paperlist.ai

AI conference papers

AI conference paper library

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

SignBind-LLM: Multi-Stage Modality Fusion for Sign Language Translation

Marshall Thomas, Edward Fish, Richard Bowden

Current sign language translation (SLT) systems attempt tolearn all aspects of signing—manual gestures, high-speed fingerspelling,and asynchronous non-manual facial cues—within a single end-to-endnetwork. Learning multiple tasks without detailed supervision leads topoor recognition of fingerspelled proper nouns and technical terms, andleaves rich disambiguating information from lip movements largely unex-ploited. We introduce SignBind-LLM, a modular framework that ad-dresses these limitations through three dedicated expert streams: one forcontinuous signing, one for fingerspelling, and one for lipreading. Eachexpert is pre-trained independently using CTC on approximately twomillion automatically generated pseudo-gloss sequences, removing theneed for manual gloss annotation. A lightweight transformer with learnedtemporal alignment fuses the expert outputs, and a pre-trained languagemodel translates the resulting pseudo-gloss sequences into fluent spokenEnglish. At matched decoder scale (250M parameters), our architecturealready surpasses all prior methods, confirming that the gains are archi-tectural rather than a consequence of scaling the language model. Scal-ing to a larger decoder sets a new state-of-the-art across How2Sign: 23.1,BOBSL: 7.0, and ChicagoFSWild+: 73.6%, while requiring significantlylower training cost than prior approaches.

ECCV 2026

Sector-Level Cross-View Geo-Localization with Implicit Orientation via Azimuthal Scanning

Yiru Li, Le Wu, Yingchen Tan, Yingying Zhu

Cross-View Geo-Localization (CVGL) is crucial for naviga-tion in GPS-restricted environments. However, bridging the severe per-spective gap between ground and aerial views remains challenging. Exist-ing approaches either rely on coarse image-level retrieval, suffer from un-stable optimization in pose regression, or require expensive fine-grainedannotations. These limitations suggest that current formulations of cross-view geo-localization are insufficient for reliable fine-grained localization.To address this limitation, we introduce a new task, Sector-Level Cross-View Geo-Localization (SLCVGL), which aims to perform fine-grainedsector-level geo-localization of a limited Field-of-View (FoV) ground im-age within an omnidirectional aerial image, while simultaneously infer-ring its ground-view orientation without manually annotated pose labels.To operationalize this task, we propose an Azimuthal Scanning frame-work. It decomposes aerial feature maps into multiple azimuthal sectors,instead of relying on holistic matching. By aligning ground features withthese sectors, the model explicitly retrieves a sector-level sub-region andimplicitly estimates orientation, eliminating the need for continuous poseregression supervision. This design also reduces interference from unob-served overhead regions. To benchmark the proposed task, we repur-pose standard panoramic datasets into a limited-FoV sector-level proto-col with random FoV cropping and soft labeling. Extensive experimentson CVUSA and CVACT demonstrate improvements over state-of-the-art methods, with a 30.17% R@1 improvement on CVACT under the 70°FoV setting, while maintaining strong robustness across varying FoVs.

ECCV 2026

ScanFocus: A Coarse-to-Fine Framework for Spatio-Temporal Video Grounding

Chen Kai, Ming Dai, Wenxuan Cheng, Wankou Yang

Spatio-Temporal Video Grounding (STVG) aims to retrievethe visual trajectory of a specific object from a video stream as describedby a natural language expression. However, most advanced methodsstruggle to balance global context modeling with precise boundary lo-calization. Due to the prohibitive computational costs of processing longvideos, these approaches typically resort to low-rate temporal downsam-pling and implicit motion modeling. This inevitably suppresses high-frequency boundary cues and neglects the explicit inter-frame depen-dencies required for precise boundary delineation. To address these lim-itations, we present ScanFocus, a novel coarse-to-fine framework thatdecouples the STVG task into a global spatio-temporal scan and a localboundary focus. Specifically, we utilize a unified vision-language fusionencoder combined with a lightweight Deformable Semantic-Motion Fu-sion module to efficiently align multimodal features and generate coarseproposals. To recover the suppressed fine-grained details, we introducethe Semantic-Guided Temporal Aggregator (SGTA) in the refinementstage. By densely sampling around coarse boundaries, SGTA explicitlymodels short-term temporal interactions under semantic guidance, cap-turing rapid motion changes for precise timestamp regression. Extensiveexperiments on three widely used benchmarks demonstrate the perfor-mance superiority of our proposed method over previous approaches.Code will be released at https://github.com/TenMinutes209/ScanFocus.

ECCV 2026

Rosetum3D: A Large-Scale 3D Vision Dataset from Preharvest Roses

Songyan Liu, Xipei Liu, Yujie Liu, Jiali Wu, Xiaofei Liu

The global rose cultivation industry has experienced continued expansion in recent years. There has been a set of value evaluation criteria covering multiple dimensions for roses. However, the evaluations are mainly conducted by human experts after harvesting, which is time-consuming and labor-intensive. 3D computer vision technologies are promising to automate the process before harvesting, but the lack of large-scale datasets with localization annotations that capture plant architecture hinders the progress. To bridge this gap, we propose Rosetum3D, which is a large-scale 3D vision dataset for preharvest roses. The dataset is constructed via occlusion-robust multi-view RGB-D capture protocols in commercial greenhouses. We provide fine-grained 2D localization labels using bounding boxes and botanically defined keypoints, and then obtain the 3D structures recovered through depth backprojection. Rosetum3D contains 21,114 images and 46,848 annotated rose objects. Models trained on Rosetum3D have achieved 2D/3D rose localization, which is a crucial step for automated preharvest quality grading and growth monitoring. Beyond localization, Rosetum3D serves as a benchmark for agricultural vision tasks, including 2D rose object detection, local feature matching, depth estimation, and instance reidentification. By enabling data-driven precision agriculture, Rosetum3D paves the way for robotic harvesting systems and AI-driven yield prediction in protected cultivation. The dataset are available at https: //huggingface.co/datasets/WaterMelon2333/Rosetum3D/tree/main.

ECCV 2026

RankT2I: A Submodular Framework for Discovering Interpretable and Diverse Semantics in Text-to-Image Models

Ritika Allada, Pinar Yanardag

Recent advances in text-to-image (T2I) models have revolu-tionized the field of image generation and editing. However, identifyingsemantics that a T2I model can successfully edit in an image contin-ues to be a challenging task. Most existing approaches require usersto manually specify semantics to modify a particular image, a time-consuming process that often involves extensive trial and error. In thispaper, we present RankT2I, a novel, training-free, and model-agnosticframework that automates the discovery of editable semantics in diffu-sion and FLUX-based models. Given a visual domain, we first utilizea multimodal vision-language model to gather a broad set of candidatesemantics. We then frame semantic discovery as a set selection problemand use a submodular objective to identify semantics that are relevant,editable, and diverse. Our method helps users efficiently identify a widerange of semantics for text-to-image editing models across several do-mains while outperforming existing methods.

ECCV 2026

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

Yeobin Hong, Suhyeon Lee, Hyungjin Chung, Jong Chul Ye

Recent approaches in controllable novel view video generationoften rely on fine-tuning pre-trained Video Di!usion Models (VDMs).This dominant paradigm is computationally expensive and frequentlysu!ers from catastrophic forgetting of the model’s original generative pri-ors. To address this challenge, here we propose InverseCrafter, a VDMtraining-free framework that reformulates novel view video generationas an inpainting-based inverse problem in the latent space, eliminatingthe need for any annotated 4D training data. The core of our methodis to establish operator equivalence by employing a lightweight latentmask encoder to define a latent-domain masking operation via a con-tinuous, multi-channel representation. This principled representationfaithfully models the forward process in the latent domain, enablinge"cient, backpropagation-free solvers while bypassing the costly bottle-neck of repeated VAE operations. InverseCrafter achieves high-fidelity,spatio-temporally coherent novel view synthesis with near-zero additionalinference overhead and excels at general-purpose video inpainting andediting by fully preserving the pre-trained VDM’s generative capabilities.

ECCV 2026

Racing in Volume with Flow Ensembles

Saswat Subhajyoti Mallick, Riu Cherdchusakulchai, Marc Ruiz, Albert Mosella-Montoro, José Ribeiro-Gomes, Francisco Vicente Carrasco, Fernando De la Torre

Streaming 4D reconstruction has been demonstrated onlyindoors, on dense camera rigs surrounding subjects that move at hu-man pace. Outdoor 4D reconstruction exists but relies either on camerasmounted on the moving vehicle itself, or on limited-coverage arrays ob-serving quasi-static subjects offline. The case that actually matters forspectators is a fast-moving subject, watched from a sparse ring of allocen-tric cameras, streaming. No method targets this, and no benchmark ex-ists to evaluate one. To this end, we introduce FastFlowGS, a streaming4D Gaussian Splatting method for reconstructing fast-moving subjectsfrom a small set of fixed external cameras, and Monaco4D, a photoreal-istic Unreal Engine 5 benchmark for high-speed outdoor reconstruction.FastFlowGS fuses sparse matches, semi-dense tracks, and dense opticalflow by lifting each signal to 3D with geometric uncertainty and combin-ing them through a Kalman-style temporal update. Monaco4D providesFormula 1 sequences under varied illumination from trackside, onboard,and drone viewpoints with dense ground truth. On CMU-Panoptic (apublic dataset), FastFlowGS exceeds the strongest baseline by 12.6%VMAF at 35% greater efficiency. On Monaco4D, where existing stream-ing methods degrade severely, it improves dynamic-region PSNR by upto 18.6% with 28.3% lower per-frame optimization time.

ECCV 2026

PHOSA: Photorealistic 3D Sign Avatar Modeling and Benchmark

Haodong Wang, Hezhen Hu, Wengang Zhou, Houqiang Li

In this work, we focus on photorealistic sign avatar modeling,which is crucial for effective communication with the Deaf community andis characterized by complex hand gestures and nuanced facial expressions.To this end, we introduce MVSign, the first multi-view Chinese signlanguage dataset co-designed with Deaf experts, featuring diverse gesturesand rich annotations. For precise SMPL-X annotation, we develop a hybridfitting pipeline that produces accurate body, hand, and facial parametersand can also be applied to the monocular setting. Building on MVSign,we propose a decoupled sign avatar representation that isolates body,head, and hand components to capture complex articulations, togetherwith a motion-aware sampling strategy to handle motion blur and balancegesture diversity. Extensive experiments demonstrate that our methodachieves high-fidelity visual results on MVSign, particularly in detailedhand and facial regions, and generalizes well to in-the-wild monocularsign language videos. Project page: https://naaapi.github.io/PHOSA.

ECCV 2026

OneHSI: A Unified Hyperspectral Foundation Model with Physical Consistency

Yufei WEN, Jingdan Kang, SHUXIN ZHONG, Yuting Zhang, Yutong Feng, Jintai Chen, Kaishun Wu

Hyperspectral imaging (HSI) promises material-level perception beyond RGB vision, yet current models remain tightly coupled to sensing hardware. Minor changes in spectral discretization or spatial resolution can invalidate trained networks, despite all sensors observing the same material reflectance physics. We introduce OneHSI, a unified hyperspectral foundation model that aligns representations across heterogeneous sensing configurations through physically motivated inductive biases. Rather than treating bands and resolutions as arbitrary tensor dimensions, OneHSI reparameterizes spectral channels in a shared continuous wavelength space, separates scale-invariant relational attention from scale-conditioned feature transformation, and balances cross-domain contributions to learn sensor-invariant representations. Cross-domain experiments demonstrate state-of-the-art performance and strong robustness under sensor and scale shifts, supporting physics-aligned representation learning as a principled path toward cross-sensor generalization.

ECCV 2026

Multi-History-Step SDE Inversion for Image Editing with Superior Regional Awareness

Haiyan Wei, Yunlong Wang, Huaibo Huang, Zhenan Sun, Kunbo Zhang

In recent years, diffusion stochastic differential equation (SDE)inversion and inversion-free methods have become prevalent for training-free image editing, as they can achieve faithful reconstruction withouttuning. However, existing approaches remain inefficient, exhibit limitedplasticity, and struggle to accurately preserve unedited regions. To ad-dress these issues, we propose MIEdit, a training-free editing frame-work based on SDE inversion. MIEdit introduces a predictor–correctormulti-history-step scheme to achieve superior editing quality with fewersteps. We further mitigate heterogeneity and conflict between the multi-conditioned noise residuals and gradient terms during sampling, improv-ing stability and editing plasticity under large edits. MIEdit also includesInversion-Time Automatic Semantic Angle Masking (IASM); it leveragesclassifier-free guidance to automatically generate semantic angle masksduring inversion and applies them throughout the sampling process forregional constraints, without extra user inputs. We additionally constructEditEval++ (30 fine-grained tasks, 1,000+ image–text–mask triplets) forcomprehensive evaluation; experiments show that MIEdit outperformsstate-of-the-art techniques. Project page: https://whywwwzzzg.github.io/MIEdit/.

ECCV 2026

SkelEM: Explicit Decoupling of Topology and Details for Self-supervised Axial Super-Resolution in Volume Microscopy

Bohao Chen, Yanchao Zhang, Yanan Lv, Chenxun Deng, Hua Han, Xi Chen

Volume microscopy, including electron and light microscopy,suffers from severe anisotropic resolution due to physical axial section-ing. Existing self-supervised axial super-resolution (ASR) methods facea trilemma bounded by overly smoothed regression textures, structuralhallucinations of pure diffusion models, and prohibitive inference latency.In this paper, we propose Skeleton-refinE Microscopy (SkelEM), a self-supervised framework that decouples ASR at the training-signal level:a frozen topological network and a diffusion refiner are optimized bydisjoint objectives, separating low-frequency topology formulation fromhigh-frequency detail enhancement. Building on this deterministic skele-ton, we exploit a unified cycle-consistent mechanism on input sparseslices to simultaneously extract a real-domain residual prior and bidi-rectionally align the diffusion refiner, washing away cross-plane arti-facts without synthetic bias. By truncating the reverse diffusion processwith this physical prior, SkelEM achieves high-fidelity detail restorationin merely ≤ 5 steps. To rigorously assess cross-instrument generaliza-tion, we further introduce BRAVE-ASR, a new benchmark of co-alignedanisotropic and isotropic volumes acquired on a Plasma-FIB instrument.Across public benchmarks, SkelEM achieves the most favorable balanceacross the fidelity-perception trade-off among self-supervised methods,with state-of-the-art downstream membrane segmentation performanceand robust zero-shot generalization across distinct modalities.

ECCV 2026

Mitigate Modality-Asymmetric Forgetting via Stabilizing Visual Representations in CLIP-Based Class-Incremental Learning

yuanhong zhang, Yanan Chen, Xin Zhang, Zhaoyang Wang, Weizhan Zhang, Muyao Yuan, Hongjin Niu, Lan Ma, Yuan Gao, Joey Tianyi Zhou

Continual Learning (CL) enables models to learn sequentially while retaining prior knowledge. Recently, CL under the CLIP framework has gained increasing attention for its remarkable cross-task generalization capability. Despite recent progress, existing approaches overlook modality-asymmetric forgetting, where the visual modality undergoes severe degradation during CL. In this paper, we empirically identify this phenomenon and provide evidence that the lack of a stable structural anchor in the visual branch may drive high-level visual features to over-adapt to new tasks, thereby exacerbating catastrophic forgetting. To address this issue, we propose Visual Anchor-based Structural Transfer (VAST) to stabilize visual representations during continual learning. Specifically, we integrate a cross-layer representation enhancement module to construct the stable visual anchor, and further employ an information-theoretic structural distillation mechanism to transfer them across tasks, promoting stable adaptation and mitigating catastrophic forgetting. Extensive experiments across multiple benchmarks demonstrate that our method consistently surpasses SOTA approaches without relying on any replay data. Code is available at VAST.

ECCV 2026

MedSPOT: A Workflow-Aware Sequential Grounding Benchmark for Clinical GUI

Rozain Shakeel, Abdul Ali, Muneeb Ganie, Tausifa Jan Saleem, Tajamul Ashraf

Despite the rapid progress of Multimodal Large Language Models (MLLMs), their ability to perform reliable visual grounding in high-stakes clinical software environments remains underexplored. Existing GUI benchmarks largely focus on isolated, single-step grounding queries, overlooking the sequential, workflow-driven reasoning required in real-world medical interfaces, where tasks evolve across interdependent steps and dynamic interface states. We introduce MedSPOT, a workflow-aware sequential grounding benchmark for clinical GUI environments. Unlike prior benchmarks that treat grounding as a standalone prediction task, MedSPOT models procedural interaction as a sequence of structured spatial decisions. The benchmark comprises 216 task-driven videos with 597 annotated keyframes, in which each task comprises 2–3 interdependent grounding steps within realistic medical workflows. This design captures interface hierarchies, contextual dependencies, and fine-grained spatial precision under evolving conditions. To evaluate procedural robustness, we propose a strict sequential evaluation protocol that terminates task assessment upon the first incorrect grounding prediction, explicitly measuring error propagation in multistep workflows. We further introduce a comprehensive failure taxonomy, including edge bias, small-target errors, no prediction, near miss, far miss, and toolbar confusion, to enable systematic diagnosis of model behavior in clinical GUI settings. By shifting evaluation from isolated grounding to workflow-aware sequential reasoning, MedSPOT establishes a realistic and safety-critical benchmark for assessing multimodal models in medical software environments. Code and data are available at https://github.com/Tajamul21/MedSPOT

ECCV 2026

Looking Back and Forth: Cross-Image Attention Calibration and Attentive Preference Learning for Multi-Image Hallucination Mitigation

Xiaochen Yang, Hao Fang, Jiawei Kong, Yaoxin Mao, Bin CHEN, Shu-Tao Xia

Although large vision-language models (LVLMs) have demonstrated remarkable capabilities, they are prone to hallucinations in multiimage tasks. We attribute this issue to limitations in existing attention mechanisms and insufficient cross-image modeling. Inspired by this, we propose a structured hallucination mitigation framework involving Cross-Image Attention calibration and Preference Learning (CAPL). CAPL explicitly enhances inter-image interactions at the architectural level while reinforcing reliance on genuine cross-image evidence during training, thereby improving the model’s perception and modeling of cross-image associations. Specifically, we (i) introduce a selectable image token interaction attention mechanism to establish fine-grained crossimage entity alignment and information flow; (ii) design a cross-image modeling–based preference optimization strategy that contrasts reasoning outcomes under full inter-image interaction and those obtained when images are mutually invisible, encouraging the model to ground its predictions in authentic visual evidence and mitigating erroneous inferences driven by textual priors. Experimental results demonstrate that CAPL consistently improves performance across multiple model architectures, achieving stable gains on both multi-image hallucination and general benchmarks. Notably, performance on single-image visual tasks remains stable or slightly improves, indicating strong generalization capability. The code is available at https://github.com/yyyxcleo/CAPL.

ECCV 2026

LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory

Junyi Zhang, Charles Herrmann, Junhwa Hur, Chen Sun, Ming-Hsuan Yang, Forrester Cole, Trevor Darrell, Deqing Sun

Feedforward geometric foundation models achieve strongshort-window reconstruction, yet scaling them to minutes-long videos isbottlenecked by quadratic attention complexity or limited effective mem-ory in recurrent designs. We present LoGeR (Long-context GeometricReconstruction), a novel architecture that scales dense 3D reconstruc-tion to extremely long sequences without post-optimization. LoGeRprocesses video streams in chunks, leveraging strong bidirectional priorsfor high-fidelity intra-chunk reasoning. To manage the critical challengeof coherence across chunk boundaries, we propose a learning-based hybridmemory module. This dual-component system combines a parametric Test-Time Training (TTT) memory to anchor the global coordinate frame andprevent scale drift, alongside a non-parametric Sliding Window Attention(SWA) mechanism to preserve uncompressed context for high-precisionadjacent alignment. Notably, this memory architecture allows LoGeR togeneralize to sequences of thousands of frames during inference, despitebeing trained on only 128 frames. When evaluated on standard bench-marks and a repurposed VBR dataset with sequences up to 19k frames,LoGeR substantially outperforms current state-of-the-art feedforwardmethods. It reduces the ATE on KITTI by over 74% while achievingrobust, globally consistent reconstruction across unprecedented horizons.

ECCV 2026

Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment

Ziyao Wang, Maonan Wang, Yucheng He, Xianping Ma, Ziyi Wang, Hongyang Zhang, Yirong Chen, Man On Pun

Cloud removal (CR) is essential for optical remote sensing,serving as a prerequisite for reliable downstream interpretation, suchas semantic segmentation and change detection. However, existing CRapproaches often prioritize visual realism while overlooking their im-pact on subsequent analytical tasks, leading to semantic drift and de-graded downstream performance. To address this issue, we propose Geo-Anchored Cloud Removal (GACR), a unified framework that jointlyensures faithful reconstruction and robust interpretability. At its core,GACR incorporates Observation-Anchored Residual Flow (OAR-Flow),which reformulates CR as a physically grounded residual inversion pro-cess. By anchoring the generative trajectory to the cloudy observationrather than pure noise, OAR-Flow enables fast, stable, and faithful re-construction. To further preserve semantic structures critical for down-stream interpretation, GACR integrates Geo-Contextual Prior Align-ment (GCPA) to constrain the reconstruction within a semantic manifoldinduced by a Vision Foundation Model (VFM). Consequently, GACRstrictly maintains the spatial-semantic integrity of complex landscapes.Extensive experiments across six CR datasets and twelve downstreamtasks demonstrate that GACR produces superior reconstruction qual-ity while consistently improving downstream task accuracy. The code isavailable at https://github.com/wzy6055/GACR.

ECCV 2026

Inclusive Interactive Collisions for Multi-View Consistent Compositional 3D Generation

Chang Liu, Mingwen Shao, Xiang Lv, Xinyuan Chen, Lingzhuang Meng, Qiao Zhang, Zhengyi Gong, Jinghao Hu

Recent breakthroughs in 3D generation have advanced no-tably with the development of text-to-image diffusion model. However,existing methods remain two practical challenges: (1) They primarilygenerate single 3D object, but struggle to generate multi-object compo-sitional 3D assets due to the lack of the modeling for Gaussian prim-itives in reasonable interactions. (2) They often suffer from cross-viewinconsistency during 3D optimization, as Score Distillation Samplinginherently performs on each single view, inevitably resulting in cross-view hallucinations. To solve above issues, we propose I2 C-3D, a noveloptimization-based method to generate multi-view consistent composi-tional 3D assets with reasonable interactions. Specifically, we proposean Inclusive Interactive Collisions strategy to guide Gaussian primitivesappearing in reasonable interaction regions naturally, thereby ensuringobjects in the compositional scene interact in a physically plausible andvisually coherent way. Additionally, to enhance multi-view consistency,Multi-View Adaptive Score Distillation Sampling is devised to distillmulti-view consistency prior and layout prior from pre-trained diffusionmodel by modulating attention map of instance token and spatial to-ken across viewpoints. Benefiting from above elaborate designs, I2 C-3Dnot only generates high-fidelity multi-view consistent compositional 3Dassets but also supports 3D editing flexibly, facilitating complex scenegeneration. Extensive experiments demonstrate our I2 C-3D outperformsexisting methods in generation quality and multi-view consistency.

ECCV 2026

Improving Image-to-Image Translation via a Rectified Flow Reformulation

Satoshi Iizuka, Shun Okamoto, Kazuhiro Fukui

In this work, we propose Image-to-Image Rectified Flow Re-formulation (I2I-RFR), a practical plug-in reformulation that recastsstandard I2I regression networks as continuous-time transport models.While pixel-wise I2I regression is simple, stable, and easy to adapt acrosstasks, it often over-smooths ill-posed and multi-modal targets, whereasgenerative alternatives often require additional components, task-specifictuning, and more complex training and inference pipelines. Our methodaugments the backbone input by channel-wise concatenation with a noise-corrupted version of the ground-truth target and optimizes a simplet-reweighted pixel loss. This objective admits a rectified-flow interpre-tation via an induced velocity field, enabling ODE-based progressiverefinement at inference time while largely preserving the standard su-pervised training pipeline. In most cases, adopting I2I-RFR requiresonly expanding the input channels, and inference can be performed witha few explicit solver steps (e.g., 3 steps) without distillation. Exten-sive experiments across multiple image-to-image translation and videorestoration tasks show broad perceptual improvements, with possibleperception–distortion trade-offs in highly pixel-aligned settings such assuper-resolution.

ECCV 2026

Hyperbolic Hierarchical Clustering for Visual Representation Learning

Jianan Wei, Guikun Chen, Zhiyuan Weng, Chunchao Guo, Yujia Wang, Wenguan Wang

We investigate the token mixer in vision backbones by revisit-ing clustering, one of the most classic approaches in machine learning. Aneffective token mixer is a fundamental component of modern vision back-bones like vision Transformers, facilitating information exchange betweenimage patches. Mainstream token mixers, which rely on convolution, at-tention, MLP, or their hybrids, primarily focus on navigating the trade-offbetween accuracy and computational cost. However, a significant draw-back of these methods is their black-box nature; their encoding processis opaque and lacks interpretability. Diverging from these opaque designs,we introduce ClusterMixer, a transparent token mixer that is grounded ina clustering paradigm and interpretable by design. ClusterMixer explicitlyformulates the token mixing process through a hierarchical clusteringmechanism. To model the natural, tree-like relationships inherent in visualdata, the clustering is performed in hyperbolic space, which is well-suitedfor embedding hierarchies with low distortion. Building on this innovation,we present HCFormer, a new backbone architecture that integratesClusterMixer with a series of meticulously designed clustering strategiesto ensure robust performance across tasks. Extensive experiments demon-strate that HCFormer consistently outperforms its counterparts acrossdiverse tasks, including image classification, object detection, instancesegmentation, and semantic segmentation. Considering its transparencyand efficacy, we hope HCFormer can facilitate a paradigm shift towardinterpretable backbones.

ECCV 2026

HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection

Jiyong Boo, ByeongIn Joung, Hyemin Yang, Kuk-Jin Yoon

Monocular 3D lane detection plays a critical role in autonomous driv-ing, yet recovering reliable 3D geometry from a single image remains challeng-ing due to inherent depth ambiguity. Prior methods project image features intoBird’s-Eye-View (BEV) space under a flat-ground assumption, causing geomet-ric distortion on real-world roads. Recent methods instead predict explicit heightmaps to capture non-planar surfaces, but still rely on sparse anchor-based regres-sion and exploit the recovered geometry merely for spatial transformation ratherthan semantic understanding. To overcome these limitations, we propose HSDF-Lane, which implicitly models the road surface as a Height-aligned Signed Dis-tance Field (HSDF) over a densely sampled 3D feature volume. Through dif-ferentiable rendering, the HSDF jointly produces an accurate height map andsurface-aligned features. We further introduce Lane-aware Semantic PositionalEncoding (LSPE), which injects a lane-existence prior derived from the surface-aligned features into the transformer queries, coupling geometric structure withsemantic guidance. Extensive experiments on the OpenLane benchmark showthat HSDF-Lane achieves state-of-the-art performance in both 3D lane detectionand height map estimation. The code is available at https://github.com/JiyongBoo/HSDF-Lane.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.