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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

Measuring 3D Spatial Geometric Consistency in Dynamic Video Generation

Weijia Dou, Wenzhao Zheng, Weiliang Chen, Yu Zheng, Jie Zhou, Jiwen Lu

Recent generative models can produce high-fidelity videos, yet they often exhibit 3D spatial geometric inconsistencies. Existing evaluation methods fail to accurately characterize these inconsistencies: fidelity-centric metrics like FVD are insensitive to geometric distortions, while consistency-focused benchmarks often penalize valid foreground dynamics. To address this gap, we introduce SGC, a metric for evaluating 3D Spatial Geometric Consistency in dynamically generated videos. We quantify geometric consistency by measuring the divergence among multiple camera poses estimated from distinct local regions. Our approach first separates static from dynamic regions, then partitions the static background into spatially coherent sub-regions. We predict depth for each pixel, estimate a local camera pose for each subregion, and compute the divergence among these poses to quantify geometric consistency. Experiments on real and generative videos demonstrate that SGC robustly quantifies geometric inconsistencies, effectively identifying critical failures missed by existing metrics.

ECCV 2026

UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters

Yongkun Du, Zhineng Chen, Yazhen Xie, Weikang Bai, Hao Feng, Wei Shi, Yuchen Su, Can Huang, Yu-Gang Jiang

Text and formulas constitute the core informational components of many documents. Accurately and efficiently recognizing both is crucial for developing robust and generalizable document parsing systems. Recently, vision-language models (VLMs) have achieved impressive unified recognition of text and formulas. However, they are largesized and computationally demanding, restricting their usage in many applications. In this paper, we propose UniRec-0.1B, a unified recognition model with only 0.1B parameters. It is capable of performing text and formula recognition at multiple levels, including characters, words, lines, paragraphs, and documents. To implement this task, we first establish UniRec40M, a large-scale dataset comprises 40 million text, formula and mixed samples, enabling the training of a powerful yet lightweight model. Secondly, we identify two challenges when building such a lightweight but unified expert model. They are: structural variability across levels and semantic entanglement between textual and formulaic content. To tackle these, we introduce a hierarchical supervision training that explicitly guides structural comprehension, and a semantic-decoupled tokenizer that separates text and formula representations. Finally, we develop a comprehensive evaluation benchmark covering Chinese and English documents from multiple domains and with multiple levels. Experimental results on this and public benchmarks demonstrate that UniRec-0.1B outperforms both general-purpose VLMs and leading document parsing expert models, while achieving 2-9× speedup, validating its effectiveness and efficiency. Codebase and Dataset: https://github.com/Topdu/OpenOCR.

ECCV 2026

SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark

Sania Bano, Shahzad Ahmad, Santosh Kumar Vipparthi, Sukalpa Chanda, Subrahmanyam Murala

Understanding human emotions in spoken conversations is a key challenge in affective computing, with applications in empathetic AI, human computer interaction, and mental health monitoring. However, existing datasets vary in scale, emotion distribution, modality alignment, and data partitioning strategies, which can influence reliable crossdataset generalization and minority-emotion modeling. We introduce SpEmoC a Speaking segment Emotion for Conversations comprising 306,544 raw clips from 3,100 English language movies and TV series. From these, 30,000 high quality, class balanced clips are curated, featuring synchronized visual, audio, and textual modalities annotated for seven emotions through a hybrid pipeline that integrates pretrained models with human validation. SpEmoC uses strict movie and series-level splits to prevent content overlap between split sets, allowing more reliable evaluation of model generalization. The dataset also maintains a nearbalanced distribution across seven emotions, including minority classes such as Fear and Disgust, which supports more balanced learning across categories. Extensive experiments, including in-domain benchmarking, cross-dataset transfer, low-data training, class-imbalance analysis, and modality transfer show that balanced data and careful splitting lead to more stable performance across emotions when models are evaluated on other datasets. These results highlight the importance of dataset design for robust and transferable multimodal emotion recognition.

ECCV 2026

Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation

Zhaowen Zhu, Li Zhang, Chen Yujie, Zhang Tian, Yingjie Wang, Mingxia Zhan

Self-Supervised Monocular Depth Estimation (MDE) has garnered attention in recent years due to its independence from ground truth. However, most existing models are limited to a single scale and exhibit considerable performance degradation in complex driving environments. Networks specifically designed to handle dynamic traffic participants tend to be overly complex, hindering their deployment on resource-constrained automotive edge devices. To address these limitations and move towards robust driving perception, we propose FlexDepth, a scale-driven and flexible family of self-supervised MDE models tailored for challenging road scenarios. FlexDepth employs a two-stage static-dynamic decoupled training strategy, enabling the independent assessment of confidence for both static backgrounds and dynamic road objects. Furthermore, it introduces a meticulously designed Scale-Driven Decoder (SDD) to dynamically select components based on scale size, facilitating efficient feature fusion and the output of high-precision depth maps. Extensive experiments on standard driving benchmarks demonstrate that without any auxiliary information, our model achieves state-of-the-art performance across arbitrary scales with minimal computational overhead. Our smallest model, Flex-Nano, requires only 0.7 GFLOPs and achieves 37.6 FPS on mobile platforms, ensuring reliable real-time perception while maintaining excellent zero-shot generalization. Our source code is avalible: https://github.com/startnew/flexdepth

ECCV 2026

TaxoGrasp: Taxonomy-Guided Human Grasp Synthesis with Sparse Contact Constraint

Haitian Liu, Yin Wang, Zhiying Leng, Kanglei Zhou, Yan Wang, Frederick W. B. Li, Xiaohui Liang

We tackle controllable human grasp synthesis under sparse user-specified thumb-contact constraints. While existing methods use contact as conditioning, they lack explicit semantic grounding, yielding weak contact-intent coupling, poor constraint sensitivity, and pose drift. We propose TaxoGrasp, a framework threading Feix grasp taxonomy as a persistent intent bottleneck throughout: (1) intent inference via supervised taxonomy prediction from object geometry and thumb-contact constraint, (2) diffusion synthesis conditioned on taxonomy templates, and (3) anchor-optimized grounding preserving taxonomy structure. We augment HOGraspNet with patch-level thumb-contact annotations and introduce a standardized evaluation protocol with three metric categories. Experiments demonstrate improved intent consistency, physical plausibility, and constraint adherence.

ECCV 2026

GKDT: General Keypoint Detection Transformer

Changsheng Lu, Yuxin Chen, Haokun GUI, Rong Wang, Jie Yang, Harry Yang, Anton van den Hengel, Jiaya Jia

With the emergence of various pre-trained vision and language models, computer vision is shifting from narrow-domain to opendomain recognition. The construction of a more powerful yet general keypoint detection (GKD) model to support diverse tasks has become increasingly important in the field. To this end, we firstly present a largescale unified keypoint dataset called MegaKPT. The dataset is composed of over 1.3 million diverse object instances from twenty-nine existing datasets, and enjoys high-quality unified annotations with keypoint text descriptions. Based on MegaKPT, we develop GKDT, a simple, flexible and powerful DINOv3 based Transformer model for General Keypoint Detection. Our GKDT supports visual prompts, text prompts, or both. To enhance model training, we also propose a suite of useful strategies such as mix-modal prompted training and dynamic importance sampling. By testing over 22 test sets with seen or unseen objects, our single GKDT model shows strong performance and generality in detecting keypoints on broad categories, with most categories over 90% PCK@0.1 accuracy, offering high practical applicability to real-world problems. The dataset, models, and codes will be released at https://github.com/AlanLuSun/General-Keypoint-Detection.

ECCV 2026

AViTS：Adaptive Spatiotemporal Token Selection for Efficient Dynamic-Resolution Generation

Haoran Qin, Zhengan Yan, Shikang Zheng, Xiaobing Tu, Jiacheng Liu, Yuqi Lin, Chang Zou, Jinshan Liu, Peiliang Cai, Xiantao Zhang, Jinkui Ren, Linfeng Zhang

Di(cid:27)usion Transformers (DiTs) achieve high-quality generation but are costly due to iterative sampling. Dynamic-resolution sampling reduces early-stage cost by denoising at low resolution; however, uniformly upsampling all latent tokens at resolution transitions incurs redundant computation and may degrade (cid:28)ne-detail consistency. Existing partial upsampling strategies typically rely on local latent structure cues or single-step statistics, making it di(cid:30)cult to jointly capture token(cid:21)text semantic relevance and token-wise representation dynamics across diffusion steps. We propose AViTS, an adaptive spatiotemporal token selection framework for dynamic-resolution DiTs. AViTS models spatial importance via latent(cid:21)text attention and temporal importance via token-level feature variation across di(cid:27)usion timesteps, and fuses them to enable spatiotemporal importance-aware selective upsampling: it prioritizes resolution re(cid:28)nement for critical tokens while deferring less important ones, thereby reducing redundant high-resolution computation and improving the quality(cid:21)e(cid:30)ciency trade-o(cid:27). AViTS achieves up to 6.34× on FLUX and nearly 9× FLOPs reduction on Qwen-ImageEdit and FLUX.1-Kontext-dev, orthogonal to distillation, quantization, and feature caching, and reaching 14.76× with distilled models. Code: https://github.com/QHR69/AViTS.

ECCV 2026

On the Faithfulness of Post-Hoc Concept Bottleneck Models

Laines Schmalwasser, Jan Blunk, Niklas Penzel, Julia Niebling, Joachim Denzler

Human decision-making interprets the world through highlevel concepts, such as recognizing a bird by its belly color. To bridge the gap between opaque deep learning representations and human understanding, Post-Hoc Concept Bottleneck Models (post-hoc CBMs) project latent features onto interpretable concept spaces using auxiliary datasets or vision-language models. However, relying on target task accuracy as the primary measure of post-hoc CBM success obscures whether the learned concepts are semantically meaningful or merely predictive artifacts. For example, random concept projections can achieve competitive accuracy despite being semantically meaningless. In this work, we analyze the learned projections directly and identify two failure cases: First, for concept projections learned from auxiliary data, covariate shifts can lead to unfaithful concept representations for the target task. In particular, we provide an upper bound on the error introduced by this shift. Second, systematic label noise in surrogate concept labels generated by visionlanguage models leads to unfaithful projections. After formalizing these failure modes, we introduce novel metrics that decouple concept faithfulness from predictive accuracy. Our empirical results across real-world and synthetic benchmarks confirm that these metrics identify unfaithful behaviors that standard accuracy-based evaluation fails to detect4.

ECCV 2026

Closing the Capacity–Convergence Gap: Globally Optimal Configuration of Implicit Neural Representations

Sipeng Chen, Yan Zhang, Shibo Li

Implicit Neural Representations (INRs) achieve state-of-theart results across image representation, 3D modeling, and novel-view synthesis, yet their performance hinges on tightly coupled design decisions— activation family, initialization scheme, and per-layer hyperparameters— whose interdependencies render manual tuning unreliable and greedy search provably suboptimal. We introduce OptiINR, the first framework that recasts INR configuration as a global optimization problem over a mixed-variable space of discrete activation choices (e.g., SIREN, WIRE, FINER) and their continuous parameters. OptiINR consists of a structured layer-wise INR configuration space and an adaptive Bayesian optimization procedure for navigating it. The search space spans activation families, activation-specific continuous parameters, initialization/scaling choices, per-layer learning rates, and positional-encoding usage. Across five tasks—1D audio, 2D image, 3D occupancy, spatiotemporal PDE, and neural radiance field synthesis—OptiINR consistently outperforms handtuned baselines and prior automated methods, revealing task-dependent activation patterns that challenge prevailing one-size-fits-all design conventions. Code is available at https://github.com/Sippengg/OptiINR.

ECCV 2026

EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents

Xuan Dong, Huanyang Zheng, Tianhao Niu, Zhe Han, Pengzhan Li, Bofei Liu, Zhengyang Liu, Guancheng Li, Qingfu Zhu, Wanxiang Che

Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across papers to align experimental settings and support reproducible conclusions. This joint capability is not systematically assessed in existing benchmarks, which largely underevaluate proactive search, multi-evidence integration and sustained evidence use over time. In this work, we introduce EpiBench, an episodic multi-turn multimodal benchmark that instantiates short research workflows. Given a research task, agents must navigate across papers over multiple turns, align evidence from figures and tables, and use the accumulated evidence in the memory to answer objective questions that require cross-paper comparisons and multi-figure integration. EpiBench introduces a process-level evaluation framework for fine-grained testing and diagnosis of research agents. Our experiments show that even the leading model achieves an accuracy of only 29.23% on the hard split, indicating substantial room for improvement in multi-turn, multievidence research workflows. Code and data are available at https: //github.com/RetroDnix/EPIBench.

ECCV 2026

FitControler: Toward Fit-Aware Virtual Try-On

lu Yang, Yicheng Liu, Letian Zhou, Yanan Li, Xiang Bai, Hao Lu

Realistic virtual try-on (VTON) concerns not only faithful rendering of garment details but also coordination of the style. Prior art typically pursues the former, but neglects a key factor that shapes the holistic style—garment fit. Garment fit delineates how a garment aligns with the body of a wearer and is a fundamental element in fashion design. In this work, we introduce fit-aware VTON and present FitControler, a learnable plug-in that can seamlessly integrate into modern VTON models to enable customized fit control. To achieve this, we highlight two challenges: i) how to delineate layouts of different fits and ii) how to render the garment that matches the layout. FitControler first features a fitaware layout generator to redraw the body-garment layout conditioned on a set of delicately processed garment-agnostic representations, and a multi-scale fit injector is then used to deliver layout cues to enable layoutdriven VTON. In particular, we build a fit-aware VTON dataset termed Fit4Men, including 13,000 body-garment pairs of different fits, covering both tops and bottoms, and featuring varying camera distances and body poses. Two fit consistency metrics are also introduced to assess the fitness of generations. Extensive experiments show that FitControler can work with various VTON models and achieve accurate fit control. Code and data are released at https://github.com/tiny-smart/FitControler.

ECCV 2026

3D-LENS: A 3D Lifting-based Elevated Novel-view Synthesis method for Single-View Aerial-Ground Re-Identification

William Grolleau, Astrid Sabourin, Guillaume Lapouge, Catherine Achard

Aerial-Ground Re-Identi(cid:28)cation (AG-ReID) is constrained by the viewpoint-domain gap, as drastic viewpoint disparities occlude or distort discriminative features, making cross-viewpoint image retrieval challenging. While existing methods rely on paired cross-view annotations, real-world deployments, such as wilderness search-and-rescue (SAR), often lack target-domain data, requiring retrieval from groundlevel references alone. To our knowledge, we are the (cid:28)rst to address this challenge by formalizing the Single-View AG-ReID (SV AGReID) setting, where models trained on a single real viewpoint must generalize to an unseen viewpoint. We propose 3D Lifting-based Elevated Novel-view Synthesis (3D-LENS), a uni(cid:28)ed framework combining geometrically-consistent novel view synthesis that leverages large-scale 3D mesh reconstruction, with a robust representation learning scheme to mitigate synthetic-to-real bias. Unlike 2D generative baselines that su(cid:27)er from geometric inconsistencies or prior 3D methods that are restricted to class-speci(cid:28)c templates, our approach ensures view-consistent synthesis across diverse categories without prede- (cid:28)ned templates that fail to capture (cid:28)ne-grained details, such as carried objects. Extensive experiments demonstrate that our method achieves state-of-the-art performance on SV AG-ReID scenarios. Code and data will be released at https://github.com/TurtleSmoke/3D-LENS. • •

ECCV 2026

Exposing Implicit Vulnerabilities in Text-to-Image Models via Adversarial Agentic Probing

Chang Ma, Junlin Han, Shuo Chen, Runjia Li, Philip Torr, Jindong Gu

Red-teaming Text-to-Image (T2I) models is essential for safedeployment, yet it remains particularly challenging against implicit ad-versarial prompts. Unlike explicit adversarial prompts that can be read-ily identified and blocked, implicit ones are much harder to detect: theprompts appear benign on the text surface yet still lead to inappropri-ate visual content. To address this, we propose Adversarial Probing forImplicit VulnErabilities (AdvPIE), a multimodal agentic framework toexpose implicit vulnerabilities without requiring access to the parametersof target models. AdvPIE adopts a policy agent to generate and refineimplicit adversarial prompts based on the feedback from a judge agent.To construct informative feedback, the judge agent provides modality-specific safety evaluation at both global and relative levels across itera-tions. To effectively leverage the feedback, we propose a novel CumulativeAdversarial Decoding strategy for the policy agent, which dynamicallyreweights token distributions to favor tokens that lead to more harmfulimages while preserving sampling diversity. Extensive experiments onstandard and safety-aligned T2I models show that AdvPIE1 effectivelyuncovers implicit vulnerabilities, outperforming various baseline meth-ods. Warning: This paper includes content that may be disturbing.

ECCV 2026

Attention-DP3: Spatially Object-aware 3D Diffusion Policy via Geometry-aligned Attentional Conditioning

Changbo Yan, Zhongbo Zhang, Zaibin Zhang, Yifan Wang, Lijun Wang, Huchuan Lu

3D point-cloud observations are inherently ambiguous in complex, cluttered manipulation scenes, where target objects may be partially occluded or tightly intermingled with visually similar distractors. As a result, standard 3D diffusion policies often struggle to localize and exploit task-relevant geometry as scene complexity grows. We propose Attention-DP3, a spatially object-aware 3D diffusion policy that injects object-level geometric cues via attention while keeping the DP3 diffusion backbone unchanged. Our pipeline performs open-vocabulary 2D segmentation on RGB images, then lifts predicted target masks into 3D using calibrated camera geometry to obtain object-centric geometric priors. We incorporate these cues through Tri-field Attentional Conditioning, which constructs three complementary fields: (i) a targetness field to anchor the target object, (ii) an intra-target saliency field to emphasize task-relevant geometry within the target, and (iii) a backgroundness field to suppress distractors and clutter. Experiments on Adroit, DexArt, MetaWorld, and the real-world SO101 platform show consistent improvements over DP3, achieving state-of-the-art performance across benchmarks. Notably, as distractor objects increase, DP3 drops sharply, whereas Attention-DP3 remains stable and outperforms DP3 by up to 31% under heavy clutter. The code is publicly available at https:// github.com/zhangzhongbo2213/Attention-DP3.

ECCV 2026

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

Revant Teotia, Adrien Bardes, Michael Rabbat, Sumit Chopra, Matthew Muckley, Nicolas Ballas

Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in video data, extending this success to jointly learn from both modalities is a natural next step, yet it remains challenging. Existing audio-visual self-supervised methods rely on modality-specific encoders and complex combinations of contrastive or reconstruction objectives, limiting cross-modal synergy and scalability. Joint Embedding Predictive Architectures (JEPAs) offer a simple, modality-agnostic alternative, but have to date been applied primarily to individual modalities. We introduce MJEPA, a jointembedding predictive architecture for audio-visual learning that uses a single, unified encoder for both modalities. Our approach uses only a single predictive objective, applied both within and across modalities. We show that cross-modal prediction is critical: without it, a shared encoder degrades below unimodal baselines; with it, each modality’s representation benefits from the other. Our frozen ViT-g model outperforms the best prior frozen baseline by over 6.8 mAP on AudioSet-20K, surpasses fully finetuned models on ESC-50 and FSD50K, and is competitive on video benchmarks despite using 10x less video data.

ECCV 2026

Towards Practical Lossless Neural Compression for LiDAR Point Clouds

Pengpeng Yu, Haoran Li, Runqing Jiang, Dingquan Li, Jing Wang, Liang Lin, Yulan Guo

LiDAR point clouds are fundamental to various applications, yet the extreme sparsity of high-precision geometric details hinders efficient context modeling, thereby limiting the compression speed and performance of existing methods. To address this challenge, we propose a compact representation for efficient predictive lossless coding. Our framework comprises two lightweight modules. First, the Geometry Re-Densification Module iteratively densifies encoded sparse geometry, extracts features at a dense scale, and then sparsifies the features for predictive coding. This module avoids costly computation on highly sparse details while maintaining a lightweight prediction head. Second, the Cross-scale Feature Propagation Module leverages occupancy cues from multiple resolution levels to guide hierarchical feature propagation, enabling information sharing across scales and reducing redundant feature extraction. Additionally, we introduce an integer-only inference pipeline to enable bit-exact cross-platform consistency, which avoids the entropy-coding collapse observed in existing neural compression methods and further accelerates coding. Experiments demonstrate competitive compression performance at real-time speed. Code is available at https://github.com/pengpeng-yu/FastPCC.

ECCV 2026

SPQR: A Multi-Dimensional Benchmark for Safety Alignment under Benign Model Adaptation

Mohammed Talha Alam, Nada Saadi, Fahad Shamshad, Nils Lukas, Karthik Nandakumar, Fakhri Karray, Samuele Poppi

Text-to-image diffusion models can emit copyrighted, unsafe, or private content. Safety alignment aims to suppress specific concepts, yet evaluations seldom test whether safety persists under benign downstream fine-tuning routinely applied after deployment (e.g., LoRA personalization, style/domain adapters). We study the stability of current safety methods under benign fine-tuning and observe frequent breakdowns. As true safety alignment must withstand even benign post-deployment adaptations, we introduce the SPQR benchmark (Safety–Prompt adherence–Quality–Robustness). SPQR is a single-scored metric that provides a unified, reproducible framework to evaluate how well safety-aligned diffusion models preserve safety, utility, and robustness under benign fine-tuning, by reporting a single leaderboard score to facilitate comparisons. We conduct multilingual, domain-specific, and out-of-distribution analyses, along with category-wise breakdowns, to identify when safety alignment fails after benign fine-tuning, ultimately showcasing SPQR as a concise yet comprehensive benchmark for T2I safety alignment techniques for T2I models. Warning: This paper features illustrative examples that may involve explicit, sexual, or violent imagery and language, which could be sensitive for certain readers.

ECCV 2026

From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology

Basit Alawode, Moshira Abdalla, Dwarikanath Mahapatra, Muhammad Muzammal Naseer, Sajid Javed

Vision Transformers (ViTs) and their hierarchical variants have achieved strong performance in Computational Pathology (CPath). However, most are pre-trained on single-resolution Whole Slide Images (WSIs), limiting their generalization across arbitrary resolutions. Gigapixel WSIs inherently contain diagnostic patterns at multiple scales, including cellular morphologies, tissue architectures, and global context, mirroring how expert pathologists examine WSIs. We introduce MultiResolution Pyramid Transformer (MRPT), a model that hierarchically aggregates multi-resolution information from cellular to tissue and WSI levels. MRPT employs a biologically meaningful Consecutive Cross-Resolution Attention (CCRA) mechanism to capture scale-independent interactions and enforces multi-resolution semantic consistency by aligning embeddings across resolutions, yielding robust and generalizable WSI representations. Pre-trained in a multi-resolution self-supervised manner on 624M patches, 2.4M regions, and 36K WSIs, MRPT learns rich coarse-to-fine histopathology features. Extensive experiments on 34 diverse datasets show that MRPT surpasses recent foundation models and Multimodal Large Language Models (MLLMs) in cancer subtype classification, tissue phenotyping, and Visual Question Answering (VQA) for WSI understanding. Our code and models available on link.

ECCV 2026

Visual Spatial Tuning

Rui Yang, Ziyu Zhu, Yanwei Li, Jingjia Huang, Shen Yan, Siyuan Zhou, Zhe Liu, Xiangtai Li, Shuangye Li, Wenqian Wang, Yi Lin, Hengshuang Zhao

Capturing spatial relationships from visual inputs is a cornerstone of human-like general intelligence, yet current Vision-Language Models (VLMs) still struggle to perform spatial understanding from everyday visual observations. To enhance the spatial ability within general architectures, we introduce Visual Spatial Tuning (VST), a comprehensive framework to cultivate VLMs with human-like visuospatial competence spanning spatial perception and reasoning. VST dataset comprises VST-P, a large-scale perception dataset with 4.1 million samples spanning 19 skills across single views, multiple images, and videos; and VSTR, a reasoning dataset with 135K samples that explicitly instruct models to reason in space. For spatial reasoning, we design Reconstruction with Text in Chain-of-Thought (RT-CoT), which represents scene layout as a textual intermediate and performs reasoning grounded on this reconstruction. We further propose a strategy termed prompting with BEV annotations to improve the faithfulness and coherence of spatial reasoning traces. Then, we adopt a progressive training pipeline: supervised fine-tuning to build foundational spatial perception abilities, followed by reinforcement learning to develop spatial reasoning, where a 3D-aware layout reward is proposed. Without the side-effect on general capabilities, the proposed VST consistently achieves state-of-the-art results on several spatial benchmarks, including 34.8% on MMSI-Bench and 61.2% on VSIBench. Moreover, VST substantially improves Vision-LanguageAction (VLA) models, paving the way for more physically grounded AI.

ECCV 2026

Improving Adversarial Robustness by Mitigating Instability through Relearning

Yi Zeng, Ling Zhou, Ruilong Yu, Qihe Liu, Shijie Zhou

Adversarial training is the most effective defense against adversarial attacks, but it often suffers from robust overfitting, whose causes remain unclear. In this paper, we observe a counterintuitive phenomenon: the model robust stability consistently deteriorates during training. To quantify this behavior, we introduce the Robust Stability Rate (RSR) and define robust instability. Our analysis demonstrates that improving the Robust Stability Rate can effectively reduce robust instability, thereby mitigating robust overfitting. Building on this insight, we propose a novel framework called Relearning Adversarial Training (RAT). Our min-max optimization establishes a new adversarial dynamic where generated adversarial examples induce robust instability, while model training enforces robust stability, aiming to enhance the model Robust Stability Rate of the current learning model by leveraging knowledge from historical model. Experimental results demonstrate that RAT can be integrated with various methods to significantly enhance their performance. It effectively reduces robust instability and mitigates robust overfitting.

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