinsic
geometric scaffold that naturally filters noise and
adversarial perturbations. We provide theoretical analysis linking this structural constraint to the maximization of the Fisher Discriminant Ratio, establishing a formal connection between
geometric disentanglement and algorithmic stability. Empirically, this approach demonstrates that robustness is an emergent property of a well-engineered feature space, significantly outperforming baselines on label noise and input corruption
benchmarks.
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Computer Vision
Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement
Xudong Li, Jiaxi Tan, Ziyin Zhou et al. (+6 more)
Image Quality Assessment (IQA) models are increasingly deployed as perceptual critics to guide generative models and image restoration. This role demands not only accurate scores but also actionable, localized feedback. However, current MLLM-based methods adopt a single-look,
language-only paradigm, which departs from human evidence-seeking judgment and yields weakly grounded rationales, limiting their reliability for in-the-loop refinement. We propose Q-DeepSight, a think-with-image framework that emulates this human-like process. It performs interleaved
Multimodal Chain-of-Thought (iMCoT) with tool-augmented evidence acquisition (e.g., crop-and-zoom) to explicitly determine where quality degrades and why. To train these long iMCoT trajectories via reinforcement learning, we introduce two techniques: Perceptual Curriculum Reward (PCR) to mitigate reward sparsity and Evidence Gradient Filtering (EGF) to improve credit assignment for visually-grounded
reasoning. Q-DeepSight achieves state-of-the-art performance across diverse
benchmarks, including natural, restored, and AI-generated content. Furthermore, we demonstrate its practical value with Perceptual-in-Generation (PiG), a training-free framework where Q-DeepSight's diagnoses guide iterative image enhancement, effectively closing the loop between assessment and refinement.
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All authors Xudong Li, Jiaxi Tan, Ziyin Zhou, Yan Zhong, Zihao Huang, Jingyuan Zheng, Yan Zhang, Xiawu Zheng, Rongrong Ji
Computer Vision
When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization
Tianqi Li, Wenyu Fang, Xin He et al. (+3 more)
Camouflaged object detection (COD) segments objects that intentionally blend with the background, so predictions depend on subtle texture and boundary cues. COD is often needed under tight on-device
memory and latency budgets, making low-bit inference highly desirable. However, COD is unusually hard to quantize aggressively. We study post-training W4A4 quantization of
Transformer-based COD and find a task-specific cliff: heavy-tailed background
tokens dominate a shared activation range, inflating the step size and pushing weak-but-structured boundary cues into the zero bin. This exposes a
token-local bottleneck – remove cross-
token range domination and bound the zero-bin mass under 4-bit activations. To address this, we introduce COD-TDQ, a COD-aware
Token-group Dual-constraint activation Quantization method. COD-TDQ addresses this
token-local bottleneck with two coupled steps: Direct-Sum
Token-Group (DSTG) assigns
token-group scales to suppress cross-
token range domination, and Dual-Constraint Range Projection (DCRP) projects each
token-group clip range to keep the step-to-dispersion ratio and the zero-bin mass bounded. Across four COD
benchmarks and two baseline models (CFRN and ESCNet), COD-TDQ consistently achieves an $S_\alpha$ score more than 0.12 higher than that of the state-of-the-art quantization method without retraining. The code is available at https://github.com/MCG-NKU/nku-model-compre.
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All authors Tianqi Li, Wenyu Fang, Xin He, Xue Geng, Xu Cheng, Yun Liu
Computer Vision
Certainty Is Redundant: Token Sparsification for Efficient Camouflaged Object Detection with Vision Foundation Models
Yuhan Gao, Shuhao Kang, Xin He et al. (+3 more)
Camouflaged object detection (COD) aims to segment objects that closely resemble their surrounding environments.
Vision foundation models (VFMs) provide strong transferable representations for COD, but their large-scale architectures and full-
token processing incur substantial computational overhead. To address this issue, we propose Certainty-Aware
Token Sparsification (CATS) for efficient VFM-based COD. Rather than estimating general
token importance, CATS determines whether each
token still requires deeper refinement according to its foreground–background certainty. It progressively terminates the independent updates of high-certainty
tokens while retaining ambiguous
tokens for further
reasoning, thereby shortening the active
token sequence across encoder stages. Since computational redundancy does not imply informational irrelevance, we further introduce Dual-Path Feature Compensation (DPFC), which separately compresses removed foreground and background
tokens into compact certainty-weighted prototypes. Extensive experiments across multiple VFMs, backbone scales, COD architectures, and
benchmark datasets show that our method significantly reduces computational cost with only marginal accuracy degradation, suggesting a favorable accuracy–efficiency trade-off for VFM-based COD. The code will be released.
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All authors Yuhan Gao, Shuhao Kang, Xin He, Bing Li, Ming-Ming Cheng, Yun Liu
Computer Vision · Cross-listed from cs.LG
Applications of deep generative models to DNA reaction kinetics and to cryogenic electron microscopy
Chenwei Zhang
This dissertation explores how deep generative models can advance the analysis of challenging biological problems by integrating domain knowledge with deep learning. It focuses on two areas: DNA reaction kinetics and cryogenic electron microscopy (cryo-EM). In the first part, we present ViDa, a biophysics-informed framework leveraging variational autoencoders (VAEs) and
geometric scattering transforms to generate biophysically-plausible
embeddings of DNA reaction kinetics simulations. These
embeddings are reduced to a two-dimensional space to visualize DNA hybridization and toehold-mediated strand displacement reactions. ViDa preserves structure and clusters trajectory ensembles into reaction pathways, making simulation results more interpretable and revealing new mechanistic insights. In the second part, we address key challenges in cryo-EM density map interpretation and
protein structure modeling. We provide a comprehensive review and benchmarking of deep learning methods for atomic model building, with improved evaluation metrics and practical guidance. We then present Struc2mapGAN, a generative
adversarial network that synthesizes high-fidelity experimental-like cryo-EM density maps from
protein structures. Finally, we present CryoSAMU, a structure-aware
multimodal U-Net that enhances intermediate-resolution cryo-EM maps by integrating density features with structural
embeddings from
protein language models via cross-
attention. Overall, these contributions demonstrate the potential of deep generative models to interpret DNA reaction mechanisms and advance cryo-EM density map analysis and
protein structure modeling.
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Computer Vision
TowerDataset: A Heterogeneous Benchmark for Transmission Corridor Segmentation with a Global-Local Fusion Framework
Xu Cui, Xinyan Liu, Chen Yang et al. (+4 more)
Fine-grained semantic
segmentation of transmission-corridor point clouds is fundamental for intelligent power-line inspection. However, current progress is limited by realistic data scarcity and the difficulty of modeling global corridor structure and local
geometric details in long, heterogeneous scenes. Existing public
datasets usually provide only a few coarse categories or short cropped scenes which overlook long-range structural dependencies, severe long-tail distributions, and subtle distinctions among safety-critical components. As a result, current methods are difficult to evaluate under realistic inspection settings, and their ability to preserve and integrate complementary global and local cues remains unclear. To address the above challenges, we introduce TowerDataset, a heterogeneous
benchmark for transmission-corridor
segmentation. TowerDataset contains 661 real-world scenes and about 2.466 billion points. It preserves long corridor extents, defines a fine-grained 22-class taxonomy, and provides standardized splits and evaluation protocols. In addition, we present a global-local fusion framework which preserves and fuses whole-scene and local-detail information. A whole-scene branch with NoCrop training and prototypical contrastive learning captures long-range
topology and contextual dependencies. A block-wise local branch retains fine
geometric structures. Both predictions are then fused and refined by
geometric validation. This design allows the model to exploit both global relationships and local shape details when recognizing rare and confusing components. Experiments on TowerDataset and two public
benchmarks demonstrate the challenge of the proposed
benchmark and the robustness of our framework in real, complex, and heterogeneous transmission-corridor scenes. The
dataset will be released soon at https://huggingface.co/
datasets/tccx18/Towerdataset/tree/main.
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All authors Xu Cui, Xinyan Liu, Chen Yang, Zhaobo Qi, Beichen Zang, Weigang Zhang, Antoni B. Chan
Computer Vision
When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution
Yiheng Chen, Zihui Ma, Peishi Jiang et al. (+6 more)
Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observations severely underdetermine fine-scale structure. In this paper, we propose Earth Foundation Model-guided
Diffusion (EFDiff), a novel framework for super-resolution under extreme spatial degradation. EFDiff uses the Prithvi-EO-2.0 Earth foundation model to encode high-resolution multispectral reflectance into geospatial
embeddings, which are injected into the denoising network via cross-
attention to guide fine-scale reconstruction from highly degraded observations. We study two variants, EFDiff-$\epsilon$ and EFDiff-$x_0$, which offer complementary trade-offs between perceptual realism and pixel-level fidelity. We evaluate EFDiff under an extreme $32\times$ scale gap using a globally diverse
benchmark comprising 242,416 co-registered Landsat thermal-reflectance patches. Results show that EFDiff consistently outperforms baseline methods and that cross-
attention conditioning by EFM is more effective than HLS channel concatenation. Although we present EFDiff in the context of LST super-resolution, the framework is broadly applicable to remote sensing problems in which pretrained geospatial representations can guide generative reconstruction.
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All authors Yiheng Chen, Zihui Ma, Peishi Jiang, Yilong Dai, Qikai Hu, Xinyue Ye, Lingyao Li, Rita Sousa, Runlong Yu
Computer Vision
Lorentz Framework for Semantic Segmentation
Zahid Hasan, Masud Ahmed and Nirmalya Roy
Semantic
segmentation in
hyperbolic space enables compact modeling of hierarchical structure while providing inherent uncertainty quantification. Prior approaches predominantly rely on the Poincaré ball model, which suffers from numerical instability, optimization, and computational challenges. We propose a novel, tractable, architecture-agnostic semantic
segmentation framework (pixel-wise and mask classification) in the
hyperbolic Lorentz model. We employ text
embeddings with semantic and visual cues to guide hierarchical pixel-level representations in Lorentz space. This enables stable and efficient optimization without requiring a Riemannian optimizer, and easily integrates with existing Euclidean architectures. Beyond
segmentation, our approach yields free uncertainty estimation, confidence map, boundary delineation, hierarchical and text-based
retrieval, and zero-shot performance, reaching generalized flatter minima. We introduce a novel uncertainty and confidence indicator in Lorentz cone
embeddings. Further, we provide analytical and empirical insights into Lorentz optimization via gradient analysis. Extensive experiments on ADE20K, COCO-Stuff-164k, Pascal-VOC, and Cityscapes, utilizing state-of-the-art per-pixel classification models (DeepLabV3 and SegFormer) and mask classification models (mask2former and maskformer), validate the effectiveness and generality of our approach. Our results demonstrate the potential of
hyperbolic Lorentz
embeddings for robust and uncertainty-aware semantic
segmentation. Code is available at https://github.com/mxahan/Lorentz_semantic_
segmentation.
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Computer Vision
Hierarchical Vision Transformer Enhanced by Graph Convolutional Network for Image Classification
Haibin Jiao
Vision Transformer (ViT) has brought new breakthroughs to the field of image classification by introducing the self-
attention mechanism and Graph Convolutional Networks(GCN) have been proposed and successfully applied in data representation and analysis. However, there are key challenges which limit their further development: (1) The patch size selected by ViT is crucial for accurate predictions, which raises a natural question: How to select the size of patches properly or how to comprehensively combine small patches and larger patches; (2) While the spatial structure information is important in
vision tasks, the 1D position
embeddings fails to capture the spatial structure information of patches more accurately; (3) The GCN can capture the local connectivity relationships between image nodes, but it lacks the ability to capture global graph structural information. On the contrary, the self-
attention mechanism of ViT can draw the global relation on image patches, but it is unable to model the local structure of image. To overcome such limitations, we propose the Hierarchical
Vision Transformer Enhanced by Graph Convolutional Network (GCN-HViT) for image classification. Specifically, the Hierarchical ViT we designed can model patch-wise information interactions on a global scale within each level and model hierarchical relationships between small patches and large patches across multiple levels. In addition, the proposed GCN method functions as a local feature extractor to obtain the local representation of each image patch which serves as a 2D position
embedding of each patch in the 2D space. Meanwhile, it models patch-wise information interactions on a local scale within each level. Extensive experiments on 3 real-world
datasets demonstrate that GCN-HViT achieves state-of-the-art performance.
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Computer Vision
LADDER: Landmark-Only Lip-Sync Deepfake Detection Across Generators and Languages
Hao Chen and Junnan Xu
We propose LADDER, a lip-sync deepfake detector that uses only the trajectories of 64 lip landmarks. It needs no pixels and no audio. We find two problems that make such a detector fail across domains. First, frame rate is a confounding factor. On PolyGlotFake, real and fake
videos differ in frame rate, and the direction of this difference changes across
languages. We remove it by resampling every trajectory to 25 fps. Second, motion amplitude does not transfer across generators: some generators move the mouth more than real speakers, and others move it less. The slope across derivative orders does transfer, because it is higher in fake
videos for all five generators we test. LADDER combines a small trained detector (0.31 M parameters) with a prior score that needs no training: the mean of the slope features, standardized on real
videos only. Learning this direction from data, or selecting it on the training domain, transfers worse. LADDER runs at 7.2 times real time on one CPU core. It reaches 0.925 AUC on LipSyncTIMIT at CRF=23 and 0.850 on seven
languages of PolyGlotFake, comparable to pixel-based and audio-visual detectors. It is also more robust than a retrained Xception and is ahead in all 14 settings with stronger compression or image corruption, for example 0.883 against 0.579 under strong blur. Finally, a decision threshold does not transfer across domains, but 20 unlabeled real
videos bring the false positive rate back to at most 3.5%.
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Computer Vision
Chen Yang
Referring image
segmentation (RIS) requires accurate
segmentation of target regions in images according to
language descriptions, which is a cross-modal task integrating
vision and
language. Existing RIS methods typically employ large-scale
vision and
language encoding models to improve performance, but their enormous parameter size severely restricts deployment in scenarios with limited computing resources. To solve this problem, this paper proposes a channel
attention-guided cross-modal knowledge
distillation method, which transfers the high-order fine-grained correlations between
vision and
language learned by the teacher network, as well as the correlations between semantic components represented by each channel, to the student network. Compared with the traditional pixel-wise relational
distillation, this method not only enables the student to learn the knowledge of the teacher, but also retains part of its independent learning ability, alleviating the transfer of learning bias. Experimental results on two public
datasets show that the proposed
distillation method does not introduce additional parameters during inference and can achieve significant performance improvement for the student model.
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