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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

Masked Depth Modeling for Spatial Perception

Bin Tan, CHANGJIANG SUN, Xiage Qin, Hanat Adai, Zelin Fu, Tianxiang Zhou, Han Zhang, Yinghao Xu, Xing Zhu, Yujun Shen, Nan Xue

Spatial visual perception is a fundamental requirement in physical-world applications like autonomous driving and robotic manipulation, driven by the need to interact with 3D environments. Capturing pixel-aligned metric depth using RGB-D cameras would be the most viable way, yet it usually faces obstacles posed by hardware limitations and challenging imaging conditions, especially in the presence of specular or texture-less surfaces. In this work, we argue that the inaccuracies from depth sensors can be viewed as “masked” signals that inherently reflect underlying geometric ambiguities. Building on this motivation, we present MDM, a depth completion model which leverages visual context to refine depth maps through masked depth modeling and incorporates an automated data curation pipeline for scalable training. It is encouraging to see that our model outperforms top-tier RGB-D cameras in terms of both depth precision and pixel coverage. Experimental results on a range of downstream tasks further suggest that MDM offers an aligned latent representation across RGB and depth modalities. Code, checkpoints, and 3M RGB-depth pairs (including 2M real data and 1M simulated data): https://github.com/robbyant/lingbot-depth.

ECCV 2026

Geo-DPO: Aligning Semantic Intent with Geometry for 3D Affordance Segmentation

Ziqian Yang, Xiaolei Wang, Xianglin Qiu, Weiguang Zhao, Quan Zhang, Jimin XIAO

3D sequential affordance reasoning requires a model to locate a logical series of functional parts based on language instructions. Current Multimodal Large Language Models (MLLMs) tackle this by generating a special <SEG> token to guide the final mask prediction. However, compressing complex reasoning into a single token loses crucial low-level structural details. This leads to severe geometric ambiguity, causing the predicted masks to have inaccurate boundaries. To solve this, we propose the Geo-DPO framework, which addresses the ambiguity from two directions. First, we introduce the Hierarchical Geometry Adapter (HGA) to structurally inject multi-scale 3D physical features back into the <SEG> token. Second, we propose Contrastive Affordance Preference Optimization (CAPO), a reward-based training objective that forces the segmentation to align with real geometric boundaries. Extensive experiments on the standard SeqAfford dataset demonstrate that our method achieves state-of-the-art performance, generating highly precise masks and effectively resolving geometric ambiguity. Code is available here.

ECCV 2026

Reliability-Aware 3D Geometric Injection for Universal Person Re-identification

Bohan Su, Jiashuo Wang, Fangyi Liu, Mang Ye

Universal person re-identification (ReID) aims to retrieve pedestrian identities across diverse real-world scenarios, including severe occlusions, clothing changes, and cross-modality shifts, within a unified model. However, existing 2D representations fundamentally struggle with spatial ambiguities due to a lack of depth and topological awareness, while naively introducing monocular 3D priors often causes severe negative transfer due to geometric estimation noise under extreme visual degradation. To safely harness the clothing-invariant and canonical structural properties of 3D geometry, we propose UniGeo, a Universal Monocular 3D-Enhanced ReID framework driven by a ConsistencyAware Reliability Gate and Dual-Stream Residual Fusion. Specifically, the processing of 3D information is strategically decoupled into geometric extraction and dynamic utilization. To provide pure structural compensation, we project monocular 3D parameters into kinematic joint representations, explicitly capturing instance-level geometric topology to resolve appearance-based ambiguities. To robustly incorporate these cues without perturbing the reliable 2D feature space, we isolate the 3D prior as a late-stage structural residual; modulated by the consistency-aware gate, this mechanism adaptively filters geometric noise and enables controlled fallback to the pure 2D baseline. Extensive experiments show that our method improves challenging, structure-sensitive scenarios while preserving competitive performance on clean domains. Code is available at https://github.com/BohanSu/UniGeo.

ECCV 2026

WebRetriever: A Large-Scale Comprehensive Benchmark for Efficient Web Agent Evaluation

Wei Dong, Tianyu Fu, Zhe Yu, Hanning Wang, Anyang Su, Zhizhou Fang, Yuyang Chen, Shuo Wang, Minghui Wu, Ping Jiang, Zhen Lei, Chenxu Zhao

As web agents increasingly demonstrate capabilities in automated task execution, the development of robust evaluation frameworks for assessing their navigation and task completion performance has emerged as a critical research priority. However, existing benchmarks exhibit several fundamental limitations. First, they suffer from insufficient scale and limited domain diversity, thereby constraining comprehensive evaluation of cross-domain generalization. Second, prevailing LLM-asJudge evaluation methodologies inadequately capture fine-grained interaction semantics, particularly regarding precise query formulation and filtering operations. Third, current benchmarks predominantly emphasize navigation success metrics while neglecting critical requirements for realworld deployment scenarios. To address these limitations, we introduce WebRetriever, a large-scale benchmark encompassing 800 websites and 1,550 tasks across diverse domains, including consumer, professional, and enterprise sectors, with comprehensive coverage of user intent patterns. We propose NavEval (Navigation Evaluation), a novel LLM-as-Judge framework that leverages rich interaction context beyond visual screenshots, achieving state-of-the-art alignment with human judgment across multiple evaluation datasets. Furthermore, we establish three complementary evaluation protocols that collectively provide holistic assessment of web agent capabilities: navigation proficiency, knowledge-assisted interaction, and end-to-end task completion with information extraction. Extensive experimental analysis reveals substantial performance disparities across evaluation protocols, demonstrating that navigation success alone serves as an insufficient predictor of real-world application effectiveness. WebRetriever delivers fine-grained diagnostic insights into agent capabilities and establishes a rigorous foundation for advancing web agent research and development.

ECCV 2026

Autoregressive Image Generation Needs Only a Few Lines of Cached Tokens

Ziran Qin, Youru Lv, Mingbao Lin, Zeren Zhang, Chaofan Gan, Tieyuan Chen, Liquan Shen, Junhui Hou, Chern Hong Lim, Fei Wen, Weiyao Lin

Autoregressive (AR) visual generation has emerged as a powerful paradigm for image and multimodal synthesis, owing to its scalability and generality. However, existing AR image generation suffers from severe memory bottlenecks due to the need to cache all previously generated visual tokens during decoding, leading to both high storage requirements and low throughput. In this paper, we introduce LineAR, a novel, training-free progressive key-value (KV) cache compression pipeline for AR image generation. By exploiting the intrinsic characteristics of visual attention, LineAR manages the cache from a 2D line-level perspective, preserving the visual dependency regions while progressively evicting less-informative tokens under inter-line attention guidance, leveraging the spatial continuity prior of images to ensure that each eviction step is harmless to subsequent line generation. Experiments across seven AR image generation models validate that LineAR achieves lossless or even improved generation quality with only a few cached lines. It improves ImageNet FID from 2.77 to 2.68 on LlamaGen-XL and COCO FID from 23.85 to 22.86 on Janus-Pro-1B at 1/6 budget ratio, and also improves DPG and HPSv2.1 scores on Lumina-mGPT-768 with just 1/8 KV cache. Additionally, LineAR achieves significant memory and throughput gains across devices, e.g., up to 67.06% memory reduction and 4.17× speedup on LlamaGen-XL, and 73.65% memory reduction and 4.34× speedup on Janus-Pro-1B, evaluated on NVIDIA RTX PRO 6000 GPUs.

ECCV 2026

S-VAM: Shortcut Video-Action Model by Self-Distilling Geometric and Semantic Foresight

Haodong Yan, Zhide Zhong, Jiaguan Zhu, Junjie He, Weilin Yuan, Wenxuan Song, Xin Gong, Yingjie CAI, Guanyi Zhao, Xu Yan, Liu Bingbing, Yingcong Chen, Haoang Li

Video action models (VAMs) have emerged as a promising paradigm for robot learning, owing to their powerful visual foresight for complex manipulation tasks. However, current VAMs, typically relying on either slow multi-step video generation or noisy one-step feature extraction, cannot simultaneously guarantee real-time inference and high-fidelity foresight. To address this limitation, we propose S-VAM, a shortcut video-action model that foresees coherent geometric and semantic representations via a single forward pass. Serving as a stable blueprint, these foreseen representations significantly simplify the action prediction. To enable this efficient shortcut, we introduce a novel self-distillation strategy that condenses structured generative priors of multi-step denoising into one-step inference. Specifically, vision foundation model (VFM) representations extracted from the diffusion model’s own multi-step generated videos provide teacher targets. Lightweight decouplers, as students, learn to directly map noisy one-step features to these targets. Extensive experiments in simulation and the real world demonstrate that our S-VAM outperforms state-of-the-art methods, enabling efficient and precise manipulation in complex environments. Our project page is https://haodong-yan.github.io/S-VAM/.

ECCV 2026

IRIS: Intersection-aware Ray-based Implicit Editable Scenes

Grzegorz Wilczyński, Mikołaj Zieliński, Krzysztof Byrski, Joanna Waczynska, Dominik Belter, Przemysław Spurek

Neural Radiance Fields achieve high-fidelity scene representation but suffer from costly training and rendering, while 3D Gaussian splatting offers real-time performance with strong empirical results. Recently, solutions that harness the best of both worlds by using Gaussians as proxies to guide neural field evaluations, still suffer from significant computational inefficiencies. They typically rely on stochastic volumetric sampling to aggregate features, which severely limits rendering performance. To address this issue, a novel framework named IRIS (Intersection-aware Ray-based Implicit Editable Scenes) is introduced as a method designed for efficient and interactive scene editing. To overcome the limitations of standard ray marching, an analytical sampling strategy is employed that precisely identifies interaction points between rays and scene primitives, effectively eliminating empty space processing. Furthermore, to address the computational bottleneck of spatial neighbor lookups, a continuous feature aggregation mechanism is introduced that operates directly along the ray. By interpolating latent attributes from sorted intersections, costly 3D searches are bypassed, ensuring geometric consistency, enabling high-fidelity, real-time rendering, and flexible shape editing.

ECCV 2026

X-SG2S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks

Zihang Cheng, Wentao Bao, Huiping Zhuang, Chun Li, Xin Meng, Ziqian Zeng, Cen Chen, Ming Li, Fei Yu

3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. However, the rapid adoption of 3D Gaussian Splatting raises growing concerns about information leakage and unauthorized use, urging the exploration of effective watermarking techniques. However, existing methods are limited by low capacity, fragility under geometric perturbations, and the infeasible requirement for costly fine-tuning or pipeline modifications, motivating the need for a generalizable, feed-forward framework capable of robust multi-modal embedding with minimal intrusion. In this paper, we propose a new framework X-SG2S which can simultaneously inject 1D to 3D watermarks for copyright protection, while keeping the high fidelity of original 3DGS scenes. Specifically, we first split the watermarks into message patches. A self-adaptive gate is developed to select the injection positions of the watermark messages. Then, we use an XD (multi-dimensional) injection head to inject multi-modal messages into sorted 3DGS points. To restore watermarking messages, a learnable gate is developed to recognize the watermarked locations, from which our XD-extraction heads are used to restore hidden messages. X-SG2S is the first framework to unify 1Dto-3D watermarking and enable simultaneous multi-modal watermark embedding in 3DGS, achieving this with minimal rendering interference and zero modifications to parameters or pipelines. Extensive experiments demonstrate that X-SG2S effectively preserves consistency between the watermark and the original 3DGS, exhibits robustness against model degradation, and maintains accurate judgment capabilities.

ECCV 2026

FMA-Net++: Motion- and Exposure-Aware Joint Video Super-Resolution and Deblurring

Geunhyuk Youk, Jihyong Oh, Munchurl Kim

Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure-duration variation, which changes the extent of motion blur across video frames. We propose FMA-Net++, a non-recurrent, sequence-level framework built from Hierarchical Refinement with Bidirectional Aggregation (HRBA) blocks. By stacking HRBA blocks, FMANet++ processes video frames in parallel while hierarchically expanding the temporal receptive field, avoiding the limited temporal receptive field of sliding-window designs and the sequential bottleneck of recurrent ones. To handle exposure-duration-dependent blur, we introduce an Exposure Time-aware Modulation (ETM) layer that conditions HRBA features on exposure embeddings from an Exposure Time-aware Feature Extractor (ETE). The conditioned features guide an exposure-aware flow-guided dynamic filtering module to predict motionand exposureaware degradation kernels. FMA-Net++ decouples degradation learning from restoration: the former predicts degradation priors and the latter exploits them for efficient high-resolution restoration. To evaluate VSRDB under controlled exposure-duration variation, we introduce the REDS-ME (multi-exposure) and REDS-RE (random-exposure) benchmarks. Trained solely on synthetic data, FMA-Net++ achieves stateof-the-art accuracy and temporal consistency on these benchmarks. It further shows strong out-of-distribution performance on GoPro and challenging real-world videos, while outperforming recent methods in both restoration quality and inference speed.

ECCV 2026

Prevention over Correction: Learning Aligned Representations in One-shot Federated Learning

YongHoon Kang, Jee-Hyong Lee

One-shot Federated Learning (OFL) reduces communication costs by requiring only a single round of model exchange. However, when clients train independently on heterogeneous data, their learned representations develop different means and variances, creating distributional mismatch that prevents effective server-side aggregation. Existing methods attempt post-calibration at the server, but struggle to reconcile representations that have already diverged into incompatible statistical spaces. We propose FACE (Federated Aligned-Consistent Ensemble) to prevent distributional divergence during client training rather than correcting it afterward. FACE uses a shared fixed reference matrix to guide representation means toward consistent directions, applies a variance stabilizer to maintain consistent feature variance, and performs featurefusion ensemble inference. Extensive experiments on CIFAR-10/100 and Tiny-ImageNet demonstrate that FACE achieves state-of-the-art performance, with particularly strong improvements under extreme data heterogeneity. Comprehensive ablation studies validate the necessity of both components for achieving distributional alignment.

ECCV 2026

EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models

Xuanlang Dai, Yujie Zhou, Long Xing, Jiazi Bu, Xilin Wei, Yuhong Liu, Beichen Zhang, Kai Chen, Yuhang Zang

Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibit insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process. Invariant guidance during decoding prevents DiT from progressively decomposing complex instructions into actionable denoising steps, even with correct MLLM encodings. To this end, we propose Endogenous Chain-of-Thought (EndoCoT), a novel framework that facilitates structured visual reasoning in MLLMs by iteratively refining latent thought states through an iterative thought guidance module, and then bridges these states to the DiT’s denoising process. Second, a terminal thought grounding module is applied to ensure the reasoning trajectory remains grounded in textual supervision by aligning the final state with ground-truth answers. With these two components, the MLLM text encoder delivers meticulously reasoned guidance, enabling the DiT to execute it progressively and ultimately solve complex tasks in a step-by-step manner. Extensive evaluations across diverse benchmarks (e.g., Maze, TSP, VSP, and Sudoku) achieve an average accuracy of 92.1%, outperforming the strongest baseline by 8.3 percentage points.

ECCV 2026

EDM:Event-guided Diffusion Model for Video Shadow Detection in Complex Dynamic Scenes

Boyang Gao, Jiayi Guo, Ziyu Wang, Yingbin Cui, Zhan Tu

Video shadow detection (VSD) aims to robustly identify shadow regions in videos and serves as a key task for improving scene understanding. Although substantial progress has been made in recent years, existing methods still suffer notable performance degradation in challenging scenarios involving fast motion and drastic illumination changes. To address these limitations, we introduce event cameras into the VSD task and propose an Event-guided Diffusion Model (EDM) tailored for complex dynamic environments. Based on the high temporal resolution and asynchronous sensing characteristics of event cameras, we design a Motion Trajectory Attention (MTA) module and a Bidirectional Mask Generation (BMG) module to capture event-driven temporal cues. These cues are further integrated through a Dual-Modal Temporal Guidance (DMTG) module and injected into the diffusion model as conditional guidance to steer the denoising process. By incorporating event streams as a complementary modality, EDM effectively captures fast scene dynamics and abrupt lighting variations, leading to more robust and accurate video shadow detection. Furthermore, to address the lack of event modalities in existing VSD benchmarks, we construct a multimodal extension of the ViSha dataset, termed Event-ViSha, and collect a new real-world multimodal dataset, the Event-based Video Shadow Dataset (EVSD). Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches across multiple benchmarks.

ECCV 2026

CMDR: Contextual Multimodal Document Retrieval

Ryota Tanaka, Taku Hasegawa, Kyosuke Nishida

Multimodal document retrieval aims to retrieve relevant pages while preserving both textual and visual content from the original document. However, existing benchmarks primarily evaluate simple lexical or semantic matching, and most methods encode pages independently. Consequently, they overlook the contextual information in the document required to resolve queries that aggregate information across multiple pages. In this paper, we introduce CMDR and CMDR-Bench, a new multimodal document retrieval task and benchmark that require modeling document context. To address this challenge, we propose CMDR-Embed, a contextual multimodal embedding framework that explicitly incorporates document context by jointly encoding multiple pages and deriving page-level embeddings from a shared contextual representation. Furthermore, we introduce CMCL, a contextual multimodal contrastive learning objective that effectively trains CMDR-Embed and balances contextual modeling with page-level discriminability. Experiments demonstrate that CMDR-Embed significantly outperforms non-contextual embeddings, highlighting the importance of context-aware multimodal embeddings for advancing document retrieval1.

ECCV 2026

360Anything: Geometry-Free Lifting of Images and Videos to 360°

Ziyi Wu, Daniel Watson, Andrea Tagliasacchi, David Fleet, Marcus Brubaker, Saurabh Saxena

Lifting perspective images and videos to 360◦ panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometric alignment between the perspective and the equirectangular projection (ERP) space. Yet, this requires known camera metadata, obscuring the application to in-the-wild data where such calibration is typically absent or noisy. We propose 360Anything, a geometry-free framework built upon pre-trained diffusion transformers. By treating the perspective input and the panorama target simply as token sequences, 360Anything learns the perspective-to-equirectangular mapping in a purely data-driven way, eliminating the need for camera information. Our approach achieves state-of-the-art performance on both image and video perspective-to-360◦ generation, outperforming prior works that use ground-truth camera information. We also trace the root cause of the seam artifacts at ERP boundaries to zero-padding in the VAE encoder, and introduce Circular Latent Encoding to facilitate seamless generation. Finally, we show competitive results in zero-shot camera FoV and orientation estimation benchmarks, demonstrating 360Anything’s deep geometric understanding and broader utility in computer vision tasks. Additional results are available at https://360anything.github.io.

ECCV 2026

Tempo-SAM3D: Monocular Video to 4D via Temporal Memory-Guided Generation

Baicheng Li*, Dong Wu, Yingdian Cao, Haoxiang Yang, Yiwen Lu, Zike Yan, Hongbin Zha

Generating temporally coherent 4D content from monocularvideo is a challenging yet practically important problem. Recent advancesin layout-aware 3D generation have enabled producing objects with theirspatial arrangements from a single image, naturally providing scene-levelunderstanding that is valuable for 4D generation. However, applyingsuch single-image models independently to each video frame yields severetemporal inconsistencies—flickering textures, structural discontinuities,and jittering poses. We present Tempo-SAM3D, a training-free frame-work that extends layout-aware 3D generation to coherent 4D generationfrom monocular video. Our approach introduces Dual-Path TemporalAttention, which maintains importance-driven KV caches in both cross-attention and self-attention to propagate visual memory and structuralmemory across frames, respectively. We further incorporate gradient-guided velocity correction that constrains the flow matching samplingtrajectory, and HexPlane-based temporal smoothing that regularizes la-tent representations via low-rank spatiotemporal decomposition. Tempo-SAM3D requires no model retraining and inherits layout awareness forspatially-grounded 4D generation. Experiments demonstrate clear im-provements over per-frame baselines and existing video-to-4D methods.

ECCV 2026

TRaM-VSR: Importance-Aware Token Routing and Merging for One-Step Diffusion Video Super-Resolution

Sicheng Gao, Yixuan Liu, Tong Shen, Zhuyun Zhou, Zongwei Wu, Radu Timofte

Video super-resolution (VSR) using large-scale Diffusion Transformer (DiT) priors achieves exceptional perceptual quality but is often impractical due to the quadratic computational cost of processing dense spatio-temporal token sequences. Existing efficiency-oriented methods risk irreversible detail loss and temporal flickering, a vulnerability especially pronounced in one-step diffusion models. To address this, we propose TRaM-VSR, a Token Routing and Merging framework for adaptive token allocation, leveraging both context-aware video priors and network-level priors. First, token importance is estimated by fusing motion-sensitive temporal cues with semantic text similarity, isolating dynamic objects and structural boundaries. Next, this importance is further calibrated and adjusted by an offline planner to guide routing across optimally grouped network blocks. Technically, within each routed group, structurally critical tokens are processed in a highfidelity local stream, while less informative tokens are aggregated into a compact global stream, both modulated by network depth and aligned with the multigranular nature of diffusion models. Extensive experiments show that TRaM-VSR accelerates inference significantly while preserving state-of-the-art reconstruction quality and robust temporal consistency. The code is available at https://github.com/Ree1s/TRaM-VSR.

ECCV 2026

Learning Manifolds in High-D Point Embedding for Anisotropic Surface Approximation from Unstructured Point Clouds

Hongbo Li, Haikuan Zhu, Xiaohu Guo, Wenping Wang, Jing Hua, Zichun Zhong

Dense 3D sensors in various real-world fields produce point clouds that are geometrically redundant for real-time processing. In this paper, we propose an efficient and scalable learning-based anisotropic surface approximation framework, HD-PEA, that operates directly on unstructured point clouds, integrating anisotropic optimization into reconstruction to produce compact, geometry-aligned surface representations with higher fidelity, fewer elements, and improved numerical stability compared to isotropic and adaptive meshes. Firstly, we develop a novel learning-based high-dimensional (high-d) Euclidean point embedding method to map the input point clouds into a high-d manifold embedding space. For handling large-scale point clouds without retraining and fine-tuning, a patch-based meta-embedding scheme is designed during the inference stage. Then, we develop a new tangent subspace estimation for the high-d embedding manifold approximation and anisotropic manifold reconstruction in high-d space. The main contribution of this work is to propose a scalable deep learning framework and a variety of datasets for constructing a high-d Euclidean point embedding space aimed to 3D anisotropic surface mesh approximation and Riemannian curvature tensor estimation from point clouds. We extensively evaluate our method against state-of-the-art surface reconstruction approaches using several datasets, such as Thingi10K dataset, AIM@SHAPE and Stanford 3D Scanning Repository, ScanNet dataset, and further demonstrate its generalization and usability on diverse unseen shapes and applications from these datasets.

ECCV 2026

From Noise to Events: Conditional Diffusion for Event Data Augmentation

Ruofei Wang, Ziyuan Luo, Peiqi Duan, Xiufeng Huang, Boxin Shi, Renjie Wan

Event vision exploration holds significant achievements in various vision tasks, such as object recognition, action recognition, etc. However, improving the model’s robustness against overfitting remains a critical challenge. To address this, we propose augmenting event data from the noise rather than applying fixed data transformations. Specifically, we first decouple the quadruplet event data across spatial, temporal, and polarity dimensions to construct a representation, breaking existing frame-tied manners. Then, an event diffusion decoder is integrated with a condition extractor to gradually optimize the noise to the event data (N2E). The condition extractor aims to yield guidance vectors from the original data, which are fed into each optimization step of the diffusion process. During data augmentation, we can directly generate new event streams by adjusting the noise, diffusion parameters (e.g., number of steps or noise intensity), or conditioning vectors, thereby boosting the performance of downstream event vision models. Extensive experiments on two downstream tasks among five event vision models demonstrate that N2E achieves greater improvements than existing augmentation baselines. Code is available at https://github.com/rfww/n2e.

ECCV 2026

Fast and Accurate Image Restoration with Rank Enhanced Linear Attention

Yuang Ai

Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Transformer hinders its applicability to high-resolution images. Existing methods mitigate this issue with sparse or windowbased attention, yet inherently limit global context modeling. Linear attention, a variant of softmax attention, demonstrates promise in global context modeling while maintaining linear complexity, offering a potential solution to the above challenge. Despite its efficiency benefits, vanilla linear attention suffers from a significant performance drop in IR, largely due to the low-rank nature of its attention map. To counter this, we propose Rank Enhanced Linear Attention (RELA), a simple yet effective method that enriches feature representations by integrating a lightweight depthwise convolution. Building upon RELA, we propose an efficient and effective Vision Transformer, named LAformer. LAformer eliminates hardware-inefficient operations such as softmax and window shifting, enabling efficient processing of high-resolution images. Extensive experiments across 7 IR tasks and 21 benchmarks demonstrate that LAformer outperforms SOTA methods and offers significant computational advantages. Furthermore, we extend LAformer to diffusion-based and flow-based visual generation, showcasing its strong potential as a competitive alternative to DiT and SiT. Code and models are available at https://github.com/shallowdream204/LAformer.

ECCV 2026

GeoCFM: Positive-Only Conditional Flow Matching for Mineral Occurrence Sampling

Moshe Eliasof, Eldad Haber

Critical mineral discovery is a positive-only problem: deposits are observed as sparse locations, while unlabeled regions are not reliable negatives, and similar geophysical signatures can arise from different subsurface states. We therefore model mineral targeting as learning a conditional spatial distribution over occurrence locations, π(p | d), given geo-images d, rather than predicting a deterministic per-pixel score map. We introduce GeoCFM, a conditional flow-matching model that generates mineral occurrence point sets conditioned on multi-channel geo-images; GeoCFM learns a point-wise transport field in R2, using UNet features with point-conditioned velocity prediction to bridge dense rasters and sparse supervision without pseudo-negatives. On a synthetic magnetics–geochemistry benchmark with latent activation and on USGS Earth MRI data with a spatially disjoint tile split, GeoCFM improves geometric agreement with observed occurrences over score-map and nonconditional baselines, while representing epistemic uncertainty through conditional sampling.

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