Computer Vision
Flow Augmentation and Knowledge Distillation for Lightweight Face Presentation Attack Detection
Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Taha Hasan Masood Siddique et al. (+2 more)
Face presentation attack detection (FacePAD) remains challenging under diverse spoofing representation, including 2D print and replay, 3D mask-based spoofing, makeup-induced appearance manipulation, and physical occlusions, as well as under varying capture conditions. Motion cues are highly discriminative for FacePAD but typically require explicit optical flow estimation, which introduces substantial computational overhead and limits real-time deployment. In this work, we leverage optical flow to enhance motion representation during training while eliminating the need for flow computation at inference. We propose a dual-branch teacher model that fuses appearance cues from RGB frames with motion cues derived from colorwheel-encoded optical flow, enabling effective modeling of micro-motions and temporal consistency. To enable efficient deployment, we introduce a knowledge
distillation framework that transfers motion-aware knowledge from the flow-augmented teacher to a lightweight RGB-only student via logit
distillation. As a result, the student implicitly learns motion-sensitive representations without requiring explicit flow estimation or additional feature extraction blocks at inference. Extensive experiments demonstrate strong performance across multiple
benchmarks, achieving 0.0% HTER on Replay-Attack and Replay-Mobile, 0.94% HTER on ROSE-Youtu, 5.65% HTER on SiW-Mv2, and 0.42% ACER on OULU-NPU. The distilled student achieves performance comparable to or better than the teacher while significantly reducing parameters and FLOPs, achieving 52 FPS on an NVIDIA Jetson Orin Nano, indicating its suitability for real-time and resource-constrained FacePAD deployment.
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All authors Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Taha Hasan Masood Siddique, Kejie Huang, Shujaat Khan
Computer Vision
RoSplat: Robust Feed-Forward Pixel-wise Gaussian Splatting for Varying Input Views and High-Resolution Rendering
Hoang Chuong Nguyen, Renjie Wu, Jose M. Alvarez et al. (+1 more)
Generalizable 3D Gaussian Splatting has recently emerged as an efficient approach for novel-view synthesis, enabling feed-forward synthesis from only a few input views. However, existing pixel-wise feed-forward methods suffer from over-bright renderings when the number of input views varies during inference, as well as insufficient supervision for accurate Gaussian scale estimation, which leads to hole artifacts, particularly in high-resolution renderings. To address these issues, we identify that the over-brightness is caused by the varying number of overlapping Gaussians and propose a simple alpha normalization strategy to maintain brightness consistency across different number of input views. In addition, we introduce an auxiliary 3D sampling-based regularizer to improve Gaussian scale estimation, thereby mitigating hole artifacts in high-resolution rendering. Experiments on
benchmark datasets demonstrate that our method significantly improves baseline models under varying input-view and high-resolution rendering settings.
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All authors Hoang Chuong Nguyen, Renjie Wu, Jose M. Alvarez, Miaomiao Liu
Computer Vision
PRA-PoE: Robust Multimodal Alzheimer's Diagnosis with Arbitrary Missing Modalities
Guangqian Yang, Ye Du, Wenlong Hou et al. (+2 more)
Missing modalities are prevalent in real-world Alzheimer's disease (AD) assessment and pose a significant challenge to
multimodal learning, particularly when the distribution of observed modality subsets differs between training and deployment. Such missingness pattern mismatch induces a conditional representation shift across modality subsets. Existing approaches that rely on implicit imputation or modality synthesis often fail to explicitly model modality availability and uncertainty, leading to overconfident dependence on synthesized features, reduced robustness, and miscalibrated uncertainty estimates. To address these limitations, we propose PRA-PoE, an incomplete
multimodal learning framework that is equipped with Prototype-anchored Representation
Alignment (PRA) and an Uncertainty-aware Product of Experts (UA-PoE) fusion mechanism. First, PRA uses learnable global prototypes and availability-conditioned
tokens to encode modality availability, distinguish observed from missing modalities, re-synthesize features for missing modalities, and adaptively refine observed representations to align latent spaces across modality subsets, with the goal of reducing representation shift under varying missingness patterns. Second, UA-PoE models each modality as a Gaussian expert and performs closed-form Product of Experts fusion, where experts with higher uncertainty are automatically down-weighted via lower precision, improving uncertainty reliability. We evaluate PRA-PoE under a clinically realistic protocol by training with naturally missing data and testing on all non-empty modality combinations. PRA-PoE consistently outperforms the state-of-the-art across
datasets, achieving a 5.4% relative improvement in average accuracy on ADNI and a 10.9% relative gain in average F1 on OASIS-3 over the strongest baseline across all non-empty modality subsets.
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All authors Guangqian Yang, Ye Du, Wenlong Hou, Qian Niu, Shujun Wang
Computer Vision
Gaze Attention: Query-Adaptive Visual Routing for Efficient Multimodal LLMs
Junha Song, Byeongho Heo, Geonmo Gu et al. (+3 more)
When humans describe a visual scene, they do not process the entire image uniformly; instead, they selectively fixate on regions relevant to their intended description. In contrast, current
multimodal large
language models (MLLMs) attend to all visual
tokens, leading to diluted focus and unnecessary computational overhead. Existing efficiency methods often compress or discard visual information before generation, potentially losing details needed for later predictions. In this work, we introduce Gaze
Attention, a mechanism that enables MLLMs to select visual regions according to the needs of each generation step. By grouping visual
tokens into spatial regions and selecting those relevant to the current prediction, Gaze
Attention reduces
attention computation while focusing on relevant visual content. We further introduce learnable context
tokens that summarize images or
video frames, preserving global context under selective
attention. Experiments on 13 image and 6
video understanding
benchmarks demonstrate that Gaze
Attention matches or surpasses dense-
attention baselines while using up to 90% fewer visual KV entries. It also achieves higher average performance than KV-cache eviction baselines under matched visual KV budgets.
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All authors Junha Song, Byeongho Heo, Geonmo Gu, Jaegul Choo, Dongyoon Han, Sangdoo Yun
Computer Vision
HarmoGS: Robust 3D Gaussian Splatting in the Wild via Conflict-Aware Gradient Harmonization
Yulei Kang, Tianze Zhu, Jian-Fang Hu et al. (+2 more)
In-the-wild 3D Gaussian Splatting remains challenging due to transient distractors and illumination-induced cross-view appearance inconsistencies. Existing methods mainly rely on image-level masking to suppress unreliable supervision, but masking alone cannot fully eliminate residual occlusions or resolve illumination-induced inconsistencies, both of which can introduce conflicting cross-view gradients. These unresolved conflicts may destabilize Gaussian optimization and lead to visible reconstruction artifacts. We propose a conflict-aware 3DGS framework that addresses this problem from both image-space supervision and gradient-level optimization. Semantic Consistency-Guided Masking learns pixel-wise consistency scores to adaptively refine prior masks and suppress unreliable supervision before gradient formation. A dual-view Conflict-Aware Gradient Harmonization strategy further reconciles view-specific gradients by mutually rotating them into an orthogonal configuration, reducing negative directional interference across views. We also introduce conflict-aware densification and
pruning to stabilize Gaussian growth and remove persistently conflicting primitives. Extensive experiments on standard in-the-wild
benchmarks demonstrate that our method achieves state-of-the-art rendering quality under complex transient distractors and cross-view inconsistencies.
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All authors Yulei Kang, Tianze Zhu, Jian-Fang Hu, Jianhuang Lai, Wei-Shi Zheng
Computer Vision
Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling
Xuehai Bai, Yang Shi, Yi-Fan Zhang et al. (+7 more)
Recent image editing models have achieved remarkable progress in instruction following,
multimodal understanding, and complex visual editing. However, existing
benchmarks often fail to faithfully reflect human judgment, especially for strong frontier models, due to limited task difficulty and coarse-grained evaluation protocols. In parallel, reward models have become increasingly important for RL-based image editing optimization, yet existing reward model
benchmarks still rely on unrealistic evaluation settings that deviate from practical RL scenarios. These limitations hinder reliable assessment of both image editing models and reward models. To address these challenges, we introduce Edit-Compass and EditReward-Compass, a unified evaluation suite for image editing and reward modeling. Edit-Compass contains 2,388 carefully annotated instances spanning six progressively challenging task categories, covering capabilities such as world knowledge
reasoning, visual
reasoning, and multi-image editing. Beyond broad task coverage, Edit-Compass adopts a fine-grained multidimensional evaluation framework based on structured
reasoning and carefully designed scoring rubrics. In parallel, EditReward-Compass contains 2,251 preference pairs that simulate realistic reward modeling scenarios during RL optimization.
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All authors Xuehai Bai, Yang Shi, Yi-Fan Zhang, Xuanyu Zhu, Yuran Wang, Yifan Dai, Xinyu Liu, Yiyan Ji, Xiaoling Gu, Yuanxing Zhang
Computer Vision
BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability
Guangqian Yang, Tong Ding, Wenlong Hou et al. (+4 more)
Clinical diagnostic workups typically follow a modality escalation pathway: after initial clinical evaluation, clinicians begin with routine structural imaging (e.g., MRI), selectively add sequences such as FLAIR or T2 to refine the differential, and reserve molecular imaging (e.g., amyloid-PET) for cases that remain uncertain after standard evaluation. Consequently, patients are observed with heterogeneous and often incomplete modality subsets. However, most current AI models assume fixed data modalities as the model inputs. In this paper, we present BrainAnytime, a unified pretraining framework pretrained on 34,899 3D
brain scans from five
datasets that support
brain image analysis under arbitrary modality availability spanning multi-sequence MRI and amyloid-PET. A single model accepts whatever imaging is available, from a lone T1 scan to a full
multimodal workup. Pretraining learns structural-molecular correspondences between MRI and PET via cross-modal
distillation (RCMD) and prioritizes disease-vulnerable anatomy via atlas-guided curriculum masking (PACM), all within a shared 3D masked autoencoder (Multi-MAE3D). Across four downstream tasks and five clinically motivated modality settings, BrainAnytime largely outperforms modality-specific models, missing-modality baselines, and large-scale
brain MRI pretrained foundation models on most modality settings. Notably, it surpasses the strongest missing-modality baselines with relative improvements of 6.2% and 7.0% in average accuracy on CN vs. AD and CN vs. MCI classification, respectively. Code is available at https://github.com/SDH-Lab/BrainAnytime.
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All authors Guangqian Yang, Tong Ding, Wenlong Hou, Yue Xun, Ye Du, Qian Niu, Shujun Wang
Computer Vision
Uncertainty-aware Spatial-Frequency Registration and Fusion for Infrared and Visible Images
Xingyuan Li, Haoyuan Xu, Xingyue Zhu et al. (+4 more)
Infrared and Visible Image Fusion (IVIF) has shown promise in visual tasks under challenging environments, but fusion under unregistered conditions faces inherent misalignments. Current studies to solve them either predict the deformation parameters coarse-to-fine (i.e., coarse registration and fine registration) or estimate the deformation fields in multi-scales for registration. Though straightforward, they overlook the cumulative errors in registration, which contaminate the fusion stage and severely deteriorate the resulting images. We introduce the Spatial-Frequency Registration and Fusion (SFRF) framework, which incorporates uncertainty estimation and infrared thermal radiation distribution consistency into a unified pipeline to handle the error accumulation for robust registration and fusion across both spatial and frequency domains. Specifically, SFRF constructs a Multi-scale Iterative Registration (MIR) framework that iteratively refines the deformation field across scales, leveraging uncertainty estimation at each stage to mitigate error accumulation and enhance
alignment accuracy dynamically. To ensure the accurate
alignment of infrared thermal distributions during registration, thermal radiation distribution consistency is employed as a frequency-domain supervisory signal, promoting global consistency in the frequency domain. Based on the spatial-frequency
alignment, SFRF further adopts a Dual-branch Spatial-Frequency Fusion (DSFF) module, which incorporates spatial
geometric features and frequency distribution information to reconstruct visually appealing images. SFRF achieves impressive performance across diverse
datasets.
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All authors Xingyuan Li, Haoyuan Xu, Xingyue Zhu, Jun Ma, Yang Zou, Zhiying Jiang, Jinyuan Liu
Computer Vision
Revealing the Gap in Human and VLM Scene Description through Counterfactual Semantic Saliency
Ziqi Wen, Parsa Madinei and Miguel P. Eckstein
Evaluating whether large
vision-
language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Traditional white-box interpretability methods are inapplicable to closed-source architectures and passive metrics fail to isolate causal features. We introduce Counterfactual Semantic Saliency (CSS). This black-box, model-agnostic framework quantifies the importance of objects by measuring the semantic shift induced by their causal ablation from a scene. To evaluate AI-human semantic
alignment, we tested prominent VLMs against a human psychophysics baseline comprising 16,603 valid responses across 307 complex natural scenes and 1,306 high-fidelity counterfactual variants. Our analysis reveals a pervasive scene comprehension gap: models exhibit an over-reliance (relative to humans) on object size, object centrality, and low-level saliency of objects. In contrast, models are less sensitive to the presence of people in the scenes than our human participants when describing the images. The over-reliance on object size is a primary driver explaining variations in model-human semantic divergence. Code and data are available at https://github.com/starsky77/Counterfactual-Semantic-Saliency-Data .
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Computer Vision
EgoForce: Robust Online Egocentric Motion Reconstruction via Diffusion Forcing
Inwoo Hwang, Donggeun Lim, Hojun Jang et al. (+1 more)
With recent advances in
embodied agents and AR devices,
egocentric observations are readily available as input for real-world interactive online applications. However,
egocentric viewpoints can only sporadically observe hands, in addition to the estimated head trajectory. We propose EgoForce, an online framework for reconstructing long-term full-body motion from noisy
egocentric input. While existing generative frameworks can robustly handle noisy and sparse measurements, they assume a fixed-length observation window is available and are thus not suitable for real-time applications. Faster inference often relies on autoregressive prediction, sacrificing robustness. In contrast, we adopt a
diffusion-based method with a temporally asymmetric noise schedule inspired by
Diffusion Forcing. Specifically, our approach models temporally evolving uncertainty and incrementally denoises states as new streaming observations arrive. Combined with a noise-robust imputation strategy, EgoForce progressively generates stable and coherent full-body motion under strict causal constraints. Experiments demonstrate that our online framework outperforms existing online and offline methods, enabling long-horizon, full-body motion reconstruction in challenging
egocentric scenarios.
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All authors Inwoo Hwang, Donggeun Lim, Hojun Jang, Young Min Kim
Computer Vision
CoGE: Sim-to-Real Online Geometric Estimation for Monocular Colonoscopy
Liangjing Shao, Beilei Cui and Hongliang Ren
Geometric estimation including depth estimation and scene reconstruction is a crucial technique for colonoscopy which can provide surgeons with 3D spatial perception and
navigation. However,
geometric ground truth in colonoscopy is difficult to obtain due to narrow and enclosed space of the colon, while there is a large feature gap between simulated data and realistic data caused by artifacts and illumination. In this paper, we present CoGE, a novel framework for online
monocular geometric estimation during colonoscopy. Firstly, we propose an illumination-aware supervision module based on the Retinex theory to address illumination diversity in different colonoscopy scenes. Moreover, a structure-aware perception module is proposed based on wavelet decomposition to extract common structural and local features of the colon. Both quantitative and qualitative results demonstrate that the proposed model solely trained on simulated data achieves state-of-the-art performance in
geometric estimation for both simulated and realistic scenes.
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Computer Vision
ViDR: Grounding Multimodal Deep Research Reports in Source Visual Evidence
Zhuofan Shi, Peilun Jia, Baoqin Sun et al. (+4 more)
Recent deep research systems have improved the ability of large
language models to produce long, grounded reports through iterative
retrieval and
reasoning. However, most text-centered systems rely mainly on textual evidence, while
multimodal systems often retrieve images only weakly or generate charts themselves, leaving source figures underused as evidence. We present ViDR, a
multimodal deep research framework that grounds long-form reports in source figures. ViDR treats source figures as retrievable, interpretable, routable, and verifiable evidence objects, while still generating analytical charts when needed. It builds an evidence-indexed outline linking claims to textual and visual evidence, refines noisy web images into source-figure evidence atoms through context-aware filtering, outline-aware reranking, and VLM-based visual analysis, and generates each section with section-specific evidence. ViDR further validates visual references to reduce hallucinated or misplaced figures. We also introduce MMR Bench+, a
benchmark for evaluating visual evidence use in deep research reports, covering source-figure
retrieval, placement, interpretation, verifiability, and analytical chart generation. Experiments show that ViDR improves overall report quality, source-figure integration, and verifiability over strong commercial and open-source baselines. These results suggest that source visual evidence is important for
multimodal deep research, as it strengthens evidential grounding, visual support, and report verifiability.
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All authors Zhuofan Shi, Peilun Jia, Baoqin Sun, Haiyang Shen, Sixiong Xie, Yun Ma, Xiang Jing
Computer Vision
PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution
Zihang Xu, Xiaoyang Liu, Zheng Chen et al. (+2 more)
Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke
topology may alter character identity and break readability. Existing methods improve text fidelity with stronger recognition-based or generative priors, yet they still face two unresolved challenges under severe degradation: the text condition extracted from low-quality inputs can itself be unreliable, and a plausible global prior does not fully determine fine-grained stroke boundaries. We present PRISM, a single-step
diffusion-based Text-SR framework that addresses these two challenges through Flow-Matching Prior Rectification (FMPR) and a Structure-guided Uncertainty-aware Residual Encoder (SURE). FMPR constructs a privileged training-time prior from paired low-quality/high-quality latents and learns a flow matching that transports degraded
embeddings toward this restoration-oriented prior space, yielding more accurate and reliable global text guidance. SURE further predicts uncertainty-aware structural residuals to selectively absorb reliable local boundary evidence while suppressing ambiguous stroke cues. Together, these components enable explicit global prior rectification and local structure refinement within a single
diffusion restoration pass. Experiments on both synthetic and real-world
benchmarks show that PRISM achieves state-of-the-art performance with millisecond-level inference. Our
dataset and code will be available at https://github.com/faithxuz/PRISM.
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All authors Zihang Xu, Xiaoyang Liu, Zheng Chen, Yulun Zhang, Xiaokang Yang
Computer Vision
OCH3R: Object-Centric Holistic 3D Reconstruction
Yi Du, Yang You, Xiang Wan et al. (+1 more)
Object-centric scene understanding is a fundamental challenge in computer
vision. Existing approaches often rely on multi-stage pipelines that first apply pre-trained segmentors to extract individual objects, followed by per-object 3D reconstruction. Such methods are computationally expensive, fragile to
segmentation errors, and scale poorly with scene complexity. We introduce OCH3R, a unified framework for Object-Centric Holistic 3D Reconstruction from a single RGB image. OCH3R performs one forward pass to simultaneously predict all object instances with their 6D poses and detailed 3D reconstructions. The key idea is a
transformer architecture that predicts per-pixel attributes, including CLIP-based category
embeddings, metric depth, normalized object coordinates (NOCS), and a fixed number of 3D Gaussians representing each object. To supervise these Gaussian reconstructions, we transform them into canonical space using the predicted 6D poses and align them with pre-rendered canonical ground truth, avoiding costly per-image Gaussian label generation. On standard indoor
benchmarks, OCH3R achieves state-of-the-art performance across
monocular depth estimation, open-vocabulary semantic
segmentation, and RGB-only category-level 6D pose estimation, while producing high-fidelity, editable per-object reconstructions. Crucially, inference is fully feed-forward and scales independently of the number of objects, offering orders-of-magnitude speedups over conventional multi-stage pipelines in cluttered scenes.
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All authors Yi Du, Yang You, Xiang Wan, Leonidas Guibas
Computer Vision · Cross-listed from eess.IV
A General Bézier Tree Encoding Counterfactual Framework for Retinal-Vessel-Mediated Disease Analysis
Tan Su, Ethan Elio Meidinger, Lin Gu et al. (+1 more)
The geometry of the retinal vessel is a key biomarker of vascular diseases, yet clinical evidence remains primarily observational. Existing generative counterfactuals intervene only at the image-level disease label, failing to isolate explicit anatomical structure. To address this limitation, we propose the Bézier Tree Encoding Counterfactual Framework (BTECF). By abstracting vascular networks into interconnected cubic-Bézier segments, BTECF establishes a disease-agnostic representation in which structural
topology is explicitly preserved and atomically perturbable. Coupling this encoding with a
diffusion-based generator enables parameter-level do-interventions on explicit
geometric axes (e.g., tortuosity, caliber) while preserving background fundus textures. We validate BTECF on diabetic retinopathy, together with independent cohorts for ischemic stroke and Alzheimer's disease. Isolated counterfactual interventions produce dose-responsive shifts in classifier predictions; a matched pixel-drop control attenuates this response by an order of magnitude or more, ruling out out-of-distribution generation artifacts. By enforcing causal isolation between vessel
topology and pixel-level confounders, BTECF provides a unified generative paradigm for hypothesis verification across systemic diseases. To support reproducibility, the code will be publicly released upon acceptance.
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All authors Tan Su, Ethan Elio Meidinger, Lin Gu, Ruogu Fang
Computer Vision
Image AID via continuous-time reinforcement learning
Yilie Huang and Xun Yu Zhou
We study image inpainting with generative
diffusion models. Existing methods typically either train dedicated task-specific models, or adapt a pretrained
diffusion model separately for each masked image at deployment. We introduce a middle-ground model, termed Amortized Inpainting with
Diffusion (AID), which keeps a pretrained
diffusion backbone fixed, trains a small reusable guidance module offline, and then reuses it across masked images without per-instance optimization. We formulate it as a deterministic guidance problem with a supervised terminal objective. To make this problem learnable in high dimensions, we derive an auxiliary Gaussian formulation and prove that solving this randomized problem recovers the optimal deterministic guidance field. This bridge yields a principled continuous-time actor–critic
algorithm for learning the guidance module in a fully data-driven manner. Empirically, on AFHQv2 and FFHQ under the pixel EDM pipeline and on ImageNet under the latent EDM2 pipeline, AID consistently improves the quality–speed trade-off over strong fixed-backbone and amortized inpainting baselines across multiple mask types, while adding less than one percent trainable overhead.
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Computer Vision
ImageAttributionBench: How Far Are We from Generalizable Attribution?
Tingshu Mou, Zhipeng Wei, Chao Gong et al. (+2 more)
The rapid advancement of generative AI has enabled the creation of highly realistic and diverse synthetic images, posing critical challenges for image provenance and misinformation detection. This underscores the urgent need for effective image attribution. However, existing attribution
datasets are constrained by limited scale, outdated generation methods, and insufficient semantic diversity - hindering the development of robust and generalizable attribution models. To address these limitations, we introduce ImageAttributionBench, a comprehensive
dataset comprising images synthesized by a wide array of advanced generative models with state-of-the-art (SOTA) architectures. Covering multiple real-world semantic domains, the
dataset offers rich diversity and scale to support and accelerate progress in image attribution research. To simulate real-world attribution scenarios, we evaluate several SOTA attribution methods on ImageAttributionBench under two challenging settings: (1) training on a standard balanced split and testing on degraded images, and (2) training and testing on semantically disjoint splits. In both cases, current methods exhibit consistently poor performance, revealing significant limitations in their robustness and generalization to unseen semantic content. Our work provides a rigorous
benchmark to facilitate the development and evaluation of future image attribution methods.
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All authors Tingshu Mou, Zhipeng Wei, Chao Gong, Jingjing Chen, Xingjun Ma
Computer Vision
Asymmetric Flow Models
Hansheng Chen, Jan Ackermann, Minseo Kim et al. (+2 more)
Flow-based generation in high-dimensional pixel spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has strong low-rank structure. We present Asymmetric Flow Modeling (AsymFlow), a rank-asymmetric velocity parameterization that restricts noise prediction to a low-rank subspace while keeping data prediction full-dimensional. From this asymmetric prediction, AsymFlow analytically recovers the full-dimensional velocity, leaving the network architecture and the standard flow-matching training and sampling procedures unchanged. On ImageNet 256$\times$256, AsymFlow achieves a leading 1.57 FID, outperforming prior DiT/JiT-like pixel
diffusion models by a large margin. AsymFlow also provides the first-ever route for finetuning pretrained latent flow models into pixel-space models: aligning the low-rank pixel subspace to the latent space gives a seamless initialization that preserves the latent model's high-level semantics and structure, so finetuning mainly improves low-level mismatches rather than relearning pixel generation. We show that the pixel AsymFlow model finetuned from FLUX.2 klein 9B establishes a new state of the art for pixel-space text-to-image generation, beating its latent base on HPSv3, DPG-Bench, and GenEval while qualitatively showing substantially improved visual realism.
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All authors Hansheng Chen, Jan Ackermann, Minseo Kim, Gordon Wetzstein, Leonidas Guibas
Computer Vision
Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering
Feijiang Li, Zhenxiong Li, Jieting Wang et al. (+3 more)
Image clustering aims to partition unlabeled image
datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowledge to guide the clustering process. Recent approaches introduce semantic descriptions as prior information, most of which typically relying on matching-based techniques with predefined vocabularies. However, the limited matching space restricts their adaptability to downstream clustering tasks. Moreover, these methods primarily focus on reducing bias to improve performance, frequently overlooking the importance of variance reduction. To address these limitations, we propose GSEC (Image Clustering based on Generative Semantic Guidance and Bi-Layer Ensemble), a framework designed to reduce bias through generative semantic guidance and mitigate variance via ensemble learning. Our method employs
Multimodal Large
Language Models to generate semantic descriptions and derive image
embeddings via weighted averaging. Additionally, a bi-layer ensemble strategy integrates cross-modal information through BatchEnsemble in the inner layer and aligns outputs via an
alignment mechanism in the outer layer. Comparative experiments demonstrate that GSEC outperforms 18 state-of-the-art methods across six
benchmark datasets, while further analysis confirms its effectiveness in simultaneously reducing both bias and variance. The code is available at https://github.com/2017LI/GSEC.git.
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All authors Feijiang Li, Zhenxiong Li, Jieting Wang, Zizheng Jiu, Saixiong Liu, Liang Du
Computer Vision
GTA: Advancing Image-to-3D World Generation via Geometry Then Appearance Video Diffusion
Hanxin Zhu, Cong Wang, Peiyan Tu et al. (+4 more)
Recent developments in generative models and large-scale
datasets have substantially advanced 3D world generation, facilitating a broad range of domains including spatial intelligence,
embodied intelligence, and autonomous driving. While achieving remarkable progress, existing approaches to 3D world generation typically prioritize appearance prediction with limited modeling of the underlying geometry, leading to issues such as unreliable scene structure estimation and degraded cross-view consistency. To address these limitations, motivated by the coarse-to-fine nature of human visual perception, we propose GTA, a novel image-to-3D world generation method following a Geometry-Then-Appearance paradigm. Specifically, given a single input image, to improve the structural fidelity of synthesized 3D scenes, GTA adopts a two-stage framework with two dedicated
video diffusion models, which first generate coarse
geometric structure from novel viewpoints and then synthesize fine-grained appearance conditioned on the predicted geometry. To further enhance cross-view appearance consistency, we introduce a random latent shuffle strategy during the training process, along with a test-time scaling scheme that improves perceptual quality without compromising quantitative performance. Extensive experiments have demonstrated that our proposed method consistently outperforms existing approaches in terms of fidelity, visual quality, and
geometric accuracy. Moreover, GTA is shown to be effective as a general enhancement module that further improves the generation quality of existing image-to-3D world pipelines, as well as supporting multiple downstream applications and exhibiting favorable data efficiency during model training, highlighting its versatility and broad applicability. Project page: https://hanxinzhu-lab.github.io/GTA/.
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All authors Hanxin Zhu, Cong Wang, Peiyan Tu, Jiayi Luo, Tianyu He, Xin Jin, Zhibo Chen
Computer Vision
AdaFocus: Adaptive Relevance-Diversity Sampling with Zero-Cache Look-back for Efficient Long Video Understanding
Xiao Yang, Yingzhe Ma, Haoxuan Yu et al. (+2 more)
Long
video understanding is heavily bottlenecked by a rigid one-shot paradigm: existing methods either densely encode
videos at prohibitive
memory and latency costs, or aggressively compress them into sparse frame sets that irreversibly discard fine-grained evidence needed for downstream
reasoning. Consequently, current models struggle to simultaneously balance temporal coverage, visual details, and computational efficiency. We propose AdaFocus, an efficient framework that rethinks long-
video understanding as progressive evidence acquisition rather than one-pass encoding. AdaFocus relies on two tightly coupled components. First, a Query-Aware Adaptive Relevance-Diversity sampler (AdaRD) produces a compact yet informative
video preview, adaptively switching to global clustering when the query lacks reliable local grounding. Second, instead of caching exhaustive frame sequences in
memory, AdaFocus introduces an uncertainty-triggered refinement mechanism. It performs targeted look-back only when the model is not confident, retrieving high-resolution evidence directly from disk via a zero-cache I/O design. This turns discarded visual details from an irreversible loss into on-demand recoverable evidence without paying the cost of exhaustive preloading. Experiments on seven standard long-
video benchmarks show that AdaFocus delivers a substantially better efficiency-accuracy trade-off than strong baselines. Compared with conventional dense encoding, AdaFocus achieves improved task performance (e.g., +2.59 accuracy on VideoMME, +8.39 mIoU on Charades-STA over single-pass inference) while reducing visual
token consumption by ~33x and eliminating the need for in-
memory frame pre-caching through its zero-cache disk
retrieval design. These findings suggest that progressive preview combined with zero-cache evidence refinement is a highly effective paradigm for scalable multimedia
reasoning.
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All authors Xiao Yang, Yingzhe Ma, Haoxuan Yu, Zixin Li, Ning Qin
Computer Vision
Chao Hao, Jun Xu, Ji Du et al. (+6 more)
Language-guided
segmentation transcends the scope limitations of traditional semantic
segmentation, enabling models to segment arbitrary target regions based on natural
language instructions. Existing approaches typically adopt a two-stage framework: employing
Multimodal Large
Language Models (MLLMs) to interpret instructions and generate visual
prompts, followed by foundational
segmentation models (e.g., SAM) to produce masks. However, due to the limited spatial grounding capabilities of off-the-shelf MLLMs, these methods often rely on extensive training on large-scale
datasets to achieve satisfactory accuracy. While recent advances have introduced
reasoning mechanisms to improve performance, they predominantly operate within the textual domain, performing chain-of-thought
reasoning solely based on abstract text representations without direct visual feedback. In this paper, we propose Seg-
Agent, a completely training-free framework that pioneers Explicit
Multimodal Chain-of-
Reasoning. Unlike prior text-only
reasoning, our approach constructs an interactive visual
reasoning loop comprising three stages: generation, selection, and refinement. Specifically, we leverage Set-of-Mark (SoM) visual prompting to render candidate regions directly onto the image, allowing the MLLM to “see” and iteratively reason about spatial relationships in the visual domain rather than just the textual one. This explicit
multimodal interaction enables Seg-
Agent to achieve performance comparable to state-of-the-art training-based methods without any parameter updates. Furthermore, to comprehensively evaluate generalization across diverse scenarios, we introduce Various-LangSeg, a novel
benchmark covering explicit semantic, generic object, and
reasoning-guided
segmentation tasks. Extensive experiments demonstrate the effectiveness and robustness of our method.
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All authors Chao Hao, Jun Xu, Ji Du, Shuo Ye, Ziyue Qiao, Xiaodong Cun, Guangcong Wang, Xubin Zheng, Zitong Yu
Computer Vision
Debunking Grad-ECLIP: A Comprehensive Study on Its Incorrectness and Fundamental Principles for Model Interpretation
Yongjin Cui and Xiaohui Fan
Grad-ECLIP is published at ICML 2024 and represents a new
Transformer interpretation technical route (intermediate features-based). First, this paper demonstrates that the intermediate features-based technical route is not a novel one. Based on the existing
attention-based route, we have developed
Attention-ECLIP, which is completely equivalent to Grad-ECLIP but with simpler computation. Both through formal derivation and experimental validation, we prove that the intermediate feature-based route represented by Grad-ECLIP is actually an equivalent variant of the
attention-based route. Next, this paper demonstrates that the Grad-ECLIP method is flawed. The model interpretation results obtained by Grad-ECLIP are not those of the original model, and the interpretation results are misaligned with the model's performance. We analyze the causes of Grad-ECLIP's flaws and propose, or rather, explicitly emphasize two fundamental principles that model interpretation should adhere to in order to avoid similar errors.
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Computer Vision
DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport
Xianbing Sun, Jiahui Zhan, Liqing Zhang et al. (+1 more)
Recent
diffusion- and flow-based VTON methods achieve strong results with pretrained generative models, but their reliance on multi-step sampling incurs high inference cost, while existing acceleration methods largely overlook the intrinsic structure of the try-on task. In this paper, we highlight a key observation: VTON outputs are highly constrained by the conditional inputs, suggesting that the conditional sampling trajectory can be much straighter than that in general image generation, making one-step generation a natural solution. However, limited task-specific data makes training from scratch impractical, forcing existing methods to fine-tune pretrained models whose objectives do not encourage such straight conditional trajectories. Thus, the deviation from an ideal straight path mainly comes from the mismatch between pretrained base models and the conditional nature of try-on generation, rather than from the task itself. Motivated by this insight, we encourage straighter VTON sampling trajectories through three targeted modifications: pure conditional transport, a garment preservation loss, and a self consistency loss. We further introduce a one-step
distillation stage. Extensive experiments show that our method achieves state-of-the-art performance with one-step sampling, establishing a new standard for efficient and high-quality VTON.
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All authors Xianbing Sun, Jiahui Zhan, Liqing Zhang, Jianfu Zhang
Computer Vision
CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation
Seonghyun Jin, Youngmin Kim, Sunwoo Park et al. (+1 more)
Video world models should predict future appearance in a way that remains consistent with 3D scene structure, camera motion, and lens geometry. Existing
attention-level camera encodings, however, either describe each
token only by its viewing ray—without locating scene content along that ray—or assume pinhole projection, limiting camera control under wide-angle and fisheye lenses. We introduce Curved Ray Expectation Positional Encoding (CRePE), which represents each image
token as a depth-aware distribution along its Unified Camera Model (UCM) ray and integrates the expected rotary positional phasor along the curved path this distribution traces when projected into each query view. CRePE is realized through a lightweight
Geometric Attention Adapter on a frozen
video diffusion transformer, with pseudo radial-distance supervision from a
monocular geometry foundation model serving as a stabilizing anchor rather than an inference-time input. CRePE improves camera-control, lens, and orientation fidelity across pinhole, wide-angle, and fisheye settings, and transfers zero-shot to unseen real fisheye and diverse pinhole
videos. Through Radial MixForcing, the same positional pathway further accepts externally supplied radial maps, enabling scene-geometry-conditioned generation and source-
video motion transfer that follow the supplied geometry more faithfully than dedicated depth-conditioned baselines. CRePE thus offers a compact interface that unifies camera control, implicit 3D scene state, and external geometry control for
video world models.
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All authors Seonghyun Jin, Youngmin Kim, Sunwoo Park, Jong Chul Ye
Computer Vision
AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
Jacob Lagogiannis, William Agnew, Rosa I. Arriaga et al. (+1 more)
Anti-facial recognition (AFR) image filters alter images in ways that are subtle to people but blinding to computer
vision. Yet, despite widespread interest in these technologies to subvert surveillance, users rarely use them in practice – because the “subtle” alterations are visible enough to conflict with users' self-presentation goals. To address this challenge, we propose AuraMask: a novel approach to creating AFR filters that are both adversarially effective and aesthetically acceptable. Using AuraMask, we produce 40 “aesthetic” filters that emulate popular “one-click” Instagram image filters. We show that AuraMask filters meet or exceed the
adversarial effectiveness of prior methods against open-source facial recognition models. Moreover, in a controlled online user study ($N=630$) we confirm these filters achieve significantly higher user acceptance than prior methods. Lastly, we provide our AFR pipeline to the community for accelerated research in adversarially effective and aesthetically acceptable protections.
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All authors Jacob Lagogiannis, William Agnew, Rosa I. Arriaga, Sauvik Das
Computer Vision
Anatomy-Slot: Unsupervised Anatomical Factorization for Homologous Bilateral Reasoning in Retinal Diagnosis
Yingzhe Ma, Xiao Yang, Yuguo Yin et al. (+1 more)
Retinal diagnosis is inherently bilateral: clinicians compare homologous structures across eyes (e.g., optic disc asymmetry), yet most deep models operate on
monocular representations. We investigate whether explicit structural correspondence improves diagnosis, and propose Anatomy-Slot to operationalize this hypothesis. Anatomy-Slot introduces an unsupervised anatomical bottleneck by decomposing patch
tokens into a set of emergent, structurally-coherent slots that correspond to anatomical regions, then aligning these slots across eyes via bidirectional cross-
attention. On ODIR-5K with $n=10$ seeds, the method improves AUC by $4.2$ points over a matched ViT-L baseline (95% CIs; Wilcoxon signed-rank test, $W=0$, $p=0.002$). Pairing disruption and stress testing under Gaussian noise provide controlled tests of correspondence dependence and robustness under corruption. We further report quantitative optic disc grounding on REFUGE and cross-
attention localization analysis. Beyond the reported gains, these results indicate that object-centric anatomical correspondence offers a principled path toward interpretable diagnostic systems aligned with clinical bilateral comparison.
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All authors Yingzhe Ma, Xiao Yang, Yuguo Yin, Zheyu Wang
Computer Vision · Cross-listed from cs.CR
ThermalTap: Passive Application Fingerprinting in VR Headsets via Thermal Side Channels
Mahsin Bin Akram, A H M Nazmus Sakib, OFM Riaz Rahman Aranya et al. (+3 more)
Standalone virtual reality (VR) headsets process highly sensitive personal, professional, and health-related data, yet their susceptibility to non-contact physical side channels remains largely unexplored. Existing side-channel attacks typically require malicious software execution or physical access to peripherals, making them conspicuous and potentially patchable. This paper introduces ThermalTap, the first passive, non-contact side-channel attack that fingerprints VR applications solely from the long-wave infrared (LWIR) radiation emitted by the headset chassis. By treating a headset's thermal signature as a high-fidelity proxy for internal computational workloads, ThermalTap enables remote application inference at meter-scale distances without any device interaction. To achieve robust performance in real-world settings, the system combines a commodity thermal camera with a multi-modal sensor suite (capturing ambient temperature, humidity, and airflow) to normalize environmental noise. We evaluate ThermalTap using six applications across three commercial standalone headsets. In indoor settings, ThermalTap identifies applications with over 90% accuracy using only 10 seconds of thermal camera data. Under outdoor conditions, with longer session-level observations, several applications remain identifiable despite environmental variability, with the strongest outdoor application reaching 81% accuracy. Our findings establish thermal radiation as a fundamental and unavoidable
privacy risk for immersive systems, exposing a critical security gap that bypasses current software-level protections and physical access controls.
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All authors Mahsin Bin Akram, A H M Nazmus Sakib, OFM Riaz Rahman Aranya, Raveen Wijewickrama, Kevin Desai, Murtuza Jadliwala
Computer Vision
GuardMarkGS: Unified Ownership Tracing and Edit Deterrence for 3D Gaussian Splatting
Utae Jeong, Jaewan Choi, Junseok Lee et al. (+4 more)
3D Gaussian Splatting (3DGS) is becoming a practical representation for novel view synthesis, but its growing adoption, together with rapid advances in instruction-driven 3DGS editing, also exposes a dual copyright risk: once a 3DGS-based asset is released, it can be used without permission and manipulated through 3D editing. Existing protection methods address only one side of this problem. Watermarking can trace ownership after unauthorized use, but it cannot prevent malicious editing.
Adversarial edit-deterrence methods can disrupt editing, but they do not provide evidence of ownership. To the best of our knowledge, we present the first unified protection framework for 3DGS that jointly optimizes ownership tracing and unauthorized editing deterrence. Our framework combines a scene-wide watermarking objective over all Gaussians with an
adversarial objective for edit deterrence. The
adversarial branch combines latent-anchor separation, denoising-trajectory diversion, and cross-
attention diversion to divert the editing trajectory, while an update-saliency-motivated Gaussian selection strategy assigns stronger
adversarial updates to mask-selected Gaussians, improving the balance among watermark recovery, edit deterrence, and rendering fidelity. Experiments on scenes from Mip-NeRF 360 and Instruct-NeRF2NeRF demonstrate that the proposed framework achieves a favorable balance among bit accuracy, edit deterrence, and rendering quality. These results suggest that practical copyright protection of 3DGS-based assets can be more effectively addressed by integrating ownership tracing and unauthorized editing deterrence into a single optimization framework.
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All authors Utae Jeong, Jaewan Choi, Junseok Lee, Jongheon Jeong, Sang Ho Yoon, ByoungSoo Koh, Sangpil Kim
Computer Vision
Adaptive Conformal Prediction for Reliable and Explainable Medical Image Classification
One Octadion, Novanto Yudistira and Lailil Muflikhah
Deep learning models for medical imaging often exhibit overconfidence, creating safety risks in ambiguous diagnostic scenarios. While Conformal Prediction (CP) provides distribution-free statistical guarantees, standard methods such as Regularized Adaptive Prediction Sets (RAPS) optimize for average efficiency and can mask severe failures on difficult inputs. We propose an Adaptive Lambda Criterion for RAPS that minimizes the worst-case coverage violation across prediction set size strata. On OrganAMNIST (58,850 abdominal CT images, 11 classes), standard size-optimized RAPS converges to near-deterministic behavior with stratified undercoverage on uncertain samples, while our method achieves 95.72 percent global coverage with average set size 1.09 and at least 90 percent coverage across all strata. Cross-domain validation on PathMNIST (107,180 pathology images, 9 classes) confirms generalizability. Quantitative Grad-CAM analysis (rho = -0.30, p < 1e-22) shows that multi-label predictions correspond to focused
attention on anatomically ambiguous regions. These results demonstrate that the proposed method improves reliability while maintaining efficiency, making it suitable for safety-critical medical AI applications.
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