Computer Vision
Virtual Try-On for Cultural Clothing: A Benchmarking Study
Muhammad Tausif Ul Islam, Shahir Awlad, Sameen Yeaser Adib et al. (+3 more)
Although existing virtual try-on systems have made significant progress with the advent of
diffusion models, the current
benchmarks of these models are based on
datasets that are dominant in western-style clothing and female models, limiting their ability to generalize culturally diverse clothing styles. In this work, we introduce BD-VITON, a virtual try-on
dataset focused on Bangladeshi garments, including saree, panjabi and salwar kameez, covering both male and female categories as well. These garments present unique structural challenges such as complex draping, asymmetric layering, and high deformation complexities which are underrepresented in the original VITON
dataset. To establish strong baselines, we retrain and evaluate try-on models, namely StableViton, HR-VITON, and VITON-HD on our
dataset. Our experiments demonstrate consistent improvements in terms of both quantitative and qualitative analysis, compared to zero shot inference.
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All authors Muhammad Tausif Ul Islam, Shahir Awlad, Sameen Yeaser Adib, Md. Atiqur Rahman, Sabbir Ahmed, Md. Hasanul Kabir
Computer Vision
Variational Flow Maps: Make Some Noise for One-Step Conditional Generation
Abbas Mammadov, So Takao, Bohan Chen et al. (+4 more)
Flow maps enable high-quality image generation in a single forward pass. However, unlike iterative
diffusion models, their lack of an explicit sampling trajectory impedes incorporating external constraints for conditional generation and solving inverse problems. We put forth Variational Flow Maps, a framework for conditional sampling that shifts the perspective of conditioning from "guiding a sampling path", to that of "learning the proper initial noise". Specifically, given an observation, we seek to learn a noise adapter model that outputs a noise distribution, so that after mapping to the data space via flow map, the samples respect the observation and data prior. To this end, we develop a principled variational objective that jointly trains the noise adapter and the flow map, improving noise-data
alignment, such that sampling from complex data posterior is achieved with a simple adapter. Experiments on various inverse problems show that VFMs produce well-calibrated conditional samples in a single (or few) steps. For ImageNet, VFM attains competitive fidelity while accelerating the sampling by orders of magnitude compared to alternative iterative
diffusion/flow models. Code is available at https://github.com/abbasmammadov/VFM
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All authors Abbas Mammadov, So Takao, Bohan Chen, Ricardo Baptista, Morteza Mardani, Yee Whye Teh, Julius Berner
Computer Vision
LEPA: Learning Geometric Equivariance in Satellite Remote Sensing Data with a Predictive Architecture
Erik Scheurer, Rocco Sedona, Stefan Kesselheim et al. (+1 more)
Geospatial foundation models provide precomputed
embeddings that serve as compact feature vectors for large-scale satellite remote sensing data. While these
embeddings can reduce data-transfer bottlenecks and computational costs, Earth observation (EO) applications can still face
geometric mismatches between user-defined areas of interest and the fixed precomputed
embedding grid. Standard latent-space interpolation is unreliable in this setting because the
embedding manifold is highly non-convex, yielding representations that do not correspond to realistic inputs. We verify this using Prithvi-EO-2.0 to understand the shortcomings of interpolation applied to patch
embeddings. As a substitute, we propose a Learned Equivariance-Predicting Architecture (LEPA). Instead of averaging vectors, LEPA conditions a predictor on
geometric augmentations to directly predict the transformed
embedding. We evaluate LEPA on NASA/USGS Harmonized Landsat-Sentinel (HLS) imagery and ImageNet-1k. Experiments show that standard interpolation achieves a mean reciprocal rank (MRR) below 0.2, whereas LEPA increases MRR to over 0.8, enabling accurate
geometric adjustment without re-encoding.
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All authors Erik Scheurer, Rocco Sedona, Stefan Kesselheim, Gabriele Cavallaro
Computer Vision
PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation
Xin-Sheng Chen, Jiayu Zhu, Pei-lin Li et al. (+3 more)
Slides serve as a critical medium for conveying information in presentation-oriented scenarios such as academia, education, and business. Despite their importance, creating high-quality slide decks remains time-consuming and cognitively demanding. Recent advances in generative models, such as Nano Banana Pro, have made automated slide generation increasingly feasible. However, existing evaluations of slide generation are often coarse-grained and rely on holistic judgments, making it difficult to accurately assess model capabilities or track meaningful advances in the field. In practice, the lack of fine-grained, verifiable evaluation criteria poses a critical bottleneck for both research and real-world deployment. In this paper, we propose PresentBench, a fine-grained, rubric-based
benchmark for evaluating automated real-world slide generation. It contains 238 evaluation instances, each supplemented with background materials required for slide creation. Moreover, we manually design an average of 54.1 checklist items per instance, each formulated as a binary question, to enable fine-grained, instance-specific evaluation of the generated slide decks. Extensive experiments show that PresentBench provides more reliable evaluation results than existing methods, and exhibits significantly stronger
alignment with human preferences. Furthermore, our
benchmark reveals that NotebookLM significantly outperforms other slide generation methods, highlighting substantial recent progress in this domain.
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All authors Xin-Sheng Chen, Jiayu Zhu, Pei-lin Li, Hanzheng Wang, Shuojin Yang, Meng-Hao Guo
Computer Vision
FabricGen: Microstructure-Aware Woven Fabric Generation
Yingjie Tang, Di Luo, Zixiong Wang et al. (+3 more)
Woven fabric materials are widely used in rendering applications, yet designing realistic examples typically involves multiple stages, requiring expertise in weaving principles and texture authoring. Recent advances have explored
diffusion models to streamline this process; however, pre-trained
diffusion models often struggle to generate intricate yarn-level details that conform to weaving rules. To address this, we present FabricGen, an end-to-end framework for generating high-quality woven fabric materials from textual descriptions. A key insight of our method is the decomposition of macro-scale textures and micro-scale weaving patterns. To generate macro-scale textures free from microstructures, we fine-tune pre-trained
diffusion models on a collected
dataset of microstructure-free fabrics. As for micro-scale weaving patterns, we develop an enhanced procedural
geometric model capable of synthesizing natural yarn-level geometry with yarn sliding and flyaway fibers. The procedural model is driven by a specialized large
language model, WeavingLLM, which is fine-tuned on an annotated
dataset of formatted weaving drafts, and
prompt-tuned with domain-specific fabric expertise. Through fine-tuning and
prompt tuning, WeavingLLM learns to design weaving drafts and fabric parameters from textual
prompts, enabling the procedural model to produce diverse weaving patterns that stick to weaving principles. The generated macro-scale texture, along with the micro-scale geometry, can be used for fabric rendering. Consequently, our framework produces materials with significantly richer detail and realism compared to prior generative models.
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All authors Yingjie Tang, Di Luo, Zixiong Wang, Xiaoli Ling, jian Yang, Beibei Wang
Computer Vision
HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing
Mengxuan Wu, Xuanlei Zhao, Ziqiao Wang et al. (+3 more)
Foundation models are transitioning from offline predictors to deployed systems expected to operate over long time horizons. In real deployments, objectives are not fixed: domains drift, user preferences evolve, and new tasks appear after the model has shipped. This elevates continual learning and instant personalization from optional features to core architectural requirements. Yet most adaptation pipelines still follow a static weight paradigm: after training (or after any adaptation step), inference executes a single parameter vector regardless of user intent, domain, or instance-specific constraints. This treats the trained or adapted model as a single point in parameter space. In heterogeneous and continually evolving regimes, distinct objectives can induce separated feasible regions over parameters, forcing any single shared update into compromise, interference, or overspecialization. As a result, continual learning and personalization are often implemented as repeated overwriting of shared weights, risking degradation of previously learned behaviors. We propose HY-WU (Weight Unleashing), a
memory-first adaptation framework that shifts adaptation pressure away from overwriting a single shared parameter point. HY-WU implements functional (operator-level)
memory as a neural module: a generator that synthesizes weight updates on-the-fly from the instance condition, yielding instance-specific operators without test-time optimization.
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All authors Mengxuan Wu, Xuanlei Zhao, Ziqiao Wang, Ruicheng Feng, Zhangyang Wang, Kai Wang
Computer Vision
Single Image Super-Resolution via Bivariate `A Trous Wavelet Diffusion
Maryam Heidari, Nantheera Anantrasirichai and Alin Achim
The effectiveness of super resolution (SR) models hinges on their ability to recover high frequency structure without introducing artifacts.
Diffusion based approaches have recently advanced the state of the art in SR. However, most
diffusion based SR pipelines operate purely in the spatial domain, which may yield high frequency details that are not well supported by the underlying low resolution evidence. On the other hand, unlike supervised SR models that may inject
dataset specific textures, single image SR relies primarily on internal image statistics and can therefore be less prone to
dataset-driven
hallucinations; nevertheless, ambiguity in the LR observation can still lead to inconsistent high frequency details. To tackle this problem, we introduce BATDiff, an unsupervised Bivariate A trous Wavelet
Diffusion model designed to provide structured cross scale guidance during the generative process. BATDiff employs an a Trous wavelet transform that constructs an undecimated multiscale representation in which high frequency components are progressively revealed while the full spatial resolution is preserved. As the core inference mechanism, BATDiff includes a bivariate cross scale module that models parent child dependencies between adjacent scales. It improves high frequency coherence and reduces mismatch artifacts in
diffusion based SR. Experiments on standard
benchmarks demonstrate that BATDiff produces sharper and more structurally consistent reconstructions than existing
diffusion and non
diffusion baselines, achieving improvements in fidelity and perceptual quality.
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Computer Vision · Cross-listed from cs.LG
LightMedSeg: Lightweight 3D Medical Image Segmentation with Learned Spatial Anchors
Kavyansh Tyagi, Vishwas Rathi and Puneet Goyal
Accurate and efficient 3D medical image
segmentation is essential for clinical AI, where models must remain reliable under stringent
memory, latency, and data availability constraints.
Transformer-based methods achieve strong accuracy but suffer from excessive parameters, high FLOPs, and limited generalization. We propose LightMedSeg, a modular UNet-style
segmentation architecture that integrates anatomical priors with adaptive context modeling. Anchor-conditioned FiLM modulation enables anatomy-aware feature calibration, while a local structural prior module and texture-aware routing dynamically allocate representational capacity to boundary-rich regions. Computational redundancy is minimized through ghost and depthwise convolutions, and multi-scale features are adaptively fused via a learned skip router with anchor-relative spatial position bias. Despite requiring only 0.48M parameters and 14.64~GFLOPs, LightMedSeg achieves
segmentation accuracy within a few Dice points of heavy
transformer baselines. Therefore, LightMedSeg is a deployable and data-efficient solution for 3D medical image
segmentation. Code will be released publicly upon acceptance.
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Computer Vision
VINO: Video-driven Invariance for Non-contextual Objects via Structural Prior Guided De-contextualization
Seul-Ki Yeom, Marcel Simon, Eunbin Lee et al. (+1 more)
Self-supervised learning (SSL) has made rapid progress, yet learned features often over-rely on contextual shortcuts-background textures and co-occurrence statistics. While
video provides rich temporal variation, dense in-the-wild streams with strong ego-motion create a co-occurrence trap: foreground objects and background context move coherently, encouraging representations to collapse into scene encoders. To address this, we propose VINO (
Video-driven Invariance for Non-Contextual Objects), a teacher-student framework that learns robust image encoders from dense
video by imposing a structural information bottleneck. Using a class-agnostic structural prior solely to generate views-not as semantic pseudo-labels-VINO forms an asymmetric
distillation problem. The teacher predicts from a foreground-union view with the background suppressed, while the student observes object-conditioned scene views that retain surrounding context but remove competing instances. Matching these targets via masked
distillation makes background cues unreliable, pushing the representation toward object-centric invariances. We further enforce temporal object permanence via teacher-anchored cross-time
distillation over track-matched objects, and stabilize part-to-whole consistency with mask-guided local views. Through
attention visualization and unsupervised object discovery on PASCAL VOC, we demonstrate that VINO effectively disentangles foreground from background. Pretrained on the dense Walking Tours Venice
video, VINO achieves 34.8 CorLoc, yielding highly focused, shape-biased representations that substantially outperform prior dense-
video and motion-guided SSL baselines.
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All authors Seul-Ki Yeom, Marcel Simon, Eunbin Lee, Tae-Ho Kim
Computer Vision · Cross-listed from cs.LG
Shaping Parameter Contribution Patterns for Out-of-Distribution Detection
Haonan Xu and Yang Yang
Out-of-distribution (OOD) detection is a well-known challenge due to deep models often producing overconfident. In this paper, we reveal a key insight that trained classifiers tend to rely on sparse parameter contribution patterns, meaning that only a few dominant parameters drive predictions. This brittleness can be exploited by OOD inputs that anomalously trigger these parameters, resulting in overconfident predictions. To address this issue, we propose a simple yet effective method called Shaping Parameter Contribution Patterns (SPCP), which enhances OOD detection robustness by encouraging the classifier to learn boundary-oriented dense contribution patterns. Specifically, SPCP operates during training by rectifying excessively high parameter contributions based on a dynamically estimated threshold. This mechanism promotes the classifier to rely on a broader set of parameters for decision-making, thereby reducing the risk of overconfident predictions caused by anomalously triggered parameters, while preserving in-distribution (ID) performance. Extensive experiments under various OOD detection setups verify the effectiveness of SPCP.
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Computer Vision
FastSTAR: Spatiotemporal Token Pruning for Efficient Autoregressive Video Synthesis
Sungwoong Yune, Suheon Jeong and Joo-Young Kim
Visual Autoregressive modeling (VAR) has emerged as a highly efficient alternative to
diffusion-based frameworks, achieving comparable synthesis quality. However, as this paradigm extends to Spacetime Autoregressive modeling (
STAR) for
video generation, scaling resolution and frame counts leads to a "
token explosion" that creates a massive computational bottleneck in the final refinement stages. To address this, we propose FastSTAR, a training-free acceleration framework designed for high-quality
video generation. Our core method, Spatiotemporal
Token Pruning, identifies essential
tokens by integrating two specialized terms: (1) Spatial similarity, which evaluates structural convergence across hierarchical scales to skip computations in regions where further refinement becomes redundant, and (2) Temporal similarity, which identifies active motion trajectories by assessing feature-level variations relative to the preceding clip. Combined with a Partial Update mechanism, FastSTAR ensures that only non-converged regions are refined, maintaining fluid motion while bypassing redundant computations. Experimental results on InfinityStar demonstrate that FastSTAR achieves up to a 2.01x speedup with a PSNR of 28.29 and less than 1% performance degradation, proving a superior efficiency-quality trade-off for
STAR-based
video synthesis.
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Computer Vision
FreeFly-Thinking : Aligning Chain-of-Thought Reasoning with Continuous UAV Navigation
Jiaxu Zhou, Shaobo Wang, Zhiyuan Yang et al. (+2 more)
Vision-
Language Navigation aims to enable
agents to understand natural
language instructions and carry out appropriate
navigation actions in real-world environments. Most work focuses on indoor settings, with little research in complex outdoor scenes. Current
UAV Vision-and-
Language Navigation models typically act as black boxes without explicit
reasoning. We introduce FreeFly-thinking, an end-to-end VLN framework that converts the
UAV agent's
egocentric images and
language instructions into a series of actions, inspired by environment of urban architecture proposed by OpenFly. We first construct a
UAV dataset for
navigation task, and then performing natural
language chain of thought. We adopt a two-stage training strategy: Supervised fine-tuning and Reinforcement fine-tuning. Experiments on unseen test demonstrate a strong performance, presenting robustness and efficiency in
UAV navigation issue.
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All authors Jiaxu Zhou, Shaobo Wang, Zhiyuan Yang, Zhenjun Yu, Tao Li
Computer Vision
Class Visualizations and Activation Atlases for Enhancing Interpretability in Deep Learning-Based Computational Pathology
Marco Gustav, Fabian Wolf, Christina Glasner et al. (+6 more)
The rapid adoption of
transformer-based models in computational pathology has enabled prediction of molecular and clinical biomarkers from H&E whole-slide images, yet interpretability has not kept pace with model complexity. While attribution- and generative-based methods are common, feature visualization approaches such as class visualizations (CVs) and activation atlases (AAs) have not been systematically evaluated for these models. We developed a visualization framework and assessed CVs and AAs for a
transformer-based foundation model across tissue and multi-organ cancer classification tasks with increasing label granularity. Four pathologists annotated real and generated images to quantify inter-observer agreement, complemented by attribution and similarity metrics. CVs preserved recognizability for morphologically distinct tissues but showed reduced separability for overlapping cancer subclasses. In tissue classification, agreement decreased from Fleiss k = 0.75 (scans) to k = 0.31 (CVs), with similar trends in cancer subclass tasks. AAs revealed layer-dependent organization: coarse tissue-level concepts formed coherent regions, whereas finer subclasses exhibited dispersion and overlap. Agreement was moderate for tissue classification (k = 0.58), high for coarse cancer groupings (k = 0.82), and low at subclass level (k = 0.11). Atlas separability closely tracked expert agreement on real images, indicating that representational ambiguity reflects intrinsic pathological complexity. Attribution-based metrics approximated expert variability in low-complexity settings, whereas perceptual and distributional metrics showed limited
alignment. Overall, concept-level feature visualization reveals structured morphological
manifolds in
transformer-based pathology models and provides a framework for expert-centered interrogation of learned representations across label granularities.
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All authors Marco Gustav, Fabian Wolf, Christina Glasner, Nic G. Reitsam, Stefan Schulz, Kira Aschenbroich, Bruno Märkl, Sebastian Foersch, Jakob Nikolas Kather
Computer Vision
ACD-U: Asymmetric co-teaching with machine unlearning for robust learning with noisy labels
Reo Fukunaga, Soh Yoshida and Mitsuji Muneyasu
Deep neural networks are prone to memorizing incorrect labels during training, which degrades their generalizability. Although recent methods have combined sample selection with semi-supervised learning (SSL) to exploit the memorization effect – where networks learn from clean data before noisy data – they cannot correct selection errors once a sample is misclassified. To overcome this, we propose asymmetric co-teaching with different architectures (ACD)-U, an asymmetric co-teaching framework that uses different model architectures and incorporates machine unlearning. ACD-U addresses this limitation through two core mechanisms. First, its asymmetric co-teaching pairs a contrastive
language-image pretraining (CLIP)-pretrained
vision Transformer with a convolutional neural network (CNN), leveraging their complementary learning behaviors: the pretrained model provides stable predictions, whereas the CNN adapts throughout training. This asymmetry, where the
vision Transformer is trained only on clean samples and the CNN is trained through SSL, effectively mitigates confirmation bias. Second, selective unlearning enables post-hoc error correction by identifying incorrectly memorized samples through loss trajectory analysis and CLIP consistency checks, and then removing their influence via Kullback–Leibler divergence-based forgetting. This approach shifts the learning paradigm from passive error avoidance to active error correction. Experiments on synthetic and real-world noisy
datasets, including CIFAR-10/100, CIFAR-N, WebVision, Clothing1M, and Red Mini-ImageNet, demonstrate state-of-the-art performance, particularly in high-noise regimes and under instance-dependent noise. The code is publicly available at https://github.com/meruemon/ACD-U.
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Computer Vision
PromptGate Client Adaptive Vision Language Gating for Open Set Federated Active Learning
Adea Nesturi, David Dueñas Gaviria, Jiajun Zeng et al. (+1 more)
Deploying medical AI across resource-constrained institutions demands data-efficient learning pipelines that respect patient
privacy.
Federated Learning (FL) enables collaborative medical AI without centralising data, yet real-world clinical pools are inherently open-set, containing out-of-distribution (OOD) noise such as imaging artifacts and wrong modalities. Standard Active Learning (AL) query strategies mistake this noise for informative samples, wasting scarce annotation budgets. We propose PromptGate, a dynamic VLM-gated framework for Open-Set
Federated AL that purifies unlabeled pools before querying. PromptGate introduces a
federated Class-Specific Context Optimization: lightweight, learnable
prompt vectors that adapt a frozen BiomedCLIP backbone to local clinical domains and aggregate globally via FedAvg – without sharing patient data. As new annotations arrive,
prompts progressively sharpen the ID/OOD boundary, turning the VLM into a dynamic gatekeeper that is strategy-agnostic: a plug-and-play pre-selection module enhancing any downstream AL strategy. Experiments on distributed dermatology and breast imaging
benchmarks show that while static VLM prompting degrades to 50% ID purity, PromptGate maintains $>$95% purity with 98% OOD recall.
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All authors Adea Nesturi, David Dueñas Gaviria, Jiajun Zeng, Shadi Albarqouni
Computer Vision
LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models
Zicheng Duan, Jiatong Xia, Zeyu Zhang et al. (+7 more)
Recent generative
video world models aim to simulate visual environment
evolution, allowing an observer to interactively explore the scene via camera control. However, they implicitly assume that the world only evolves within the observer's field of view. Once an object leaves the observer's view, its state is "frozen" in
memory, and revisiting the same region later often fails to reflect events that should have occurred in the meantime. In this work, we identify and formalize this overlooked limitation as the "out-of-sight dynamics" problem, which impedes
video world models from representing a continuously evolving world. To address this issue, we propose LiveWorld, a novel framework that extends
video world models to support persistent world
evolution. Instead of treating the world as static observational
memory, LiveWorld models a persistent global state composed of a static 3D background and dynamic entities that continue evolving even when unobserved. To maintain these unseen dynamics, LiveWorld introduces a monitor-based mechanism that autonomously simulates the temporal progression of active entities and synchronizes their evolved states upon revisiting, ensuring spatially coherent rendering. For evaluation, we further introduce LiveBench, a dedicated
benchmark for the task of maintaining out-of-sight dynamics. Extensive experiments show that LiveWorld enables persistent event
evolution and long-term scene consistency, bridging the gap between existing 2D observation-based
memory and true 4D dynamic world simulation. The baseline and
benchmark will be publicly available at https://zichengduan.github.io/LiveWorld/index.html.
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All authors Zicheng Duan, Jiatong Xia, Zeyu Zhang, Wenbo Zhang, Gengze Zhou, Chenhui Gou, Yefei He, Feng Chen, Xinyu Zhang, Lingqiao Liu
Computer Vision
CanoVerse: 3D Object Scalable Canonicalization and Dataset for Generation and Pose
Li Jin, Yuchen Yang, Weikai Chen et al. (+11 more)
3D learning systems implicitly assume that objects occupy a coherent reference frame. Nonetheless, in practice, every asset arrives with an arbitrary global rotation, and models are left to resolve directional ambiguity on their own. This persistent misalignment suppresses pose-consistent generation, and blocks the emergence of stable directional semantics. To address this issue, we construct \methodName{}, a massive canonical 3D
dataset of 320K objects over 1,156 categories – an order-of-magnitude increase over prior work. At this scale, directional semantics become statistically learnable: Canoverse improves 3D generation stability, enables precise cross-modal 3D shape
retrieval, and unlocks zero-shot point-cloud orientation estimation even for out-of-distribution data. This is achieved by a new canonicalization framework that reduces
alignment from minutes to seconds per object via compact hypothesis generation and lightweight human discrimination, transforming canonicalization from manual curation into a high-throughput data generation pipeline. The Canoverse
dataset will be publicly released upon acceptance. Project page: https://github.com/123321456-gif/Canoverse
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All authors Li Jin, Yuchen Yang, Weikai Chen, Yujie Wang, Dehao Hao, Tanghui Jia, Yingda Yin, Zeyu Hu, Runze Zhang, Keyang Luo, Li Yuan, Long Quan, Xin Wang, Xueying Qin
Computer Vision
PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection
Xijun Lu, Hongying Liu, Fanhua Shang et al. (+2 more)
Medical image anomaly detection faces unique challenges due to subtle, heterogeneous anomalies embedded in complex anatomical structures. Through systematic Grad-CAM analysis, we reveal that discriminative activation maps fail on medical data, unlike their success on industrial
datasets, motivating the need for
manifold-level modeling. We propose PDD (
Manifold-Prior Diverse
Distillation), a framework that unifies dual-teacher priors into a shared high-dimensional
manifold and distills this knowledge into dual students with complementary behaviors. Specifically, frozen VMamba-Tiny and wide-ResNet50 encoders provide global contextual and local structural priors, respectively. Their features are unified through a
Manifold Matching and Unification (MMU) module, while an Inter-Level Feature Adaption (InA) module enriches intermediate representations. The unified
manifold is distilled into two students: one performs layer-wise
distillation via InA for local consistency, while the other receives skip-projected representations through a
Manifold Prior Affine (MPA) module to capture cross-layer dependencies. A diversity loss prevents representation collapse while maintaining detection sensitivity. Extensive experiments on multiple medical
datasets demonstrate that PDD significantly outperforms existing state-of-the-art methods, achieving improvements of up to 11.8%, 5.1%, and 8.5% in AUROC on HeadCT, BrainMRI, and ZhangLab
datasets, respectively, and 3.4% in F1 max on the Uni-Medical
dataset, establishing new state-of-the-art performance in medical image anomaly detection. The implementation will be released at https://github.com/OxygenLu/PDD
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All authors Xijun Lu, Hongying Liu, Fanhua Shang, Yanming Hui, Liang Wan
Computer Vision
The Model Knows Which Tokens Matter: Automatic Token Selection via Noise Gating
Landi He, Xiaoyu Yang and Lijian Xu
Visual
tokens dominate inference cost in
vision-
language models (VLMs), yet many carry redundant information. Existing
pruning methods alleviate this but typically rely on
attention magnitude or similarity scores. We reformulate visual
token pruning as capacity constrained communication: given a fixed budget K, the model must allocate limited bandwidth to maximally preserve visual information. We propose AutoSelect, which attaches a lightweight Scorer and Denoiser to a frozen VLM and trains with only the standard next
token prediction loss, without auxiliary objectives or extra annotations. During training, a variance preserving noise gate modulates each
token's information flow according to its predicted importance so that gradients propagate through all
tokens; a diagonal
attention Denoiser then recovers the perturbed representations. At inference, only the Scorer and a hard top-K selection remain, adding negligible latency. On ten VLM
benchmarks, AutoSelect retains 96.5% of full model accuracy while accelerating LLM prefill by 2.85x with only 0.69 ms overhead, and transfers to different VLM backbones without architecture-specific tuning. Code is available at https://github.com/MedHK23/AutoSelect.
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Computer Vision
Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge
Shuai Lu, Meng Wang, Jia Guo et al. (+6 more)
Large
Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis. However, their clinical deployment is severely hindered by lacking domain-specific knowledge. In this work, we identify two structural deficiencies hindering reliable medical
reasoning: 1) the Perception Gap, where general-purpose visual encoders fail to resolve fine-grained pathological cues (e.g., microaneurysms); and 2) the
Reasoning Gap, where sparse visual evidence is progressively overridden by massive
language priors in deeper
transformer layers, leading to ungrounded
hallucinations. To bridge these gaps, we propose EyExIn, a data-efficient framework designed to anchor retinal VLMs with expert knowledge via a Deep Expert Injection mechanism. Our architecture employs an Expert-Aware Dual-Stream encoding strategy that decouples visual representation into a general stream for anatomical context and a specialized expert stream for pathological semantics. To ensure high-fidelity integration, we design a Semantic-Adaptive Gated Fusion module, which dynamically amplifies subtle lesion signals while filtering irrelevant background noise. Furthermore, we introduce Adaptive Deep Expert Injection to embed persistent "
Vision Anchors" by integrating fused visual features as residual biases directly into intermediate LLM layers. This mechanism creates a visual shortcut that forces the
reasoning stack to remain strictly grounded in visual evidence. Extensive experiments across four
benchmarks demonstrate that our model consistently outperforms massive proprietary systems. EyExIn significantly enhances domain-specific knowledge
embedding and achieves state-of-the-art precision in ophthalmic visual question answering, advancing the development of trustworthy ophthalmic AI.
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All authors Shuai Lu, Meng Wang, Jia Guo, Jiawei Du, Bo Liu, Shengzhu Yang, Weihang Zhang, Huazhu Fu, Huiqi Li
Computer Vision
Inter-Image Pixel Shuffling for Multi-focus Image Fusion
Huangxing Lin, Rongrong Ma and Cheng Wang
Multi-focus image fusion aims to combine multiple partially focused images into a single all-in-focus image. Although deep learning has shown promise in this task, its effectiveness is often limited by the scarcity of suitable training data. This paper introduces Inter-image Pixel Shuffling (IPS), a novel method that allows neural networks to learn multi-focus image fusion without requiring actual multi-focus images. IPS reformulates the task as a pixel-wise classification problem, where the goal is to identify the focused pixel from a pixel group at each spatial position. In this method, pixels from a clear optical image are treated as focused, while pixels from a low-pass filtered version of the same image are considered defocused. By randomly shuffling the focused and defocused pixels at identical spatial positions in the original and filtered images, IPS generates training data that preserves spatial structure while mixing focus-defocus information. The model is trained to select the focused pixel from each spatially aligned pixel group, thus learning to reconstruct an all-in-focus image by aggregating sharp content from the input. To further enhance fusion quality, IPS adopts a cross-image fusion network that integrates the localized representation power of convolutional neural networks with the long-range modeling capabilities of state space models. This design effectively leverages both spatial detail and contextual information to produce high-quality fused results. Experimental results indicate that IPS significantly outperforms existing multi-focus image fusion methods, even without training on multi-focus images.
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Computer Vision
TIQA: Human-Aligned Perceptual Text Quality Assessment in Generated Images
Kirill Koltsov, Aleksandr Gushchin, Anastasia Antsiferova et al. (+1 more)
Recent text-to-image models have improved global realism, but text rendering remains a persistent failure mode: images may look convincing overall, yet local typography often contains malformed glyphs, broken strokes, irregular spacing, and other artifacts that humans heavily penalize. We formulate Text-in-Image Quality Assessment (TIQA), a no-reference task that estimates a human-aligned perceptual quality score for detected text regions while disentangling visual text quality from semantic correctness. To support this setting, we introduce two
datasets. TIQA-Crops contains 120k text crops from 36k AI-generated images produced by 12 generators, with 10k mean-opinion-score (MOS) labels and 110k proxy labels for pretraining. TIQA-Images contains 1,500 text-heavy images from 10 recent generators, including proprietary systems, with paired overall-quality and text-quality subjective scores. We also propose ANTIQA, a lightweight predictor with text-specific inductive biases. Across crop-level and image-level evaluations, ANTIQA achieves the best
alignment with human judgments, reaching PLCC/SROCC of 0.942/0.935 on TIQA-Crops and 0.842/0.837 for text-quality MOS on unseen generators in TIQA-Images. In best-of-5 AI-generated image ranking, ANTIQA improves the text quality of the selected image by 0.36 MOS (14%), demonstrating utility for benchmarking, filtering, and generation-time selection. Together, these findings establish perceptual text quality as a distinct evaluation target for modern text-to-image generation.
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All authors Kirill Koltsov, Aleksandr Gushchin, Anastasia Antsiferova, Dmitriy Vatolin
Computer Vision
Efficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learning
Wangyu Feng, Shawn Young and Lijian Xu
Self-supervised learning (SSL) has emerged as a powerful paradigm for Chest X-ray (CXR) analysis under limited annotations. Yet, existing SSL strategies remain suboptimal for medical imaging. Masked image modeling allocates substantial computation to reconstructing high-frequency background details with limited diagnostic value. Contrastive learning, on the other hand, often depends on aggressive augmentations that risk altering clinically meaningful structures. We introduce Semantic-Partitioned Contrastive Learning (S-PCL), an efficient pre-training framework tailored for CXR representation learning. Instead of reconstructing pixels or relying on heavy augmentations, S-PCL randomly partitions patch
tokens from a single CXR into two non-overlapping semantic subsets. Each subset provides a complementary but incomplete view. The encoder must maximize agreement between these partitions, implicitly inferring global anatomical layout and local pathological cues from partial evidence. This semantic partitioning forms an internal bottleneck that enforces long-range dependency modeling and structural coherence. S-PCL eliminates the need for hand-crafted augmentations, auxiliary decoders, and momentum encoders. The resulting architecture is streamlined, computationally efficient, and easy to scale. Extensive experiments on large-scale CXR
benchmarks, including ChestX-ray14, CheXpert, RSNA Pneumonia and SIIM-ACR Pneumothorax, show that S-PCL achieves competitive performance while attaining the lowest GFLOPs and superior accuracy among existing SSL approaches.
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Computer Vision
NuNext: Reframing Nucleus Detection as Next-Point Detection
Zhongyi Shui, Honglin Li, Xiaozhong Ji et al. (+7 more)
Nucleus detection in histopathology is pivotal for a wide range of clinical applications. Existing approaches either regress
nuclear proxy maps that require complex post-processing, or employ dense anchors or queries that introduce severe foreground-background imbalance. In this work, we reformulate
nucleus detection as next-point prediction, wherein a
multimodal large
language model is developed to directly output foreground
nucleus centroids from the input image. The model is trained in two stages. In the supervised learning stage, we propose spatial-aware soft supervision to relax strict centroid matching and a chain-of-visual-thought strategy to incorporate visual priors that facilitate coordinate prediction. In the reinforcement fine-tuning stage, we design distribution matching reward, low-variance group filtering, and fine-grained advantage shaping to further improve the model's detection quality. Extensive experiments on nine widely used
benchmarks demonstrate the superiority of our method. Code will be released soon.
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All authors Zhongyi Shui, Honglin Li, Xiaozhong Ji, Ye Zhang, Zijiang Yang, Chenglu Zhu, Yuxuan Sun, Kai Yao, Conghui He, Cheng Tan
Computer Vision
Facial Expression Generation Aligned with Human Preference for Natural Dyadic Interaction
Xu Chen, Rui Gao, Xinjie Zhang et al. (+5 more)
Achieving natural dyadic interaction requires generating facial expressions that are emotionally appropriate and socially aligned with human preference. Human feedback offers a compelling mechanism to guide such
alignment, yet how to effectively incorporate this feedback into facial expression generation remains underexplored. In this paper, we propose a facial expression generation method aligned with human preference by leveraging human feedback to produce contextually and emotionally appropriate expressions for natural dyadic interaction. A key to our method is framing the generation of identity-independent facial expressions as an action learning process, allowing human feedback to assess their validity free from visual or identity bias. We establish a closed feedback loop in which listener expressions dynamically respond to evolving conversational cues of the speaker. Concretely, we train a
vision-
language-action model via supervised fine-tuning to map the speaker's
multimodal signals into controllable low-dimensional expression representations of a 3D morphable model. We further introduce a human-feedback reinforcement learning strategy that integrates the imitation of high-quality expression response with critic-guided optimization. Experiments on two
benchmarks demonstrate that our method effectively aligns facial expressions with human preference and achieves superior performance.
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All authors Xu Chen, Rui Gao, Xinjie Zhang, Haoyu Zhang, Che Sun, Zhi Gao, Yuwei Wu, Yunde Jia
Computer Vision · Cross-listed from cs.CR
mAVE: A Watermark for Joint Audio-Visual Generation Models
Luyang Si, Leyi Pan, Dongsheng Ma et al. (+1 more)
Watermarking joint audio-visual generation supports vendor copyright protection and content provenance. However, independently valid audio and
video watermarks do not establish a shared generation session. An adversary can splice watermarked modalities from different sessions, causing the pair to be mistaken for the vendor's original joint output. We introduce mAVE (
Manifold Audio-Visual
Entanglement), a training-free watermarking framework that strengthens vendor attribution through session binding in native joint audio-visual
diffusion transformers. mAVE separates public record
retrieval from secret session authentication: a fixed public index locates the server record, while a randomized payload binds audio bits to a session-keyed
video grid through a cryptographic digest. One
prompt-conditioned joint inversion supports provider-assisted verification of both modalities against a session record, without modifying generator weights or training auxiliary watermark networks. Our analysis establishes implementation-matched distribution preservation and a full-initialization routing/clipping budget, alongside adaptive session-pool security and stable local-perturbation bounds. Experiments on LTX-2 and MOVA show comparable generation quality. mAVE achieves 99.8% true-positive rate and 0% observed false-positive rate in the evaluated swap test, and retains 99.2% true-positive rate under FrameAvg temporal averaging. Same-
prompt and similarity-selected swaps further test session authentication beyond perceptual compatibility.
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All authors Luyang Si, Leyi Pan, Dongsheng Ma, Lijie Wen
Computer Vision
Aligning What EEG Can See: Structural Representations for Brain-Vision Matching
Jingyi Tang, Shuai Jiang, Fei Su et al. (+1 more)
Visual decoding from electroencephalography (
EEG) has emerged as a highly promising avenue for non-invasive
brain-computer interfaces (BCIs). Existing
EEG-based decoding methods predominantly align
brain signals with the final-layer semantic
embeddings of deep visual models. However, relying on these highly abstracted
embeddings inevitably leads to severe cross-modal information mismatch. In this work, we introduce the concept of Neural Visibility and accordingly propose the
EEG-Visible Layer Selection Strategy, aligning
EEG signals with intermediate visual layers to minimize this mismatch. Furthermore, to accommodate the multi-stage nature of human visual processing, we propose a novel Hierarchically Complementary Fusion (HCF) framework that jointly integrates visual representations from different hierarchical levels. Extensive experiments demonstrate that our method achieves state-of-the-art performance, reaching an 84.6% accuracy (+21.4%) on zero-shot visual decoding on the THINGS-
EEG dataset. Moreover, our method achieves up to a 129.8% performance gain across diverse
EEG baselines, demonstrating its robust generalizability.
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All authors Jingyi Tang, Shuai Jiang, Fei Su, Zhicheng Zhao
Computer Vision
Retinex Meets Language: A Physics-Semantics-Guided Underwater Image Enhancement Network
Shixuan Xu, Yabo Liu, Chao Huang et al. (+2 more)
Underwater images often suffer from severe degradation caused by light absorption and scattering, leading to color distortion, low contrast and reduced visibility. Existing
Underwater Image Enhancement (UIE) methods can be divided into two categories, i.e., prior-based and learning-based methods. The former rely on rigid physical assumptions that limit the adaptability, while the latter often face data scarcity and weak generalization. To address these issues, we propose a Physics-Semantics-Guided
Underwater Image Enhancement Network (PSG-UIENet), which couples the Retinex-grounded illumination correction with the
language-informed guidance. This network comprises a Prior-Free Illumination
Estimator and a Semantics-Guided Image Restorer. In particular, the restorer leverages the textual descriptions generated by the Contrastive
Language-Image Pre-training (CLIP) model to inject high-level semantics for perceptually meaningful guidance. Since
multimodal UIE data sets are not publicly available, we also construct a large-scale image-text UIE data set, namely, LUIQD-TD, which contains 6,418 image-reference-text triplets. To explicitly measure and optimize semantic consistency between textual descriptions and images, we further design an Image-Text Semantic Similarity (ITSS) loss function. To our knowledge, this study makes the first effort to introduce both textual guidance and the
multimodal data set into UIE tasks. Extensive experiments on our data set and four publicly available data sets demonstrate that the proposed PSG-UIENet achieves superior or comparable performance against fifteen state-of-the-art methods.
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All authors Shixuan Xu, Yabo Liu, Chao Huang, Junyu Dong, Xinghui Dong
Computer Vision
Physics-Guided VLM Priors for All-Cloud Removal
Liying Xu, Huifang Li and Huanfeng Shen
Cloud removal is a fundamental challenge in optical remote sensing due to the heterogeneous degradation. Thin clouds distort radiometry via partial transmission, while thick clouds occlude the surface. Existing pipelines separate thin-cloud correction from thick-cloud reconstruction, requiring explicit cloud-type decisions and often leading to error accumulation and discontinuities in mixed-cloud scenes. Therefore, a novel approach named Physical-VLM All-Cloud Removal (PhyVLM-CR) that integrates the semantic capability of
Vision-
Language Model (VLM) into a physical restoration model, achieving high-fidelity unified cloud removal. Specifically, the cognitive prior from a VLM (e.g., Qwen) is transformed into physical scattering parameters and a
hallucination confidence map. Leveraging this confidence map as a continuous soft gate, our method achieves a unified restoration via adaptive weighting: it prioritizes physical inversion in high-transmission regions to preserve radiometric fidelity, while seamlessly transitioning to temporal reference reconstruction in low-confidence occluded areas. This mechanism eliminates the need for explicit boundary delineation, ensuring a coherent removal across heterogeneous cloud covers. Experiments on real-world Sentinel-2 surface reflectance imagery confirm that our approach achieves a remarkable balance between cloud removal and content preservation, delivering
hallucination-free results with substantially improved quantitative accuracy compared to existing methods.
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Computer Vision
VirtueBench: Evaluating Trustworthiness under Uncertainty in Long Video Understanding
Xueqing Yu, Bohan Li, Yan Li et al. (+1 more)
Recent
Vision-
Language Models (VLMs) have made remarkable progress in
multimodal understanding tasks, yet their evaluation on long
video understanding remains unreliable. Due to limited frame inputs, key frames necessary for answering the question may be missing from the model's input. However, models that truthfully refuse to answer under such uncertainty are marked as incorrect, while those that guess may coincidentally produce the correct answer and thus obtain deceptively higher accuracy, leading to misleading evaluation results and encouraging models to guess rather than respond honestly. To address this issue, we introduce VirtueBench, a
benchmark explicitly designed to assess model trustworthiness under uncertainty. VirtueBench constructs multiple frame-sampling levels for each
video and provides ground truths that distinguish between answerable and unanswerable cases. Evaluations on 25 open-source and commercial VLMs reveal distinct refusal behaviors across different model families, with refusal accuracy ranging from over 70% in the best models to nearly 0% in the worst. Moreover, most models exhibit a substantial drop in refusal when the
prompt does not explicitly require them to do so. These findings highlight the need for developing trustworthy VLMs for
multimodal understanding, guided by
benchmarks and leaderboards that emphasize reliability and trustworthiness.
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All authors Xueqing Yu, Bohan Li, Yan Li, Zhenheng Yang