Computer Vision
Robust Onion: Peeling Open Vocab Object Detectors Under Noise
Priyank Pathak, Mukilan Karuppasamy, Aaditya Baranwal et al. (+2 more)
The impact of real-world noise on Open Vocabulary Object Detectors (OV-ODs) remains poorly understood due to their architectural complexity. We present our comprehensive analysis Robust Onion, an empirical study that uses controlled synthetic visual degradations to peel OV-ODs layer-by-layer, revealing how, why, and where robustness degrades, systematically analyzing feature collapse. Our findings reveal that models with similar
vision backbones exhibit comparable robustness, driven by similar feature collapse at similar layers, while factors such as pretraining strategy, architectural nuances, and caption supervision contribute little. Robustness is primarily governed by the image domain rather than annotations, explaining the similar robustness impact on COCO and LVIS, and why
datasets like ODinW-13 can give an impression of inflated robustness due to large, isolated objects. Finally, we validate our insights by improving robustness on real-world BDD100K, WiderFace, and VisDRONE via our lightweight plug-and-play NN & TK0 approach, using 96x fewer trainable parameters than end-to-end training. We also explain the prior works' robustness observations.
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All authors Priyank Pathak, Mukilan Karuppasamy, Aaditya Baranwal, Shruti Vyas, Yogesh S Rawat
Computer Vision
Full spectrum Unlearnable Examples via Spectral Equalization
Jiale Cai, Gezheng Xu, Zhihao Li et al. (+6 more)
Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In this paper, we reveal that existing UEs exhibit a critical failure once low-pass filtering is applied, indicating that the effective perturbation signals for unlearnability concentrate predominantly in high frequencies. Hence, we argue that reliable UEs should remain effective across the full spectrum. To this end, we propose Full-spectrum Unlearnable examples via Spectral Equalization (FUSE), which aims to generate spectrum-agnostic perturbations by equalizing the contributions from different bands and enforcing cross-band consistency. Specifically, FUSE adopts a Random Spectral Masking (RSM) strategy during generator training, which randomly removes a contiguous frequency band, forcing the remaining bands to maintain unlearnability. In addition, FUSE further integrates Cross-Band Guidance (CBG), which enforces mutual consistency between high- and low-frequency components, thereby further enhancing low-frequency unlearnability and regulating high-frequency perturbations to preserve the semantic fidelity of images. Extensive experiments across multiple
datasets, architectures, and spectral filtering demonstrate the strong protection achieved by FUSE.
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All authors Jiale Cai, Gezheng Xu, Zhihao Li, Ruiyi Fang, Ruizhi Pu, Di Wu, Qicheng Lao, Charles Ling, Boyu Wang
Computer Vision · Cross-listed from cs.AI
A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI
David Brüggemann, Ekaterina Krymova, Firat Özdemir et al. (+6 more)
Cardiac
magnetic resonance imaging (CMR) captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived indices from selected cardiac phases. We present a latent dynamical model that encodes bi-ventricular anatomy and full-cycle cine motion as a continuous latent trajectory, using heart-rate-aware neural ordinary differential equation (ODE) dynamics and a graph-based mesh autoencoder to reconstruct anatomically consistent 3D+t ventricular motion. A
covariate-conditioned prior defines the expected end-diastolic latent state, and a Cox proportional hazards model tests whether deviations from this prior predict incident heart failure. We studied 72,386 UK Biobank participants without baseline cardiovascular disease, including 367 incident heart failure events. In a held-out evaluation subset, adding the latent score to refitted pooled cohort equations improved the stratified C-index from 0.704 to 0.785, compared with 0.764 for seven established cardiac markers. Compared with non-graph and non-ODE approaches, the proposed model gave the best trade-off between reconstruction fidelity, generative realism, and downstream prognostic performance. These results suggest that continuous full-cycle modeling of ventricular motion provides informative cardiac phenotypes beyond conventional CMR summaries, while external validation in more representative patient cohorts is required before clinical risk-prediction use.
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All authors David Brüggemann, Ekaterina Krymova, Firat Özdemir, Jochen von Spiczak, Sebastian Kozerke, Samia Mora, Robert Manka, Mathieu Salzmann, Olga V. Demler
Computer Vision · Cross-listed from eess.IV
Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution
Haofei Song, Siyuan Xu, Xintian Mao et al. (+3 more)
Arbitrary slice super-resolution reconstructs isotropic volumes from anisotropic clinical acquisitions by synthesizing intermediate slices at arbitrary scales. However, treating this ill-posed inverse problem as unconstrained residual-based regression risks hallucinating anatomically implausible structures or altering the originally observed data. To address both concerns, this paper presents the Dual-Prior Null-space Learning (DP-NSL) framework, which reformulates the task as a constrained recovery process guided by two complementary priors. A Measurement-Consistent Projection (MCP) enforces a Deterministic Observation Prior: the reconstruction undergoes an exact orthogonal projection that reproduces every acquired slice with zero error, confining all learned details to the unobservable null space. Within this null space, a Mixture-of-Splines (MoS) module imposes a
Geometric Continuity Prior by dynamically mixing B-spline experts of different analytic orders, allowing each anatomical region to be modeled with a content-aware level of continuity. To promote spatial coherence, a Local Spatial Consistency Decoder (LSCD) further injects local inductive bias. Experiments on three CT and one MRI
benchmark show that DP-NSL outperforms existing approaches while strictly preserving measurement consistency. Code is available at https://github.com/DeepMed-Lab-ECNU/Medical-Image-Reconstruction.
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All authors Haofei Song, Siyuan Xu, Xintian Mao, Shaojie Guo, Qingli Li, Yan Wang
Computer Vision
Extracting Neural Materials from Images
Kim Youwang, Jon Hasselgren, Peter Kocsis et al. (+3 more)
Neural materials can represent complex specular reflections and scattering effects in a compact, universal basis. However, acquiring and authoring such materials remains challenging. We present NeuMatEx, a differentiable inverse rendering method for extracting spatially varying neural materials from images. The nonlinear structure of neural material latent spaces makes optimization with naive inverse rendering infeasible. To address this, we train a Large Material Reconstruction Model (LMRM) that directly predicts initial base color, neural material latents, and aleatoric uncertainty guides from images. This material prior provides a good initialization and better constrains our subsequent optimization using inverse path tracing. The predicted uncertainty further helps by anchoring high-confidence regions more tightly to the LMRM prediction, preventing lighting and complex specular effects from being baked into materials. Experiments on synthetic and real assets show that NeuMatEx extracts complex materials with better visual quality and material decomposition than PBR-based methods.
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All authors Kim Youwang, Jon Hasselgren, Peter Kocsis, Andrea Weidlich, Tae-Hyun Oh, Jacob Munkberg
Computer Vision · Cross-listed from eess.IV
MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation
Jingjun Gu, Chaojie Shen, Yifeng Cao et al. (+3 more)
Skin lesion
segmentation is a key task in computer-aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease classification. However, dermoscopic images are challenging due to blurred boundaries, low contrast, large shape variations, and artifacts such as hair and shadows. Recently,
diffusion models have shown strong performance in medical image
segmentation thanks to their progressive denoising and distribution modeling capabilities. Nevertheless, existing
diffusion-based methods still suffer from limited cross-level feature interaction and insufficient boundary detail recovery. To address these issues, we propose MLFFM-SegDiff, a multi-level feature fusion
diffusion model for skin lesion
segmentation. Built on a
diffusion framework, the method introduces a dual-path U-Net encoder, a Multi-Level Feature Fusion Module (MLFFM), and a boundary-sensitive loss function. The dual-path encoder enhances interaction between noisy mask features and dermoscopic image features. MLFFM improves skip connections via
attention, scale
alignment, and adaptive cross-level fusion. These designs enable the decoder to jointly leverage shallow boundary cues and deep semantic representations, improving mask reconstruction quality. Experiments on ISIC2018, PH2, and HAM10000 demonstrate that MLFFM-SegDiff outperforms representative methods including DermoSegDiff, U-Net, and SwinUNETR across Accuracy, F1-score, Jaccard index, Recall, and Dice. In particular, it achieves an average Jaccard index of 0.8546 and Dice coefficient of 0.9207. These results validate the effectiveness of the proposed multi-level feature fusion strategy for improving lesion
segmentation performance. The code will be released at https://github.com/Qacket/MLFFM-SegDiff.git after publication.
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All authors Jingjun Gu, Chaojie Shen, Yifeng Cao, Wei Zhang, Yiliu Li, Aobo Fan
Computer Vision
Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation
Quan Zhou, Shaoqing Zhai, Qiang Hu et al. (+3 more)
Transforming foundation
segmentation models from human-prompted tools into auto-promptable annotators is critical for scalable medical data annotation. Current methods commonly depend on external feature matchers or auxiliary networks to automate
geometric prompting, but introducing architectural overhead and limiting performance scalability. Although SAM3 natively supports concept
segmentation via reusable text
prompts, its direct use in medical imaging is hindered by a lack of fine-grained clinical knowledge and the ambiguity of human-written descriptions. In this work, we propose Mask to Concept (M2C), an efficient framework that adapts SAM3 for medical few-shot annotation without external modules, parameter retraining, or manual text engineering. Using only a few labeled images, M2C enables SAM3 to automatically search for transferable visual concepts entirely within its frozen architecture: it initializes a learnable concept
embedding, uses it to
prompt segmentation, and updates the
embedding by gradients of minimizing the concept
segmentation error. We further introduce a Hybrid Uncertainty Estimation (HUE) module that calculates the prediction
entropy and maps concept predictions back to the box
prompts, measuring concept-geometry prompting inconsistency. Highly uncertain samples are flagged actively for human correction, and the corrected masks are then fed back to M2C to continuously search for more precise concept
embeddings, forming a self-enhancing annotation loop with minimal expert effort. Experiments on medical
segmentation benchmarks show that our method achieves SOTA few-shot
segmentation performance and outstanding annotation efficiency, offering a practical and efficient pathway toward scalable medical image labeling. Codes are at https://github.com/Huster-Hq/M2C.
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All authors Quan Zhou, Shaoqing Zhai, Qiang Hu, Jia Chen, Qiang Li, Zhiwei Wang
Computer Vision
Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning
Pengcheng Shi, Kaiyuan Yang, Houjing Huang et al. (+7 more)
Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both aneurysm morphology and anatomical location, yet existing automated methods remain limited to binary detection without fine-grained anatomical classification or multi-class
segmentation. We present a multi-task framework that simultaneously performs multi-label classification, multi-class aneurysm
segmentation, and multi-class vessel
segmentation across 13 anatomical locations and four imaging modalities (CTA, MRA, T2, T1-post). Our two-stage approach combines a fast 2D tri-axial Region of Interest (ROI) extraction method with a 3D multi-task nnU-Net backbone. A dual-decoder design mitigates the extreme volume imbalance between aneurysm and vessel classes, while cross-
attention pooling and modality-specific auxiliary heads improve feature learning across heterogeneous inputs. Our two-fold ensemble achieved 2nd place in the RSNA 2025 Intracranial Aneurysm Detection challenge. Code, model weights, a 3D Slicer plugin, and the corrected
segmentation labels are publicly available.
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All authors Pengcheng Shi, Kaiyuan Yang, Houjing Huang, Jiawei Chen, Yan Lu, Jiaqi Liu, Murong Xu, Minghui Zhang, Bjoern Menze, Xinglin Zhang
Computer Vision
PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models
Bin Hu, Yanwen Ma, Jiehui Huang et al. (+14 more)
Recent game world models can synthesize visually plausible, action-conditioned rollouts. However, their interaction behaviors often remain limited to exploratory or wandering trajectories, and physical dynamics are typically learned as implicit correlations from data rather than as controllable variables. This limitation hinders their applicability to authored game environments, where physical rules are deliberately designed and require explicit manipulation. We introduce PhysEditWorld, a
multimodal dataset with physical parameters, with a primary focus on
gravity in this initial version. At its core, PhysEditWorld is built upon a replay paradigm implemented with a UE5 replay-and-rendering pipeline. Each scenario records a normalized action trace and replays the same initial state, character controller, action sequence, and camera policy under multiple
gravity configurations, enabling controlled and attributable physical variation. PhysEditWorld contains 12 cinematic UE5 scenes, over 100 hours of gameplay interactions, and more than 60 million rendered rollout frames. Each sample provides synchronized
multimodal signals, including RGB, depth, normals, audio, action traces, camera trajectory, engine states, semantic annotations, and explicit
gravity labels. We further conduct initial utility studies on both generative
video models and world understanding models, demonstrating that PhysEditWorld enables improved
gravity-faithful dynamics modeling, enhances consistency under physical edits, and provides a scalable foundation for controllable world modeling research.
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All authors Bin Hu, Yanwen Ma, Jiehui Huang, Ziliang Zhang, Haoning Wu, Ruicheng Zhang, Yaokun Li, Zijun Wang, Yuechen Zhang, Chun-Mei Tseng, Hanhui Li, Shengju Qian, Jun Zhou, Kaipeng Zhang, Xiaodan Liang, Jiaya Jia, Xiu Li
Computer Vision
DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection
Hun Im, Jungi Lee, Subeen Cha et al. (+1 more)
In industrial environments, new product categories arrive sequentially, requiring continual anomaly detection without access to past data. Normalizing Flows (NFs) provide exact density estimation but suffer from catastrophic forgetting as parameter updates across tasks distort the density
manifold. While parameter isolation can prevent interference, it must preserve the strict invertibility and Jacobian validity of NFs. To satisfy these requirements, we exploit the inherent property that affine coupling layers maintain transformation validity regardless of subnet parameterization. Based on this, we propose DeCoFlow, which decomposes subnets into a frozen universal base and task-specific low-rank adapters to isolate updates. We further introduce Task-Specific
Alignment, Auxiliary Coupling Layers, and Tail-Aware Loss to compensate for frozen-base
rigidity. DeCoFlow achieves state-of-the-art image-level AUROCs of 98.40% on MVTec-AD and 93.00% on VisA, while maintaining parameter-level zero forgetting (0.00% FM under correct routing) with only 2.27M parameters per task.
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All authors Hun Im, Jungi Lee, Subeen Cha, Pilsung Kang
Computer Vision
Disco-LoRA: Disentangled Composition of Content, Style, and Motion for Multi-concept Video Customization
Xuancheng Xu, Gengyun Jia and Bing-Kun Bao
Video customization based on Text-to-
Video (T2V) models aims to learn specific features from reference data to generate controllable
videos. While significant strides have been made in image stylization and
video motion customization, simultaneously controlling multiple concepts, such as content, style, and motion, remains a major challenge. In this work, we systematically define the task of multi-concept
video customization, which requires the joint control of content, style, and motion. To facilitate research in this area, we construct a comprehensive
benchmark and propose Disco-LoRA, a unified framework designed to tackle this problem by disentangling and flexibly recombining different concepts in two stages: (1) We decompose the objective into two sub-tasks: Content-Style and Content-Motion. Each sub-task is addressed using our Iterative Dual-LoRA Disentanglement Framework, which effectively disentangles distinct concepts within the data. (2) We identify layer-wise weight trends as crucial for LoRA identity, while weight magnitudes dictate composability. To harmonize these scales, we propose a Z-score-based statistical regularization that aligns weight distributions, preserving layer-wise trends while minimizing interference between different LoRAs. Extensive experiments show that Disco-LoRA excels in multi-concept
video customization, effectively preserving appearance, style, and motion for controllable text-to-
video generation.
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Computer Vision
LayersReg: A Layer-by-Layer Progressive Regressor for Reliable Intraoperative 3D/2D Registration
Xiyuan Wang, Zhenchao Wang, Xinran Chen et al. (+3 more)
3D/2D registration serves as a cornerstone technique in
surgical navigation. Traditional iterative optimization
algorithms suffer from low efficiency and high failure rates in intraoperative settings. Deep learning-based methods reformulate registration from iterative optimization to a regression problem that maps image appearance features to spatial pose, typically achieving improved real-time performance and accuracy. However, such learnable methods are confined to
memory-driven
retrieval of specific pose features rather than understanding the task of image
alignment itself, which limits their generalization in complex scenarios. We propose LayersReg, a pioneering regression paradigm that endows the model with 3D anatomical awareness and searches for the correct pose in a progressive, layer-by-layer manner. Inspired by the iterative pose-searching optimization criterion of classical registration, LayersReg searches for correlations between the moving and fixed images in feature space, capturing the trend of pixel flow and thereby converging iteratively toward the correct spatial pose transformation. We further design a coupling of node-wise regression with the progressive registration framework to enhance the model's perception of spatial pose changes. Experimental results demonstrate that under large offsets and multimodality conditions, LayersReg achieves high accuracy on both X-ray/CT registration (0.68{\deg}, 1.41 mm) and slice localization (0.73{\deg}, 1.55 mm) tasks, outperforming existing state-of-the-art methods while meeting the intraoperative demands for precision and real-time capability.
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All authors Xiyuan Wang, Zhenchao Wang, Xinran Chen, Junkai Liu, Chuan Chen, Feng Yin
Computer Vision
FracEvent: Event-Camera Simulation via Fractional-Relaxation Pixel Dynamics
Langyi Chen, Chuanzhi Xu, Haoxian Zhou et al. (+6 more)
Event cameras asynchronously report brightness changes with microsecond-level temporal resolution, but real event data remain difficult to collect at scale because specialized sensors, careful synchronization, and task-specific annotations are required. Event-camera simulation is therefore important to event-based
vision tasks. Most practical simulators build on contrast-threshold event generation, some with additional filtering,
stochastic noise, or calibrated sensor parameters. While effective, such formulations can simplify the temporal structure produced by a pixel's recent response history. We introduce FracEvent, a physics-inspired sensor-side simulator with compact fractional-relaxation voltage dynamics. Given a log-intensity trajectory, FracEvent combines fast and slow response modes, emits ON/OFF events by localizing threshold crossings on the continuous voltage trajectory, and updates the event reference while retaining the mode states. This retained state links residual response to later event timing. We evaluate FracEvent through direct event-stream comparison and downstream transfer on image reconstruction and optical flow estimation. Across multiple
datasets, FracEvent achieves leading results on multiple metrics in both evaluations, showing its practical value for event-camera simulation. Project page: https://bonaparte233.github.io/FracEvent/
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All authors Langyi Chen, Chuanzhi Xu, Haoxian Zhou, Pengfei Ye, Ziyu Luo, Haodong Chen, Qiang Qu, Xiaoming Chen, Weidong Cai
Computer Vision
Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions
Garam Kim and Juyoun Park
Effective multi-task learning for
surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks such as phase recognition, step recognition and anticipation benefit from dense frame-level supervision, whereas pixel-level spatial tasks including instrument
segmentation and action recognition are only sparsely annotated on selected keyframes due to prohibitive labeling costs. This supervision imbalance undermines shared representation learning and limits joint optimization across heterogeneous
surgical tasks. To address this, we propose Flow-guided Annotation for Robust Operating Scenes (FAROS), a flow-guided label interpolation framework, that combines zero-shot
segmentation-based mask propagation with optical flow estimation to overcome the limitations of appearance-based propagation under challenging
surgical conditions such as occlusion, smoke, and motion blur, generating temporally consistent dense pseudo labels from sparse keyframe annotations. The densified instrument masks and action labels are integrated into a unified
Transformer-based multi-task framework that jointly learns
surgical phase recognition, step recognition, anticipation, instrument
segmentation, and action recognition, enabling balanced optimization between dense temporal supervision and sparse spatial supervision. The label interpolation quality of FAROS is first validated on the DAVIS 2017
benchmark under a sparse ground-truth protocol, confirming robust propagation beyond the
surgical domain. Extensive experiments on GraSP, MISAW, and AutoLaparo
benchmarks further demonstrate that FAROS significantly improves cross-task representation learning and enhances holistic
surgical scene understanding performance across spatio-temporal tasks.
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Computer Vision
Position Rebinding Cache Reuse: Replay-Free Visual Revisiting for Interleaved Multimodal Reasoning
Mengzhao Wang, Yanli Ji, Wangmeng Zuo et al. (+2 more)
Interleaved
multimodal reasoning improves visual grounding by revisiting visual evidence during multi-step generation, yet existing methods typically rely on
token replay, repeatedly forwarding selected visual
tokens. A natural shortcut is to reuse the historical visual key-value (KV) cache directly. However, we identify a critical failure mode of this strategy: cached visual keys are already bound to their original positional context. Such stale positional binding distorts
attention under later decoding contexts and can trigger severe autoregressive decoding collapse. This failure suggests that effective cache reuse requires reconstructing visual evidence under positions compatible with the current decoding state, rather than directly copying position-bound historical cache entries. To this end, we propose Position Rebinding Cache Reuse (PRCR), a cache-level framework for replay-free visual revisiting. PRCR stores raw visual KV cache together with their original spatial coordinates, then reassigns position-compatible coordinates to select entries and rebinds their keys before injecting the reconstructed cache into the active decoder cache. This design reuses historical visual evidence while preserving textual positional continuity and relative visual structure. Experiments across multiple
multimodal reasoning benchmarks show that PRCR achieves replay-level or better performance, improving average accuracy by 5 percent and reducing visual-revisiting computation by up to tens of thousands of times.
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All authors Mengzhao Wang, Yanli Ji, Wangmeng Zuo, Peng Ye, Chongjun Tu
Computer Vision
TaskTok: Delving into Task Tokens for Task-driven Image Restoration
Hongjae Lee, Sojung Kang, Jaeseong Yu et al. (+1 more)
While traditional image restoration focuses on perceptual quality, Task-Driven Image Restoration (TDIR) aims to maximize the performance of downstream high-level
vision tasks. Recent approaches leveraging generative priors have shown promise for TDIR; however, they typically suffer from computational inefficiency and potential semantic alteration by indiscriminately updating all latent
tokens. In this paper, we posit that not all visual information is equally important for machine perception. Through an analysis of the latent
token space, we observe that task-relevant cues are unevenly distributed across the
token sequence, exhibiting index-wise specialization. This suggests that selectively refining a subset of
tokens can be sufficient for task-driven objectives. Leveraging this insight, we propose TaskTok, a novel framework that selectively restores only task-relevant
tokens via a learnable
token switch and a lightweight
token refinement module. Extensive experiments across image classification, semantic
segmentation, and object detection demonstrate that TaskTok significantly enhances task performance with high computational efficiency. The source code is available at https://github.com/jimmy9704/TaskTok
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All authors Hongjae Lee, Sojung Kang, Jaeseong Yu, Seung-Won Jung
Computer Vision
LogicIR: Logic Gate Networks for Image Restoration
Hongjae Lee, Myungjun Son, Jaeseong Yu et al. (+1 more)
Image restoration aims to reconstruct high-quality images from degraded low-quality inputs. As the computational demands of image restoration models continue to rise, there is growing interest in lightweight architectures optimized for fast and efficient inference. Logic gate networks (LGNs), which operate using fundamental logic operations such as NAND and XOR, have recently emerged as a promising direction for achieving highly efficient computation. However, their potential remains largely untapped in the domain of image restoration. In this work, we introduce LogicIR, the first LGN specifically designed for image restoration tasks. LogicIR incorporates a UNet-inspired architecture composed entirely of logic gates. In addition, we propose a differentiable bit decoding layer and an index shuffling mechanism that improves information propagation across logic gates. Experimental results across multiple image restoration
benchmarks demonstrate that LogicIR achieves strong performance with significantly reduced computational cost, establishing LogicIR as a viable and efficient alternative for image restoration. The source code is available at https://github.com/jimmy9704/LogicIR
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All authors Hongjae Lee, Myungjun Son, Jaeseong Yu, Seung-Won Jung
Computer Vision
DiCoBench: Benchmarking Multi-Image Fine-Grained Perception via Differential and Commonality Visual Cues
Geng Li and Yuxin Peng
Recent advancements in
Multimodal Large
Language Models (MLLMs) have demonstrated impressive fine-grained perception capabilities. However, existing
benchmarks predominantly rely on explicit textual cues or low-resolution inputs, failing to evaluate a model's ability to autonomously perceive implicit visual cues in high-resolution. To bridge this gap, we introduce DiCoBench, a comprehensive, multi-image high-resolution
benchmark designed for cross-image fine-grained perception. DiCoBench consists of 765 meticulously curated samples categorized into two progressive tracks: Differential Visual Cues and Commonality Visual Cues, covering 8 distinct perception tasks. By formulating the
benchmark as a multiple-choice question task and utilizing high-resolution imagery (approaching 2K), we eliminate evaluation metric bias and pose a substantial challenge to current state-of-the-art MLLMs. Our extensive evaluation of 18 diverse MLLMs reveals a striking performance gap compared to human accuracy (98.3%), with top-performing models struggling significantly with micro-scale detail capture. We believe DiCoBench will serve as a challenging testbed to drive future research in autonomous, high-resolution multi-image perception.
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Computer Vision
SpaceRipple: Lightweight Semantic Delivery for Mission-Oriented LEO Earth Observation Satellite Networks
Ziyi Yang, Hao Yuan, Yunxiang Yi et al. (+2 more)
Earth observation satellite networks generate massive volumes of high-resolution imagery, whereas inter-satellite and downlink resources remain limited. In many time-sensitive missions, ground users require mission-relevant semantic information rather than a full raw-image downlink. This paper proposes SpaceRipple, a lightweight framework for mission-oriented semantic delivery and on-board processing in Earth observation satellite networks. A sensing satellite performs adaptive compression and metadata generation to reduce inter-satellite traffic, while an edge computing satellite restores the received representation and extracts task-relevant semantic information. Unlike fidelity-driven image transmission, SpaceRipple coordinates compression, forwarding, restoration, and semantic inference within a collaborative pipeline, enabling semantic-oriented delivery instead of pixel-level image delivery. A compression-aware MoE enhancement module is further introduced to improve robustness under degraded visual inputs. Experimental results show that SpaceRipple achieves favorable reconstruction quality, improved semantic detection performance, and substantial bandwidth savings, demonstrating its potential for efficient and reliable Earth observation under constrained satellite-network resources.
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All authors Ziyi Yang, Hao Yuan, Yunxiang Yi, Wenbo Wang, Xing Zhang
Computer Vision
Coarse-to-Fine: A Hybrid Self-Supervised Method for Non-rigid 3D Shape Matching
Feifan Luo, Ting Li, Zhao Li et al. (+1 more)
Non-rigid 3D shape matching is a fundamental task in computer
vision and graphics. In this paper, we propose a hybrid self-supervised method based on a coarse-to-fine strategy, which ensures consistency between the coarse mapping and the refined correspondence produced by our refinement module. The architecture features a dual-branch design, consisting of two symmetric functional map learning streams: one based on the
Laplacian basis and the other utilizing the elastic basis. Extensive experiments show that our approach not only maintains computational efficiency, but also achieves state-of-the-art performance across a variety of challenging scenarios, including non-isometric deformations and topological noise. Finally, we rigorously demonstrate that contrastive energies promote feature discrimination. Furthermore, integrating these energies with existing methods yields consistent improvements, validating the overall efficacy of our approach. Our code is available at https://github.com/LuoFeifan77/Coarse-to-Fine-Hybrid-Self-Supervised-Matching.
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All authors Feifan Luo, Ting Li, Zhao Li, Hongyang Chen
Computer Vision
Perception, Verdict, and Evolution: Hindsight-Driven Self-Refining Forensics Agent for AI-Generated Image Detection
Yangjun Wu, Keyu Yan, Yu Liu et al. (+5 more)
The rapid advancement of generative models presents a significant challenge to existing deepfake detection methods, particularly given the widespread dissemination of highly realistic AI-generated images. Although
Multimodal Large
Language Models (MLLMs) show strong potential for this task, existing approaches suffer from two key limitations: insufficient sensitivity to fine-grained forensic artifacts and reliance on static synthetic supervision from frontier models, leading to limited flexibility and high-cost. To address these issues, we propose ForeAgent, an agentic forensics framework for AI-generated image detection with iterative self-
evolution. First, ForeAgent adopts a Perception-Verdict architecture that aggregates multi-view cues spanning semantic, spatial, and frequency-domain features, and leverages an MLLM as a verdict module to fuse these signals for a logical-grounded verdict. Second, to enable continual self-improvement, we introduce a Hindsight-Driven Self-Refining strategy following a Sampling-Reflection-
Evolution paradigm. The
agent performs inference rollouts on training instances. Guided by ground-truth labels as hindsight, it reflects on failure cases and low-quality
reasoning trajectories to regenerate higher-quality
reasoning traces. These synthesized samples are then strictly filtered through a dual-expert quality gating module. ForeAgent continuously evolves via fine-tuning on self-curated high-quality samples. Extensive experiments demonstrate that ForeAgent achieves state-of-the-art performance on the Chameleon
benchmark, reaching 82.18% accuracy (+16.41% over AIDE), and achieves 93.3% mean accuracy on AIGCDetect-
Benchmark across 16 generators. In addition, external evaluation shows that ForeAgent produces more consistent and causally grounded
reasoning compared to GPT-5 and GPT-5-mini.
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All authors Yangjun Wu, Keyu Yan, Yu Liu, Jingren Zhou, Fei Huang, Rong Zhang, Zhou Zhao, Fei Wu
Computer Vision
PhyEditBench: A Real-World Multi-Stage Benchmark for Physics-Aware Image Editing
Shengbin Guo, Shaokang He, Chaoyue Meng et al. (+4 more)
While instruction-based image editing, enabled by multi-modal generative models, has advanced significantly, existing
benchmarks lack a comprehensive evaluation of physics-based
reasoning, a critical capability for handling real-world scenarios. To address this, we introduce PhyEditBench, a
benchmark designed to assess the physical understanding of editing models. Guided by a hierarchical taxonomy, we establish 4 primary classes and 12 subclasses. It comprises 238 high-quality, high-resolution, real-world instances meticulously extracted from
videos to capture authentic physical dynamics, alongside 35 synthetic Anti-Physics instances. Our empirical analysis of current SOTA editing methods exposes substantial limitations in their physics-based
reasoning. We further propose a training-free baseline named PhyWorld that uses test-time scaling and a latent reduction strategy. PhyWorld outperforms comparable models and suggests that the
video generation process can effectively serve as a
reasoning mechanism for image editing. The project page is available at https://github.com/Previsior/PhyEditBench.
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All authors Shengbin Guo, Shaokang He, Chaoyue Meng, Shengpeng Xiao, Xunzhi Xiang, Shaofeng Zhang, Qi Fan
Computer Vision
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
Zhixing Li and Yinan Yu
Current VLM evaluations often conflate
language priors with genuine spatial
reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the
alignment between implicit perception and explicit
reasoning. Unlike traditional black-box QA, CRISP utilizes metric 3D Scene Graphs and an oracle intervention protocol to decouple latent
reasoning capabilities from perceptual bottlenecks. This granular diagnosis uncovers a systematic perception-
reasoning disconnect. Crucially, we reveal that while proprietary models possess robust latent
reasoning engines, they suffer from inaccurate metric estimation and a critical failure to leverage their implicit structural representations. Conversely, open-source models remain fundamentally bottlenecked by their lack of multi-hop compositional
reasoning. By shifting the focus from merely “guessing correctly” via
language priors to genuinely “perceiving, verifying, and
reasoning,” CRISP offers a rigorous roadmap for
multimodal alignment beyond end-to-end post-training. The code and
dataset are available at https://github.com/iiyamayuki/CRISP-Bench.
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Computer Vision · Cross-listed from cs.CL
The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals
Kwan Soo Shin
AI in radiology and other safety-critical workflows is evaluated on the hazards it is told to find, yet harm arises disproportionately from hazards no one specified. We show that conditioning a
language or
vision model on a narrow task suppresses its reporting of co-present, safety-critical signals it can otherwise report, a behavioral analogue of human inattentional blindness. Across radiology text scenarios and thoracic-image
vision tasks, ordinary focused instructions suppressed reporting by up to 0.92; the gap ranged from minimal to complete across seven models, did not vary monotonically with scale, and persisted in a
reasoning model, while one flagship model showed a robust safety-reporting override. We term this dissociation the Inattentional Gap: a system can score near-perfectly on specified hazards while omitting co-present safety-critical hazards. In a 24-scenario probe, an independent open-ended critic restored every omitted finding. We propose reporting-complete evaluation as an admission criterion for safety-critical deployment.
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Computer Vision
Forget, Anticipate and Adapt: Test Time Training for Long Videos
Rajat Modi, Sebastian Noel, Xin Liang et al. (+1 more)
Test Time Training (TTT) is a mechanism in which a model adapts to an incoming test-sample by performing some self-supervised (SSL) task and updating its weights even during inference. This procedure does not require labels at test-time. This paper focuses on TTT for long-
videos. A major concern with existing approaches is: 1) they perform TTT updates using a sliding window containing frames in the past, whose compute increases linearly with the size of window. This becomes computationally intractable when the
videos are hours long. 2) TTT is performed even when temporally close frames look similar, thereby consuming a lot of compute. We present the Frame Forgetting Network (FFN) that: 1) operates on only three frames within the sliding window, namely the frame that exits, the current frame and the frame after that. The model still manages to retain temporal context and work for hours long-
videos; 2) mathematically define a surprise metric: how much new information the incoming frame contains with respect to the past seen frame. This facilitates determining how to modify the effective window size during TTT and constitutes the core mechanism of an adaptive windowing
algorithm. Additionally, we curate a
dataset EpicTours containing up to 3 hour long
videos of walking city-tours, whereas earlier
datasets on this problem were only 5 min long. We demonstrate FFNs empirical effectiveness on dense-
segmentation,
video classification tasks, generalization to depth-estimation, and multi-hour long
videos.
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All authors Rajat Modi, Sebastian Noel, Xin Liang, Yogesh Singh Rawat
Computer Vision · Cross-listed from cs.HC
Budget-Aware Keyboardless Interaction
Quang-Thang Nguyen, Gia-Phuc Song-Dong, Minh-Triet Tran et al. (+1 more)
Interacting with computers typically relies on traditional input devices such as keyboards, mice, and monitors, which can be cumbersome for users seeking greater mobility. Virtual keyboards have been explored to address these limitations, but they often involve complex setups or expensive equipment. This paper proposes a novel virtual keyboard system that leverages only a standard camera and a paper with a printed keyboard layout. Unlike previous methods requiring complex calibration or special lighting conditions, our approach can work on standard environment using modern computer
vision technologies. Combining modern
segmentation and detection models with traditional image processing
algorithms, we efficiently identify the keyboard region. Touch detection is performed using an
algorithm analyzing the color of the user's fingernail. Experiments demonstrated a promising results our proposed solution of keyboard and keystroke detection for practical applications. Participants attended our user study also found the proposed system interesting.
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All authors Quang-Thang Nguyen, Gia-Phuc Song-Dong, Minh-Triet Tran, Trung-Nghia Le
Computer Vision · Cross-listed from cs.HC
DanceDuo: Bridging Human Movement and AI Choreography
Gia-Cat Bui-Le, Tuong-Vy Truong-Thuy, Hai-Dang Nguyen et al. (+1 more)
In recent years, advancements in deep learning and generative models have revolutionized music-driven dance generation. This paper introduces a novel platform, namely DanceDuo, leveraging
diffusion models to generate AI-choreographed dance sequences synchronized with a variety of music genres, to encourage dancing practice. The system allows users to interact with AI by selecting music tracks,
humanoid models, and importing personal dance
videos for comparison, fostering a rich and engaging user experience. DanceDuo not only offers dance generation but also integrates human pose estimation models to provide users with insightful comparisons of their own performances with AI-generated sequences. We conducted a comprehensive user study, revealing that users found the interface intuitive, with particular praise for the dance comparison feature. Our DanceDuo contributes significantly to the integration of AI in dance choreography, offering novel avenues for both recreational and professional applications.
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All authors Gia-Cat Bui-Le, Tuong-Vy Truong-Thuy, Hai-Dang Nguyen, Trung-Nghia Le