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ECCV 2026

Disentangling and Reusing Interaction Cues for Zero-Shot HOI Detection

Jie Gu, He-yang Xu, Hongxiang Gao, Chengyu Liu

Zero-shot Human-Object Interaction (HOI) detection has achieved significant progress by leveraging the open-vocabulary capabilities of Vision-Language Models. However, the generalization of existing methods is severely restricted by a two-fold representational bottleneck. First, at the feature level, essential fine-grained interaction cues are frequently overshadowed by high-level global semantics. Second, at the decision level, models exhibit a strong background shortcut bias, often relying on scene co-occurrences rather than the actual humanobject interactions. To address these challenges, we propose a novel zeroshot HOI detection framework, DRIC, which is explicitly formulated for Disentangling and Reusing Interaction Cues. Specifically, we introduce the Context-Orthogonal Residual Disentangling (CORD) module, which projects features onto a global prototype subspace to decouple fine-grained residual details from global semantic biases, subsequently reintegrating them for enhanced local discriminability. Furthermore, the proposed Stratified Local Fusion (SLF) mechanism mitigates background shortcuts by aggregating multi-depth features from the image encoder of CLIP. This recovers essential contact-level details, guiding the model toward actual interactions. Extensive experiments demonstrate that DRIC consistently outperforms state-of-the-art methods across zero-shot evaluation settings, validating the robustness of the proposed framework in handling interaction ambiguity and cross-scene transfer.

ECCV 2026

Degradation-Agnostic Clarity Learning for Unpaired Image Dehazing

Zhiyuan Song, Hannan Lu, Haiqian Han, Bo Li, Chang Liu, Pengxu Wei, Xiangyang Ji, Liang Lin

Unpaired image dehazing models cast the restoration process as unsupervised domain translation, effectively avoiding the rigid physical assumptions of traditional methods. However, these methods frequently suffer from severe semantic hallucinations and content distortion. This stems from the significant semantic gap between unpaired clean and hazy domains, forcing the discriminator to overfit to high-level semantics rather than low-level clarity. In this paper, we propose Degradation-Agnostic Clarity Learning (DCL), a novel paradigm that steers the network to learn genuine low-level clarity. We introduce two core insights: (1) Explicitly injecting low-level degradations provides strong surface statistical shortcuts that effectively suppress high-level semantic interference; (2) Clarity is a relative concept, and various degraded variants of a clean image share a common intrinsic clarity direction. To realize this, we innovatively formulate discriminator training as a Multi-Task Learning (MTL) problem. First, we employ Adversarial AutoAugment to dynamically mine unlearned, diverse degraded clean samples by maximizing the discriminator’s loss. Second, we utilize Pareto Optimization to extract the shared common descent direction across degraded variants, thereby preventing the model from overfitting to any specific degradation type. Furthermore, we extend our core hypothesis to an Enhance-to-Resist self-supervised texture enhancement paradigm that benefits both dehazing and lowlight enhancement tasks. Extensive experiments on real-world datasets demonstrate that DCL significantly suppresses semantic hallucinations and achieves state-of-the-art performance in unpaired dehazing.

ECCV 2026

VideoSearch-R1: Iterative Video Retrieval and Reasoning via Soft Query Refinement

Seohyun Lee, Seoung Choi, Dohwan Ko, Jongha Kim, Hyunwoo Kim

As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video reasoning) and subsequently perform fine-grained, query-conditioned tasks (intra-video reasoning) within the retrieved content, such as temporal grounding. However, existing approaches typically treat retrieval as a preprocessing step, and consequently, when the initial retrieval fails, there is no mechanism to refine the search, leading to the failure of subsequent fine-grained intra-video reasoning. Moreover, while recent agentic frameworks have advanced video understanding, they typically assume that the queryrelevant video is already given, focusing exclusively on intra-video reasoning tasks. To address these limitations, we propose VideoSearch-R1, an agentic framework for iterative video retrieval and reasoning through multi-turn interaction with a video search engine. Specifically, we introduce Soft Query Refinement (SQR) to refine search query tokens in a continuous latent space rather than rewriting queries in the discrete text space, enabling more efficient and fine-grained adjustments. SQR and its reasoning process are trained using Group Relative Policy Optimization (GRPO), guided by task-level reward signals derived from retrieval and downstream tasks. Building upon this, VideoSearch-R1 achieves stateof-the-art performance across three datasets on Video Corpus Moment Retrieval (VCMR), iteratively retrieving videos from large-scale corpora, refining search queries, and performing precise query-conditioned temporal grounding within the retrieved content. Our analyses show that SQR effectively refines the original query, requiring significantly fewer generated tokens than explicit text-level query refinement. Code and model checkpoints are publicly available at mlvlab.github.io/VideoSearch-R1.

ECCV 2026

IndoorSplat: Enhanced Indoor Scene Reconstruction with Structured 2D Gaussian Splatting

Min Shi, Tao Yang, Xigang Zhao, Qi Wang, Dengming Zhu, Zhaoxin Li

Geometric reconstruction of indoor scenes poses unique challenges due to their inherent characteristics, such as large textureless surfaces, complex occlusions, and intricate geometric structures. While recent 2D/3D Gaussian Splatting-based methods have achieved impressive performance in general scene reconstruction, their direct application to indoor settings often leads to noisy, incomplete, or geometrically inconsistent results. In this paper, we propose a novel framework IndoorSplat to bridge this gap and enable high-quality indoor scene geometry reconstruction based on 2D Gaussian Splatting (2DGS). To this end, we first revisit the importance of Gaussian initialization in the indoor scene reconstruction process and introduce a carefully designed dense initialization strategy. This strategy leverages local geometric priors to densify point clouds estimated by an advanced feed-forward model and utilizes them to initialize the structure of Gaussian primitives. In addition, to better capture the rich structural details in indoor scenes, we introduce an iterative adaptive densification strategy that accelerates convergence in complex regions by applying more aggressive splitting and cloning policies to Gaussians with consistent activation. Extensive experiments demonstrate that our method achieves state-of-the-art performance by producing more geometrically accurate and complete surface reconstructions compared to existing approaches.

ECCV 2026

Same Person, Different Depiction: Counterfactual Evaluation of Vision-Language Models on Individuals with Limb Deficiencies

Heming Du, Jiaying Ying, Xin Chen, Sen Wang, Xue Li, Xin Yu

Vision-language models increasingly support everyday communication and decision-making from images. Their outputs shape how people talk about disability and can affect how people with disabilities are treated. Disability is common, but it is almost entirely absent from vision-side, controlled evaluations of model behavior. We ask a simple question: when a prompt is not about physical ability, should a model change its judgment just because visible limb-deficiency cues are present? A reliable model should base judgments and descriptions on task-relevant visual evidence, not on the mere presence of a disability cue. We introduce InclusiveCFImageBias, a benchmark of 924 images and more than 22k image-level queries. Each example contains two image versions that show the same person in the same scene, with and without visible limb-deficiency cues. We keep the background and activity fixed, and only edit cues such as a prosthesis or a residual limb. We query both versions with the same prompt and compare the outputs directly. Our evaluation spans both open-weight families (Qwen3-VL, DeepSeek-VL2, Gemma-3, and Ministral-3) and proprietary models (GPT-5 and Gemini-2.5). Many models shift both structured judgments and open-ended wording when limb-deficiency cues are visible. In our tests, these cues often push decisions toward more affirmative answers and higher ratings, and they make free responses less neutral and more evaluative. Higher scores are not automatically fairer. The key issue is that outputs change under a minimal, controlled visual substitution. InclusiveCFImageBias makes this cue-driven instability measurable. More importantly, it aims to draw community attention to disability-related VLM bias and provide a clear target for building more consistent disability-facing multimodal systems.

ECCV 2026

Foveated Reasoning: Stateful, Action-based Visual Focusing for Vision-Language Models

Juhong Min, Lazar Valkov, Vitali Petsiuk, Hossein Souri, Deen Dayal Mohan

Vision–language models benefit from high-resolution images, but the increase in visual-token count incurs high compute overhead. Humans resolve this tension via foveation: a coarse view guides “where to look”, while selectively acquired high-acuity evidence refines “what to think”. We introduce Foveated Reasoner, an autoregressive visionlanguage framework that unifies foveation and reasoning within a single decoding trajectory. Starting from a low-resolution view, the model triggers foveation only when needed, retrieves high-resolution evidence from selected regions, and injects it back into the same decoding trajectory. We train the method with a two-stage pipeline: coldstart supervision to bootstrap foveation behavior, followed by reinforcement learning to jointly improve evidence acquisition and task accuracy while discouraging trivial “see-everything” solutions. Experiments show that the method learns effective foveation policies and achieves stronger accuracy under tight visual-token budgets across multiple vision–language benchmarks.

ECCV 2026

What You Ask is What You Ground: Bridging Question Intent to Temporal Evidence for Grounded VideoQA

Jinhwan Seo, Kyu Han, Jumin Lee, Junhyug Noh, Sung-Eui Yoon

We study a critical yet overlooked failure mode in Grounded Video Question Answering: question-invariant grounding, where models predict nearly identical temporal segments for different questions about the same video. We trace this behavior to two structural limitations in prior common designs: (i) modality isolation that fixes video representations before they receive question semantics, and (ii) weak question injection inside the grounding module. To address this, we propose †Corresponding authors. 2 J. Seo et al. GroundFormer, which conditions video features on question intent before localization via learnable communication tokens that mediate directed visuo-lingual interaction. On top of the question-conditioned features, a factorized MIL cross-attention couples answer selection with temporal evidence under candidate-level supervision, while Gaussian smoothing converts peaked attention into temporally coherent segments. We further introduce a hierarchical multi-modal contrastive loss that aligns video, question, and answer embeddings across a two-pass training pipeline. GroundFormer achieves state-of-the-art grounded VideoQA performance on NExT-GQA and STAR, substantially improving question-discriminative temporal grounding.

ECCV 2026

Incentive Noise and Structural Prior Infusion for Multi-Modal Object Re-Identification

Weixiang Zhou, Yuhao Wang, Xingguo Xu, Cong Wang, Weizhen Zhou, Zhixun Su, Jinshan Pan

Multi-modal object Re-Identification (ReID) benefits from complementary information across heterogeneous imaging modalities. To further enrich semantic representation, text descriptions have recently been incorporated as an additional modality. However, recent visionlanguage approaches often treat text descriptions as clean, deterministic signals and overlook their inherent noise, including modality-mismatched phrases and semantically ambiguous expressions. Moreover, prevailing methods lack explicit mechanisms to reconcile fine-grained structural discrepancies between modalities, even after high-level semantic alignment. To address these challenges, we propose a novel framework centered on Positive-Incentive Noise (π-noise) and structured prompt modulation. First, the Semantic Cross-Modal Modulator harnesses task-aware π-noise, sampled from a distribution conditioned on both visual and text inputs, to perturb global tokens and enable semantics-guided crossmodal compensation. Second, the Structure-Aware Prompt Adapter injects learnable geometric priors via prompts to enhance spatial consistency. Third, the Context-Aware Sparse Fusion module distills structural context to guide adaptive fusion while shielding identity features from noisy local details. Experiments on three multi-modal ReID benchmarks demonstrate the effectiveness and robustness of our approach. The code is available at https://github.com/zw-absin/INSPI.

ECCV 2026

Mitigating Radar-Inertial Calibration Ambiguities via SO(3) Manifold Steering

Chunshen Li, Shengpeng Wang, Zitao Ye, Wei Wang

Reliable Radar-inertial fusion hinges on accurate extrinsic calibration, yet mmWave Radar poses unique calibration ambiguities: sparse and noisy returns, limited angular resolution, and a fundamental lack of stable geometric correspondences. Prior approaches typically rely on dedicated calibration targets, restrictive motion patterns, or strong scene assumptions, limiting their practicality for real-world deployment. In this paper, we present RadarCalib, a targetless, plug-and-play calibration framework that uses rigorous steering on the SO(3) manifold to establish reliable Radar-inertial constraints. Our key insight is that 3D rotational motion naturally induces a temporal synthetic aperture (TSA), which enables recovery of a fine-grained, high-resolution azimuthal spectrum. This TSA-enhanced representation reveals a highly stable rotationdependent azimuthal shift, which we use as a calibration signal to tightly couple Radar observations with IMU preintegration on the SO(3) manifold. Unlike point-based or motion-prior paradigms, RadarCalib establishes motion-consistent alignment in the spectral domain, regularizing SO(3) optimization and avoiding collapse induced by sparse Radar returns. Extensive real-world experiments show that RadarCalib achieves sub-degree rotational accuracy and outperforms state-of-the-art RadarIMU baselines in accuracy, robustness, and computational efficiency. Our self-collected dataset and code is released at https://github.com/ MetaIoT-WHU/RadarCalib.

ECCV 2026

Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation

Lucas Görnhardt, Timo Bartels, Niklas Schwarz, Tim Fingscheidt

Conventional one-hot encodings often yield poorly calibrated models, being overconfident under attack, and letting entropy-based detection algorithms fail. Previous image classification works have demonstrated that Hadamard-coded output representations can improve adversarial robustness. However, attempts to integrate Hadamard codes into semantic segmentation fall far behind state-of-the-art models in mean intersection-over-union performance. Regarding object detection, such output encodings have not yet been investigated at all. Further, no prior art addressed intrinsic codeword inconsistencies or actually exploited intrinsic codeword redundancy. Accordingly, we first derive a novel decoding procedure for Hadamard codewords towards optimal class-wise probabilities, solving the underlying optimization problem by using the projection onto the probability simplex. Second, our optimization delivers a measure of prediction inconsistency. Third, we are the first to show how to exploit these inconsistencies for adversarial attack and disturbance detection. Fourth, we introduce HadamardNet, a framework employing Hadamard codes as output representations for semantic segmentation and object detection models and tasks. We conduct a comprehensive evaluation both on disturbances and adversarial attacks, achieving state-of-the-art perturbation detection performance for both tasks in only a single detection pass, while delivering equivalent or close-by reference performance on clean data. Code is available at https://github.com/ifnspaml/HadamardPerturbationDetection.

ECCV 2026

This Looks Distinctly Like That: Grounding Interpretable Recognition in Stiefel Geometry against Neural Collapse

Junhao Jia, Jiaqi Wang, Yunyou Liu, Haodong Jing, Yueyi Wu, Xian Wu, Yefeng Zheng

Prototype networks provide an intrinsic case-based expla-nation mechanism, but their interpretability is often undermined byprototype collapse, where multiple prototypes degenerate to highly re-dundant evidence. We attribute this failure mode to the terminal dy-namics of Neural Collapse, where cross-entropy optimization suppressesintra-class variance and drives class-conditional features toward a low-dimensional limit. To mitigate this, we propose Adaptive Manifold Pro-totypes (AMP), a framework that leverages Riemannian optimizationon the Stiefel manifold to represent class prototypes as orthonormalbases and make rank-one prototype collapse infeasible by construction.AMP further learns class-specific effective rank via a proximal gradi-ent update on a non-negative capacity vector, and introduces spatialregularizers that reduce rotational ambiguity and encourage localized,non-overlapping part evidence. Extensive experiments on fine-grainedbenchmarks demonstrate that AMP achieves state-of-the-art classifica-tion accuracy while significantly improving causal faithfulness over priorinterpretable models.

ECCV 2026

RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control

Junpeng Yue, Zepeng Wang, Jiangxing Wang, Yuxuan Wang, Yu Zhang, Xinrun Xu, Bin Cao, Sipeng Zheng, gang ding, Zongqing Lu

This paper focuses on a critical challenge in robotics: translating text-driven generated human motions into executable actions for real humanoid robots. While existing text-to-motion (T2M) generation methods achieve semantic alignment between language and motion, they often produce physically infeasible motions unsuitable for real-world deployment. To bridge the gap between T2M and humanoid execution, we propose Reinforcement Learning from Physical Feedback (RLPF), a novel framework that integrates text-conditioned motion generation with motion-conditioned humanoid whole-body control. RLPF employs a low-level motion tracking policy to assess feasibility in physical simulators, providing rewards for (cid:28)ne-tuning the high-level motion generator. Moreover, RLPF introduces an alignment veri(cid:28)cation module to preserve semantic alignment with text instructions. This joint optimization ensures both physical feasibility and text alignment of T2M generators. Furthermore, to overcome the limitation of frozen tracking policy, we propose Hierarchical RLPF, a co-training framework that iteratively optimizes the high-level T2M generator and the low-level tracking policy. Extensive experiments show that RLPF and Hierarchical RLPF greatly outperform baseline methods in generating physically feasible motions while maintaining semantic alignment with text instructions. •

ECCV 2026

From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting

Zizhao Chen, Ping Wei, Guang Dai, Jingdong Wang, Mengmeng Wang

Video object removal is a fundamental yet challenging task in video editing. Despite recent progress, existing methods typically fall into two categories. Traditional approaches based on optical flow or attention mechanisms often introduce noticeable artifacts and yield unnatural results. In contrast, diffusion-based methods improve visual realism but demand multiple denoising steps, limiting their practicality. To address these issues, we propose From-Draft-to-Draft-Free (D2DF), a framework that distills the ability of transforming coarse drafts into refined videos into a one-step video generation model. Within D2DF, a teacher model is trained to refine low-quality removal results (“drafts”) into high-fidelity videos by multiple steps. Then, through Prior-Privileged Consistency Distillation (PPCD), we distill this capability into a student model that performs one-step removal conditioned on the draft. To eliminate draft dependency, we introduce a Self-Guided Fast Planting (SGFP) module based on our Temporal Masked Transformer that autonomously generates scene-consistent pseudo-drafts in latent space, enabling a fully draft-free one-step model. Extensive experiments show that both draft-conditioned and draft-free versions achieve state-of-the-art performance on multiple metrics, surpassing traditional and multi-step generative methods in both quality and efficiency. The denoising process for a single video takes only about 1 second.

ECCV 2026

MambaRaw: Selective State Space Modeling for Efficient 4K RAW Image Reconstruction

Peize Li, Fanhu Zeng, Tongda Xu, Xinjie Zhang, Xingtong Ge, Haotian Zhang, Xingguo Xu, Yan Wang

In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based reconstruction frameworks can exploit this side information when recovering raw images, their context models often become computationally expensive especially at high resolution, e.g., 4K raw image, given that attention mechanisms scale quadratically with feature maps, hindering its practical application. To address these limitations, we propose MambaRaw, a JPEG-conditioned metadatabased raw image reconstruction framework that uses State Space Models (SSMs) to estimate entropy parameters efficiently. Our key contribution comprises a Spatial-Energy Coupled Context Modeling mechanism with two lightweight modules: (1) TileMambaBlock, which performs Mambastyle selective scanning only on information-dense tiles to improve the efficiency; and (2) Energy-Aware Refinement (EAR), an identity-initialized residual module that enhance feature representation to match the longtail energy distribution of raw signals. Extensive experiments on three camera datasets (Sony, Olympus, Samsung) show consistent improvements over strong metadata-based baselines and set a new state of the art for JPEG-guided raw reconstruction with great efficiency. Notably, at low metadata bitrates, MambaRaw increases PSNR by 1.2–1.4 dB and reduces end-to-end coding latency by about 9%. Code is released at https://github.com/Peizeli1/MambaRaw.

ECCV 2026

Multi-View Foundation Models

Leo Segre, Or Hirschorn, Shai Avidan

Foundation models are vital tools in various Computer Vision applications. They take as input a single RGB image and output a deep feature representation that is useful for various applications. However, in case we have multiple views of the same 3D scene, they operate on each image independently and do not always produce consistent features for the same 3D point. We propose a way to lift a Foundation Model into a Multi-View Foundation Model. Such a model takes as input a set of images and outputs a feature map for each image such that the features of corresponding points are as consistent as possible. This approach bypasses the need to build a consistent 3D model of the features. Specifically, we show how to augment any Transformer-based foundation model (i.e., DINO, SAM, CLIP) with intermediate 3D-attention layers that help match features across different views. Quantitative evaluations demonstrate that our approach achieves superior 3D-awareness while best preserving the native semantic space of the original foundation model, outperforming existing lifting techniques.

ECCV 2026

PanoLess: Environment Reconstruction from Partial Reflective Views

Ahitagni Das, Ashok Veeraraghavan, Vivek Boominathan

Reflections from shiny objects and glass façades naturally extend the field of view of a camera, capturing the surrounding environment without the need to pan the camera or acquire a full panorama. We propose PanoLess, a Gaussian-splat–based framework that reconstructs the surrounding environment as a distant-illumination map from images captured on only one side of a reflective surface. PanoLess leverages surface-aligned 2D Gaussian splats with deferred shading to recover accurate per-pixel normals and reflection cues, which are fused into a neural cubemap representation of the environment. In addition, PanoLess produces a visibility map that explicitly denotes which regions of the environment are supported by the partial reflective observations. Unlike existing inverse-rendering and reflection-aware Gaussian-splatting approaches—which typically require full 360◦ coverage and struggle under incomplete views—PanoLess enables consistent, physically grounded illumination estimation from partial-view input. We show that PanoLess achieves high-fidelity and geometrically consistent environment reconstruction, outperforming reflection-aware baselines on a new custom synthetic benchmark and publicly available datasets, and demonstrating generalization to real-world reflective captures.

ECCV 2026

Score-Based Matching with Target Guidance for Cryo-EM Denoising

Xiaoqi Wu, Xueying Zhan, Wen Li, Junhao Wu, Xin Huang, Ke Ni, Min Xu

Cryo-electron microscopy (cryo-EM) enables single-particle analysis of biological macromolecules under strict low-dose imaging conditions, but the resulting micrographs often exhibit extremely low signalto-noise ratios and weak particle visibility. Image denoising is therefore an important preprocessing step for downstream cryo-EM analysis, including particle picking, 2D classification, and 3D reconstruction. Existing cryo-EM denoising methods are commonly trained with pixel-wise or Noise2Noise-style objectives, which can improve visual quality but do not explicitly account for structural consistency required by downstream analysis. In this work, we propose a score-based denoising framework for cryo-EM that learns the clean-data score to recover particle signals while better preserving structural information. Building on this formulation, we further introduce a target-guided variant that incorporates referencedensity guidance to stabilize score learning under weak and ambiguous signal conditions. Rather than simply amplifying particle-like responses, our framework better suppresses structured low-frequency background, which improves particle–background separability for downstream analysis. Experiments on multiple cryo-EM datasets show that our score-based methods consistently improve downstream particle picking and produce more structure-consistent 3D reconstructions. Experiments on multiple cryo-EM datasets show that our methods improve downstream particle picking and produce more structure-consistent reconstructions. Our code is available at https://github.com/xulabs/aitom.

ECCV 2026

PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Grounding

Haoze Zhang, Tianyu Huang, Zichen Wan, Xiaowei Jin, Hongzhi Zhang, Hui Li, Wangmeng Zuo

While recent video generation models have achieved significant visual fidelity, they often suffer from the lack of explicit physical controllability and plausibility. To address this, some recent studies attempted to guide video generation with physics-based rendering. However, these methods face inherent challenges in accurately modeling complex physical properties and effectively controlling the resulting physical behavior over extended temporal sequences. In this work, we introduce PhysChoreo, a framework that can generate videos with diverse controllability and physical realism from a single image. Our method consists of two stages: first, it estimates the static initial physical properties of all objects in the image through part-aware physical property reconstruction. Then, through temporally instructed and physically editable simulation, it synthesizes high-quality videos with rich dynamic behaviors and physical realism. Experimental results show that PhysChoreo can generate videos with rich behaviors and physical realism, outperforming state-of-the-art methods on multiple evaluation metrics.

ECCV 2026

Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detection

Gerhard Krumpl, Henning Avenhaus, Horst Possegger

Out-of-Distribution (OOD) detection remains a fundamental challenge for neural networks, whose predictions can be overconfident on inputs that deviate from the training distribution. Most post-hoc OOD detection methods derive scores from a single representation level (e.g., logits or penultimate features) or combine multiple layers via depth selection or OOD-calibrated weighting. However, because OOD shifts are diverse, the most informative representation level can vary strongly across OOD types and domains, making fixed-layer choices and OOD-calibrated aggregation brittle. In this paper, we propose PRISM (Projected Representation with Intermediate-layer Subspace Modeling), a model-agnostic post-hoc OOD detection method that models a unified multi-layer feature representation rather than aggregating independently scored layers. PRISM fuses intermediate and deep features into a single hierarchical embedding, estimates an in-distribution (ID) principal subspace, and then combines two complementary signals: (i) a class-conditional Mahalanobis distance in the projected subspace and (ii) the residual energy orthogonal to the learned manifold. This simple design avoids OOD-tuned layer weighting while capturing both in-subspace semantic deviations and offsubspace anomalies. Across diverse benchmarks spanning natural images, medical imaging, and industrial visual inspection, PRISM achieves consistent state-of-the-art cross-domain OOD detection performance with a single default configuration across all evaluated domains and architectures. We further show that PRISM incurs minimal inference overhead, making it practical for real-world deployment.

ECCV 2026

TEX-Drive: Temporal Perception Meets Experience-Guided Mixture-of-Experts for End-to-End Autonomous Driving

Yitong Li, Xuchong Zhang, Fanjie Kong, Weihuang Chen, Haonan Hou, Hongbin Sun

Human driving behavior embodies two core cognitive mechanisms: experiential reasoning and functional specialization. However, existing end-to-end autonomous driving (E2E-AD) frameworks typically model these two processes separately, often resulting in inconsistent decisions under long-horizon or dynamic conditions. To address this issue, we propose TEX-Drive, a unified E2E framework that integrates temporal perception with an experience-driven Mixture-of-Experts, achieving coordinated temporal perception–guided experience routing. Specifically, the temporal perception unit employs explicit key-frame selection to fuse critical information from both historical and current inputs, forming a temporally coherent scene representation. Building upon this, the experience-driven Mixture-of-Experts planning retrieves expert modules from a long-term memory space according to contextual similarity, dynamically routing the most relevant experts to adaptively generate future trajectories. This design enables the decision-making process to maintain temporal consistency while being guided by accumulated experiential knowledge. Extensive experiments on the Bench2Drive benchmark show that TEX-Drive consistently improves driving score, route completion, and behavioral robustness, substantially outperforming existing state-ofthe-art methods.

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