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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

VPA-WM: Vision-Priors-Aligned World Models for Robust Visual Reinforcement Learning

Xu Zhang, Sicong Liu, Liwei Guo, Chenjuan Guo, Bin Yang, Yang Shu

Building robust world models for visual reinforcement learn-ing remains challenging under complex visual conditions. When facedwith task-irrelevant visual distractions, the representations learned byworld models tend to collapse. Pre-trained vision models (PVMs) providestrong visual priors and structured representations, yet those learned forvisual perception are incompatible with dynamic control in RL. In thiswork, we present Vision-Priors-Aligned World Model (VPA-WM1 ): aunified framework that organically integrates PVMs and world models toachieve robust visual control. We first introduce Control-Oriented Align-ment, which effectively guides PVMs to focus on control-relevant signalsthrough two auxiliary objectives: instant action prediction and short-horizon return estimation. We then design Spatio-Temporal Collabora-tive Integration mechanism, which guides the world model to adaptivelyattend to the visual cues of PVMs through two complementary designs(spatially grounded guidance and experience query integration), therebylearning representations that are visually robust and dynamically con-sistent. Superior performance on visually-distracted control benchmarksdemonstrates that VPA-WM effectively bridges the semantic gap be-tween visual perception and dynamic control, and successfully leveragesvisual priors to enhance the robust control of world models.

ECCV 2026

LEGO: Leveled Language Gaussian Splatting

Yuning Peng, Haiping Wang, Yuan Liu, Yipeng Lu, Zhen Dong, Bisheng Yang

We introduce LEGO for advanced open-vocabulary scene un-derstanding. Beyond basic concept recognition, its core innovation lies incapturing the intrinsic semantic hierarchies within the scene, such as the"flowerpot → bouquet → bud → petal" lineage. While foundation mod-els like SAM can identify multi-granular structures in 2D, their partitionsare strictly perspective-bound and lack cross-view consensus. LEGO self-adaptively re-grades volatile multi-view SAM granularities into a unified,3D-consistent hierarchy. This provides precise supervision for the struc-turally coherent, multi-level segmentation of 3D scenes. By groundingthese segments with CLIP embeddings, LEGO recovers open-vocabularysemantic logic across hierarchical levels. Furthermore, by incorporatingspatial relationships, we elevate these segments into level-wise languagescene graphs, effectively empowering Large Language Models to performcomplex, context-aware spatial reasoning and precise visual grounding.Experimental results demonstrate that LEGO establishes new state-of-the-art performance across both promptable and open-vocabulary 3Dsegmentation benchmarks, exhibiting advanced hierarchical scene de-composition and context-aware spatial reasoning. Project page: https://pz0826.github.io/LEGO-Webpage/

ECCV 2026

Trajectory Forcing: Structure-First Generation with Controllable Semantic Trajectories

Merve Kocabas, Gege Gao, Bernhard Scholkopf, Andreas Geiger

Diffusion and flow-based generative models produce strong images, yet their controllability remains largely endpoint-centric: users specify conditions and receive final outputs, while the intermediate generative dynamics remain hidden. Recent methods have begun to exploit generation order and process decomposition to improve sample quality, but still treat intermediate states as internal computation rather than objects for interaction. We propose Trajectory Forcing (TF), a trajectory-centric framework that makes the generation path explicit, semantic, and editable. TF organizes synthesis as a sequence of semantically structured stages, progressing from global layout to object-, part-, and detail-level representations. Each stage produces a decodable latent state that can be inspected, evaluated, and locally edited before the next stage begins. To instantiate this path, we derive coarse-to-fine teacher hierarchies by clustering pretrained visual representations such as DINOv2, and train a hierarchy-conditioned one-step flow-matching model at each level. We further introduce trajectory-aware metrics that measure structural consistency and local controllability beyond endpoint quality metrics such as FID. Experiments show that TF achieves competitive sample quality while exposing coherent intermediate states and supporting localized edits across semantic levels. By shifting the focus from final images to the generative path itself, TF opens a route toward controllable, trajectory-aware image synthesis.

ECCV 2026

AnyView: Synthesizing Any Novel View in Dynamic Scenes

Basile Van Hoorick, Dian Chen, Shun Iwase, Pavel Tokmakov, Muhammad Zubair Irshad, Igor Vasiljevic, Swati Gupta, Fangzhou Cheng, Sergey Zakharov, Vitor Guizilini

Modern generative video models excel at producing convinc-ing, high-quality outputs, but struggle to maintain multi-view and spa-tiotemporal consistency in highly dynamic real-world environments. Inthis work, we introduce AnyView, a diffusion-based video generationframework for dynamic view synthesis with minimal inductive biasesor geometric assumptions. We leverage multiple data sources with var-ious levels of supervision, including monocular (2D), multi-view static(3D) and multi-view dynamic (4D) datasets, to train a generalist spa-tiotemporal implicit representation capable of producing zero-shot novelvideos from arbitrary camera locations and trajectories. We evaluateAnyView on standard benchmarks, showing competitive results with thecurrent state of the art, and propose AnyViewBench, a challengingnew benchmark tailored towards extreme dynamic view synthesis in di-verse real-world scenarios. In this more dramatic setting, we find thatmost baselines drastically degrade in performance, as they require signif-icant overlap between viewpoints, while AnyView maintains the abilityto produce realistic, plausible, and spatiotemporally consistent videoswhen prompted from any viewpoint.

ECCV 2026

SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models

Jingfeng Mao, Xuyang Chen, Qilin Zhang, Oussema Dhaouadi, Guangming Wang, Brian Sheil, Daniel Cremers, Yan Xia, Olaf Wysocki

Aerial 6DoF localization typically relies on precise GNSSsignals or radiometrically rich 3D reconstructions, limiting scalability andon-board deployment. We propose SemCityLoc, a semantic–geometricalignment system that reframes aerial pose estimation as structuredsurface registration between foundation-model-derived visual priors andstandardized LoD-compliant 3D city models. Instead of matching sparsecontours or dense texture, our method aligns semantic surfaces and monoc-ular depth with lightweight semantic 3D building models, increasing pose* Corresponding authordiscriminability in repetitive and occluded urban environments. To enableaccurate evaluation, we introduce SemCityLockeD, the first real-worldbenchmark combining centimeter-accurate UAV poses with standardizedLoD1–LoD3 semantic city models and challenging low-altitude imagery.Experiments demonstrate substantial improvements over existing map-based approaches, improving recall by up to 36% and reducing meanpositional error from 9.89 m to 2.62 m in challenging urban canyons. Ourresults indicate that semantically structured geometry provides sufficientand scalable constraints for high-precision aerial localization withoutradiometric scene reconstructions. The code and data are available athttps://albertchen98.github.io/SemCityLoc.

ECCV 2026

Cross-Resolution Distribution Matching for Diffusion Distillation

Chen Feiyang, Hongpeng Pan, Haonan Xu, Xinyu Duan, Zhefeng Wang, Yang Yang

Diffusion distillation is central to accelerating image andvideo generation, yet existing methods are fundamentally limited by thedenoising process, where step reduction has largely saturated. Partial-timestep, low-resolution generation can further accelerate inference, butit suffers from noticeable quality degradation due to cross-resolutiondistribution gaps. We propose Cross-Resolution Distribution MatchingDistillation (RMD), a novel distillation framework that bridges cross-resolution distribution gaps for high-fidelity, few-step multi-resolutioncascaded inference. Specifically, RMD divides the timestep intervals foreach resolution using logarithmic signal-to-noise ratio (logSNR) curves,and introduces logSNR-based mapping to compensate for resolution-induced shifts. Distribution matching is conducted along resolution tra-jectories to reduce the gap between low-resolution generator distributionsand the teacher’s high-resolution distribution. In addition, a predicted-noise re-injection mechanism is incorporated during upsampling to stabi-lize training and improve synthesis quality. Quantitative and qualitativeresults show that RMD preserves high-fidelity generation while acceler-ating inference across various backbones. Notably, RMD achieves up to33.4× speedup on SDXL and 25.6× on Wan2.1-14B, while preservinghigh visual fidelity.

ECCV 2026

A second-order theory of texture for depth from focus

Sreekar Ranganathan, Ioannis Gkioulekas

We present a theory of textured appearance of optically roughsurfaces based on wave optics, emphasizing the role of texture for passivedepth from focus. Our theory shows that even surfaces that traditionalcomputer vision would consider textureless can produce textured appear-ance, due to subjective speckle from surface microgeometry. We analyzethe properties of this second-order texture, and show that we can enhanceits contrast under natural ambient lighting by simply using a narrowbandspectral filter. Doing so results in dramatic improvements in passive depthreconstruction of seemingly textureless scenes, as we demonstrate throughextensive theory, simulations, and real-world experiments.

ECCV 2026

OctWorld: Long-Range World-Consistent Video Generation with Octree-based 3D Mapping

Zelong Lv, Sicheng Xu, Jianfeng Xiang, Yue Dong, Ruicheng Wang, Yu Deng, Guangzhong Sun, Jiaolong Yang

We present OctWorld, a video diffusion framework with 3Dmemory for generating explorable, world-consistent, and high-fidelity vi-sual scenes. Given a single image, OctWorld enables stable autoregressiveworld generation along user-specified camera trajectories. We focus onthe problem of long spatial range generation, characterized by long cam-era path and wide viewing angle coverage, where maintaining spatialconsistency – particularly when revisiting previously generated regions –becomes challenging. To address this, we first build upon a pretrained au-toregressive video diffusion model that supports longer-horizon contextmodeling. We adapt this model to RGB-D generation and introduce adepth normalization scheme for inter-chunk consistency. Next, and moreimportantly, we propose an extensible and spatially-adaptive 3D memory,termed OctMap. OctMap constructs a global 3D representation of thegenerated scene by progressively fusing the generated RGB-D frames.To accommodate diverse scene scales in the open world setting, it ap-plies adaptive spatial resolution dynamically determined by image evi-dence with a sparse octree hierarchy, which preserves effective geometricand appearance details while maintaining low memory overhead. Experi-ments demonstrate that OctWorld can generate long-range and spatiallyconsistent videos, outperforming prior methods on existing benchmarksas well as in longer-range generation cases.

ECCV 2026

Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion

Bowen Xue, Brandon Feng, Chenguo Lin, Yuchen Lin, Yujia Zeng, Lvmin Zhang, Maneesh Agrawala, Honglei Yan, Panwang Pan

Scaling video generation to long durations reveals a criti-cal bottleneck: current models lack robust long-term memory. This defi-ciency can be studied along two critical aspects: object permanence, theability to precisely reproduce the appearance of objects upon re-entry;and memory capacity, the ability to process ultra-long context and use in-formation from distant history. Robust long-term memory requires both:object permanence without sufficient context handling limits the tempo-ral scope, while long context length without permanence fails to maintainidentity. To address this, we present Ring Forcing, an autoregressivevideo diffusion framework designed to robustly construct and preciselyutilize long-term memory. Our ring-structured training strategy enforcesretrieval from distant history, effectively reconciling the trade-off betweenstrict historical adherence and generative diversity. To expand memorycapacity, we introduce a compression and timestep composition strat-egy. Under fixed sequence length constraints, this method extends theeffective historical span to minutes-long durations and achieves a compre-hensive receptive field over the entire history. Furthermore, we present asparse RoPE mechanism to enable flexible, scalable memory adaptationwhile fully exploiting pre-trained priors. Extensive experiments demon-strate that Ring Forcing achieves superior minutes-long coherence andobject permanence, significantly outperforming state-of-the-art methods.

ECCV 2026

SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities

Diwas Lamsal, Pramod Wickramatilake, Jednipat Moonrinta, Mongkol Ekpanyapong, Matthew Dailey

Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as falls, particularly for mobility-challenged individuals. Existing datasets are often clip-based, lacking the frame-level detail needed to recognize actions online, as they unfold. To address this, we introduce SAFER-Activities, a dataset for fall detection and physical activity monitoring, with a dedicated subset for wheelchair use scenarios. It comprises over 66 hours of video data captured by multiple cameras, with 85,310 action instances and frame-level annotations for 30 action classes. We benchmark action recognition on SAFER-Activities with 2D and 3D skeleton models, RGB models with frozen backbones, and multimodal fusion strategies, and evaluate on in-lab, out-of-distribution, and crossdataset test sets. Skeleton-based models generalize best under domain shift; fusing frozen RGB features with the skeleton stream improves in-domain recognition over the baseline CNN1D, most clearly on the wheelchair subset, but degrades out of distribution. Cross-dataset and qualitative evaluations confirm that models trained on SAFER-Activities transfer well to unseen environments and external fall data. To support research on robust fall detection and activity monitoring, we release the dataset and code at https://safer-activities.github.io/.

ECCV 2026

Discovering Geometric Biases in 3D Face Reconstruction: A Curvature-Aware Spectral Framework for Fairness Evaluation

Veronika Shilova, Emmanuel Malherbe, Giovanni Palma, Panagiotis-Alexandros Bokaris, Laurent Risser, Jean-Michel Loubes

3D Morphable Models (3DMMs) remain the standard parametric shape priors for many state-of-the-art 3D face reconstruction algorithms. However, as these models are derived from a finite number of 3D face samples, they inherit the morphological biases of their training data, potentially limiting their generalizability across diverse global populations. In this paper, we propose a novel framework to analyze 3DMM reconstructions through the lens of surface curvature, with the objective to discover, quantify and visualize biases. While standard evaluation metrics often rely on Euclidean distances, our reconstruction error captures subtle surface nuances such as local topology or undulations. To do so, we leverage the Laplace-Beltrami Operator (LBO) to generate high-resolution curvature error maps, providing a localized and geometrically meaningful visualization of discrepancies between ground truth faces and reconstructed meshes. We derive from it an error metric that we validated through a user study, observing a significantly higher correlation to human perception compared to traditional methods. Furthermore, we conduct extensive experiments across several 3DMM bases and fitting algorithms, uncovering systematic age-related biases and providing preliminary evidence of biases associated with gender and ethnicity. Our findings highlight the necessity of adopting curvature-aware evaluation protocols to ensure demographic fairness and geometric precision in future 3D face reconstruction research. The code and annotation data are available at https://github.com/artefactory/3dface-fairness.

ECCV 2026

Training-Free Multi-Concept Image Editing

Niki Maria Foteinopoulou, Ignas Budvytis, Stephan Liwicki

Training-free image editing with diffusion models is highlydesirable yet is complex and remains a significant challenge. While recentoptimisation-based methods achieve strong zero-shot edits from text,they still struggle to preserve identity and capture intricate details, suchas facial structure, surface texture, or object-specific geometry, that ex-ist below the level of linguistic abstraction. To address this fundamentalgap, we propose Concept Distillation Sampling (CDS). To the best of ourknowledge, we are the first to introduce a unified, training-free frameworkfor target-less, multi-concept image editing.CDS overcomes this linguistic bottleneck of previous methods by anchor-ing the editing process in the certainty of pretrained LoRA adapters.We integrate a highly stable distillation backbone (featuring orderedtimesteps, regularisation, and negative-prompt guidance) with a noveldynamic weighting mechanism. This approach enables the compositionand control of multiple visual concepts directly within the diffusion pro-cess, utilising spatially-aware priors from pretrained LoRA adapters with-out causing concept clashing. Our method preserves instance conceptidentity without requiring reference samples of the desired edit. Extensivequantitative and qualitative evaluations demonstrate that CDS estab-lishes a new state-of-the-art over existing training-free editing and multi-LoRA composition methods on the InstructPix2Pix and ComposLoRAbenchmarks. Project Page: https://nickyfot.github.io/cds/.

ECCV 2026

From Macro to Micro: Benchmarking Microscopic Spatial Intelligence on Molecules via Vision-Language Models

Zongzhao Li, Xiangzhe Kong, Jiahui Su, Zongyang Ma, Mingze Li, Songyou Li, Yuelin Zhang, Yu Rong, Tingyang Xu, Deli Zhao, Wenbing Huang

This paper introduces the concept of Microscopic Spatial In-telligence (MiSI), the capability to perceive and reason about the spatialrelationships of invisible microscopic entities, which is fundamental toscientific discovery. To assess the potential of Vision-Language Models(VLMs) in this domain, we propose a systematic benchmark frameworkMiSI-Bench. This framework features over 163,000 question-answer pairsand 587,975 images derived from approximately 4,000 molecular struc-tures, covering nine complementary tasks that evaluate abilities rangingfrom elementary spatial transformations to complex relational identifi-cations. Experimental results reveal that current state-of-the-art VLMsperform significantly below human level on this benchmark. However,a fine-tuned 7B model demonstrates substantial potential, even sur-passing humans in spatial transformation tasks, while its poor perfor-mance in scientifically-grounded tasks like hydrogen bond recognitionunderscores the necessity of integrating explicit domain knowledge forprogress toward scientific AGI. The datasets are available at https://huggingface.co/datasets/zongzhao/MiSI-bench.

ECCV 2026

D²R²OSR: Degradation-Disentangled Representation for Real-World Omnidirectional Image Super-Resolution

Hongyu An, Xinfeng Zhang, Xu Fan, Shijie Zhao, Li Zhang, Ruiqin Xiong

With the growing demand for immersive visual experiences, high-quality omnidirectional images (ODIs) have become increasingly important. However, limitations in imaging devices and transmission bandwidth often lead to low-resolution ODIs, hindering the rendering of fine-grained 360◦ details, especially in the presence of real-world degradations and geometric distortions. Existing real-world super-resolution (Real-SR) methods are inadequate for ODIs, as their degradation models fail to account for the complex imaging pipeline involving fisheye capture and Equirectangular Projection (ERP), introducing severe aliasing and projection-specific distortions. To address these challenges, we propose D2R2OSR, a Degradation-Disentangled Representation framework for Real-world Omnidirectional image Super-Resolution. D2R2OSR explicitly models degradations arising from both fisheye imaging and ERP projection, guided by two key insights: (1) projection priors play a critical role in shaping real-world degradations, and (2) human perception in immersive environments is inherently viewpoint-centric. Accordingly, we introduce a Perspective Projection Representation (PPR) operating alongside the ERP branch to capture viewpoint-aware features, together with a Degradation-Specific Module (DSM) that jointly models ERPinduced geometric distortions and PPR-specific real-world degradations. Extensive experiments demonstrate that D2R2OSR achieves state-of-theart performance and produces visually compelling, high-fidelity omnidirectional Real-SR results while maintaining favorable computational efficiency for low-resource deployment.

ECCV 2026

Learning Ego-Centric BEV Representations from a Perspective-Privileged View: Cross-View Supervision for Online HD Map Construction

Daniel Lengerer, Mathias Pechinger, Klaus Bogenberger, Carsten Markgraf

Bird’s-Eye View (BEV) representations derived from multi-camera input have become a central interface for online HD map con-struction. However, most approaches rely solely on ego-centric super-vision, requiring large-scale scene structure to be inferred from incom-plete observations, occlusions, and diminishing information density atlong range, where perspective effects and spatial sparsity hinder consis-tent structural reasoning. We introduce Cross-View Supervision (CVS),a representation learning paradigm that transfers geometric and topolog-ical priors from an ego-aligned overhead perspective into camera-basedBEV encoders. Rather than adding auxiliary semantic losses, CVS alignsrepresentations in a shared BEV feature space and distills globally con-sistent structural knowledge from a perspective-privileged teacher intothe ego-centric backbone. This supervision enhances structural coher-ence without modifying the inference architecture or requiring overheadinput at test time. Experiments on nuScenes using ego-aligned aerialimagery from the AID4AD cross-view extension demonstrate consistentimprovements over StreamMapNet while maintaining identical camera-only inference. CVS yields +3.9 mAP in the standard 60 × 30 m regionand +9.9 mAP in the extended 100 × 50 m setting, corresponding toa 44% relative gain at long range. These results highlight perspective-privileged structural supervision as a promising training principle forimproving BEV representation learning in HD map construction. Theproject repository is available athttps://github.com/DriverlessMobility/CrossViewSupervision.

ECCV 2026

Consistent Feature Transport for Image Relighting

Bohan Zhang, huanweiliang huanweiliang, Yuhan He, Hongteng Xu, Xiaochao Qu, Luoqi Liu, Dixin Luo, Ting Liu

Image relighting modifies illumination while preserving non-lighting content such as identity and geometry. Existing diffusion-basedmethods often suffer from unstable illumination changes or inconsistentcontent preservation under complex lighting, as they lack an explicitmechanism to learn feature transformations between images. We refor-mulate relighting as an illumination feature transport problem and in-troduce Consistent Feature Transport (CFT), a training principle thatexplicitly enforces illumination-consistent transport between source andtarget image distributions. Built upon rectified flow, CFT jointly modelsnoise-to-image generation and illumination-consistent source-to-targettransport through trajectory-level supervision. This dual-transport for-mulation encourages isolation of illumination-specific variations whilepreserving content-aligned features. To support complex lighting scenar-ios, we construct a large-scale portrait relighting dataset with diverserelighting effects. Experiments show consistent improvements over ex-isting state-of-the-art relighting approaches and demonstrate that CFTcan generalize to other editing tasks, including style transfer. Code isavailable at https://github.com/Dixin-Lab/CFT.

ECCV 2026

ATP-Bench: Towards the Agentic Tool Planning for MLLM Interleaved Generation

Yinuo Liu, Zi Qian, Heng Zhou, Jiahao Zhang, Yajie Zhang, Zhihang Li, Mengyu Zhou, Erchao Zhao, Xiaoxi Jiang, Guanjun Jiang

Interleaved text-and-image generation represents a signifi-cant frontier for Multimodal Large Language Models (MLLMs), offeringa more intuitive way to convey complex information. Current paradigmsrely on either image generation or retrieval augmentation, yet they typi-cally treat the two as mutually exclusive paths, failing to unify factualitywith creativity. We argue that the next milestone in this field is AgenticTool Planning, where the model serves as a central controller that au-tonomously determines when, where, and which tools to invoke to pro-duce interleaved responses for visual-critical queries. To systematicallyevaluate this paradigm, we introduce ATP-Bench, a novel benchmarkcomprising 7,702 QA pairs (including 1,592 VQA pairs) across eight cat-egories and 25 visual-critical intents, featuring human-verified queriesand ground truths. Furthermore, to evaluate agentic planning indepen-dent of end-to-end execution and changing tool backends, we proposea Multi-Agent MLLM-as-a-Judge (MAM) system. MAM evaluates tool-call precision, identifies missed opportunities for tool use, and assessesoverall response quality without requiring ground-truth references. Ourextensive experiments on 10 state-of-the-art MLLMs reveal that modelsstruggle with coherent interleaved planning and exhibit significant varia-tions in tool-use behavior, highlighting substantial room for improvementand providing actionable guidance for advancing interleaved generation.

ECCV 2026

From Drop-off to Recovery: A Mechanistic Analysis of Segmentation in MLLMs

Boyong Wu, Sanghwan Kim, Zeynep Akata

Multimodal Large Language Models (MLLMs) are increasingly applied to pixel-level vision tasks, yet their intrinsic capacity for spatial understanding remains poorly understood. We investigate segmentation capacity through a layerwise linear probing evaluation across the entire MLLM pipeline: vision encoder, adapter, and LLM. We further conduct an intervention based attention knockout analysis to test whether cross-token attention progressively refines visual representations, and an evaluation of bidirectional attention among image tokens on spatial consistency. Our analysis reveals that the adapter introduces a segmentation representation drop-o!, but LLM layers progressively recover through attention-mediated refinement, where correctly classified tokens steer misclassified neighbors toward the correct label. At early image token positions, this recovery is bounded by causal attention, which bidirectional attention among image tokens alleviates. These findings provide a mechanistic account of how MLLMs process visual information for segmentation, informing the design of future segmentation-capable models.

ECCV 2026

Learn to Rank: Visual Attribution by Learning Importance Ranking

David Schinagl, Christian Fruhwirth-Reisinger, Alexander Prutsch, Samuel Schulter, Horst Possegger

Interpreting the decisions of complex computer vision modelsis crucial to establish trust and accountability, especially in safety-criticaldomains. An established approach to interpretability is generating visualattribution maps that highlight regions of the input most relevant to themodel’s prediction. However, existing methods face a three-way trade-off. Propagation-based approaches are efficient, but they can be biasedand architecture-specific. Meanwhile, perturbation-based methods arecausally grounded, yet they are expensive and for vision transformersoften yield coarse, patch-level explanations. Learning-based explainersare fast but usually optimize surrogate objectives or distill from heuris-tic teachers. We propose a learning scheme that instead optimizes dele-tion and insertion metrics directly. Since these metrics depend on non-differentiable sorting and ranking, we frame them as permutation learn-ing and replace the hard sorting with a differentiable relaxation usingGumbel-Sinkhorn. This enables end-to-end training through attribution-guided perturbations of the target model. During inference, our methodproduces dense, pixel-level attributions in a single forward pass with op-tional, few-step gradient refinement. Our experiments demonstrate con-sistent quantitative improvements and sharper, boundary-aligned expla-nations, particularly for transformer-based vision models. Code and pre-trained models are available at https://github.com/dschinagl/AHA.

ECCV 2026

Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning

Kazi Sajeed Mehrab, Hani Alomari, Najibul Sarker, Zaber Ibn Abdul Hakim, Chia-Wei Tang, Anuj Karpatne, Chris Thomas

Multimodal large language models (MLLMs) ground wholeobjects well from free-form language queries, but they struggle whenthe query names a part rather than the object. We trace this to amissing object-part hierarchy, since parts are localized in the same sin-gle step used for objects. We propose Object-Part Hierarchical Reflec-tive Grounding (OP-HRG), a coarse-to-fine reasoning-guided groundingstrategy that first localizes the parent object and then the part within it.A self-check then reflects on the result, with an extension to re-encodethe predicted crop to inspect the region it is correcting. We introducea part-aware GRPO framework to train our pipeline with stage-wise re-wards. A 4B model trained this way outperforms 7B grounding LLMs andSAM3 across PascalPart, PartImageNet, and InstructPart, and transfersto reasoning segmentation. 1

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