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ECCV 2026

CoCo-IR: Conversational Composed Image Retrieval

Shengcao Cao, Tanmaya Dabral, Zhongli Ding, Madhuri Shanbhogue, Kaifeng Chen, Zhe Li, Mojtaba Seyedhosseini, Liang-Yan Gui, Yu-Xiong Wang

Current instruction-based image retrieval systems are powerful but limited to single-turn interactions, failing to capture the iterative nature of complex, real-world visual searches. To overcome this limitation, we introduce Contextual Composed Image Retrieval (CoCo-IR), a novel task that enables users to progressively refine search results through interactions. We address this new task by proposing a new model based on a Large Multimodal Model (LMM) that functions as a context-aware reasoner for CoCo-IR. Our model interprets the entire interaction history to generate Transformable Image Embeddings (TIE) that evolve across turns. To fuel the model training without expensive human annotations, we develop a fully autonomous, scalable data engine that leverages LMMs to generate high-quality contextual retrieval data, and uses model-guided verification to mine challenging hard negatives. Extensive experiments demonstrate that our approach establishes new state-of-the-art performance: We achieve 39.4 mAP@5 on the challenging single-turn benchmark CIRCO; furthermore, on our new CoCo-IR benchmark, our model maintains robust performance with 44.1 R@1 on 4-turn dialogues, dramatically outperforming existing methods (28.2 4-turn R@1) that fail to handle multi-turn context. Project page: https://CoCo-IR.github.io.

ECCV 2026

BWAFDA: Block-wise Weighted Attention Fusion with Detail-aware for No-Reference Image Quality Assessment

Shilv Cai, Jian Jin, Tianang Chen, Zhuangzi Li, Weisi Lin

We present BWAFDA, a dual-branch no-reference image quality assessment model that replaces uniform averaging with detail-aware, block-wise fusion. The context encoder branch produces multi-scale block tokens, and a lightweight detail branch extracts high-frequency cues. These streams are combined late. First, block tokens are aggregated via block-wise attention across scales, guided by a soft multi-scale prior that stabilizes cross-scale interactions and preserves localized artifacts. The resulting descriptors are then fused per scale using learned weights. Subsequently, the detail cues are injected to refine distortion-sensitive evidence. Finally, a class-token-guided head performs “where-to-trust-what” selection over blocks to produce the quality score, without the need for region labels. Comprehensive evaluations on seven public NR-IQA benchmarks show state-of-the-art (SOTA) performance. Across multiple controlled ablations, we observe consistent drops in correlation compared to the full model, indicating that region-aware masking, contextual features, and multi-scale aggregation contribute jointly rather than redundantly. Overall, detail-aware, block-wise fusion delivers SOTA accuracy while preserving data efficiency and transferability by aligning localized artifact cues with global context.

ECCV 2026

Attention is Case-Sensitive

Maximilian Dillitzer, Tin Stribor Sohn, Jason Corso, Michael Auerbach

In human visual perception, uppercase lettering serves as anatural salience cue that captures attention within lowercase text. Inthis paper, we present a systematic empirical characterization study re-vealing that Large Language Models (LLMs) exhibit an analogous prop-erty: letter casing modulates internal attention allocation. Through anal-ysis across 13 models—nine LLMs and four Vision-Language Models(VLMs)—with diverse tokenization schemes, we show that formattingtarget information in alternating or uppercase against a lowercase con-text concentrates attention on those textual spans. In text this e!ectis universal, holding across every evaluated non-reasoning model. Weframe it as a previously under-explored latent property of pretrainedtransformers rather than a prescriptive method. Our investigation re-veals a central attention–performance divergence: while this “casing ef-fect” robustly shifts attention, its impact on downstream accuracy isnon-trivial—increased concentration does not inherently improve taskaccuracy and, in high-entropy contexts like alternating case, can degradeit. We further identify a boundary condition: the deliberative “thinking”phase in reasoning models acts as a semantic bu!er that mitigates ty-pographic sensitivity in text. Extending the study to VLMs, we find thee!ect transfers partially: the same prompt-side casing reorganizes cross-modal attention along two coupled axes—predominantly a macroscopicdisengagement from the image toward the text prompt, and secondar-ily a concentration of the residual visual attention on the target region.By isolating casing as a zero-shot mechanism for attention steering thatrequires no model access or fine-tuning, we provide a new foundationalunderstanding of how pretraining internalizes typographic emphasis.

ECCV 2026

Agentic Collaborative Cognition for Zero-Shot 3D Understanding

Wenxin Wang, Bo Zhang, Feng Chen, Zixuan Wang, Wen Li, Changsheng Li, Yinjie Lei

Recent advancements have explored agentic zero-shot 3D un-derstanding by reformulating it as video keyframe understanding withMultimodal Large Language Models (MLLMs). However, existing meth-ods face an intrinsic bottleneck due to the finite observation perspectivesinherent in videos and the implicit perception of 3D scenes. In this pa-per, we propose a collaborative multi-agent framework that assigns aPlanning Agent to handle high-level viewpoint planning and supplementnovel perspectives, and a Perception Agent to explicitly summarize the3D scene into a structured holistic cognitive map. Specifically, PlanningAgent first analyzes this cognitive map to determine query-relevant view-points and supplements missing critical perspectives to ensure compre-hensive observation. Subsequently, Perception Agent documents object-level attributes from these views by assigning consistent instance identi-fiers across viewpoints, thereby integrating fragmented observations intothe holistic cognitive map. In parallel, it provides feedback to filter outmismatched candidate objects and guide subsequent viewpoint planning.Through this closed-loop iterative process, two agents collaboratively fig-ure out candidates until Perception Agent determines that sufficient in-formation has been captured to complete the task. Extensive experimentsdemonstrate that our method achieves state-of-the-art performance on6 benchmarks, with improvements of 11.1% Acc@0.5 on ScanRefer, 14.6BLEU-1 on 3D-assisted dialog, and 2.1 EM on SQA3D. Project Page:https://zhangbo135.github.io/agentic-collaborative-cognition/

ECCV 2026

2D Features Are All You Need for 3D Shape Understanding

Jinfan Zhou, Richard Liu, Itai Lang, Rana Hanocka

We present MeshFM, an efficient feedforward framework for extracting rich features from 3D inputs. Our method distills 2D features from visual foundation models into 3D. We train a feedforward network to directly predict 3D features without requiring optimization during inference. The approach utilizes a two-stage training strategy. First, we optimize a feature field in 3D using only 2D feature supervision. Second, we train a network to regress this feature field. The entire procedure requires no 3D annotation, instead relying on the powerful information in 2D foundation models. We demonstrate that our learned features can be immediately applied to downstream tasks, including part segmentation, dense correspondence, and mesh deformation. Extensive experiments show that MeshFM, trained solely with 2D supervision, performs on par with methods trained explicitly with 3D supervision, even without task-specific fine-tuning. Moreover, our model is trained to be robust to extreme rotations of the input objects. Project page: https://threedle.github.io/MeshFM/

ECCV 2026

VLTR: Vision-Language Tool Reasoning for Instruction-Guided Image Editing

Yike Wang, Yitao Yu, Shaohua Sun, Ping Luo

Multi-tool agents for instruction-guided image editing re-main brittle under open-loop pipelines, where early routing errors propa-gate unchecked through the entire workflow. We propose VLTR (Vision-Language Tool Reasoning), a training-free framework that reformulatesediting as closed-loop tool reasoning over a directed acyclic graph (DAG)of atomic primitives. A generative decomposer first converts each natural-language instruction into executable primitives. A Bayes-UCB routerthen adaptively selects tools by combining contextual statistics withsemantic priors, balancing exploration and exploitation online. Aftereach execution, a heteroscedastic verifier produces a calibrated quality–uncertainty signal (q, σ 2 ) through inverse-variance fusion of three com-plementary assessment tiers. A verifier-guided scheduler uses this jointsignal to retry, reroute, or replan only the failed subgraph while preserv-ing validated ancestors, turning full-pipeline restarts into efficient localrepair. On PIE-Bench++, VLTR achieves the best overall VLM rankingamong all evaluated baselines. On MagicBrush, it delivers a 4.8× run-time speedup over the strongest multi-tool competitor with no qualityloss. Pilot evaluations on GEdit-Bench and RISEBench further confirmgeneralization to challenging edits.

ECCV 2026

VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM

Zihan Zhu, Wei Zhang, Moyang Li, Norbert Haala, Marc Pollefeys, Daniel Barath

We present VIGS-SLAM, a visual-inertial 3D Gaussian Splat-ting SLAM system that achieves robust real-time tracking and high-fidelity reconstruction. Although recent 3DGS-based SLAM methodsachieve dense and photorealistic mapping, their purely visual design de-grades under challenging conditions such as motion blur, low texture,and exposure variations. Our method tightly couples visual and inertialcues within a unified optimization framework, jointly optimizing cam-era poses, depths, and IMU states. It features robust IMU initialization,time-varying bias modeling, and loop closure with consistent Gaussianupdates. Experiments on five challenging datasets demonstrate our su-periority over state-of-the-art methods. The code will be made public.

ECCV 2026

ViBe: Ultra-High-Resolution Video Synthesis Born from Pure Images

Yunfeng Wu, Hongying Cheng, Zihao He, Songhua Liu

Transformer-based video diffusion models rely on 3D attention over spatial and temporal tokens, which incurs quadratic time and memory complexity and makes end-to-end training for ultra-highresolution videos prohibitively expensive. To overcome this bottleneck, we propose a pure image adaptation framework that upgrades a video Diffusion Transformer pre-trained at its native scale to synthesize higherresolution videos. Unfortunately, naively fine-tuning with high-resolution images alone often introduces noticeable noise due to the image–video modality gap. To address this, we decouple the learning objective to separately handle modality alignment and spatial extrapolation. At the core of our approach is Relay LoRA, a two-stage adaptation strategy. In the first stage, the video diffusion model is adapted to the image domain using low-resolution images to bridge the modality gap. In the second stage, the model is further adapted with high-resolution images to acquire spatial extrapolation capability. During inference, only the high-resolution adaptation is retained to preserve the video generation modality while enabling high-resolution video synthesis. To enhance fine-grained detail synthesis, we further propose a High Frequency Awareness Training Objective, which explicitly encourages the model to recover high-frequency components from degraded latent representations via a dedicated reconstruction loss. Extensive experiments demonstrate that our method produces ultra-high-resolution videos with rich visual details without requiring any video training data, even outperforming previous state-ofthe-art models trained on high-resolution videos by 0.8 on the VBench benchmark. Code is available here.

ECCV 2026

VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension

Zeyu Ling, Bo Han, Shiyang Li, Jikang Cheng, Hongdeng Shen, Changqing Zou

Human motion modeling must support generation from text,music, and speech, comprehension through captioning, temporal com-pletion, and multi-person coordination—yet existing methods typicallytackle each scenario with a separate, task-specific system. We arguethat discrete tokenization plus autoregressive (AR) modeling possessesunique compositional advantages for building a unified motion model:all modalities share a single vocabulary and training objective, tasks aredefined by token arrangement rather than architectural changes, andthe same model naturally supports both generation and understand-ing by simply reordering the sequence. However, realizing this potentialhas been blocked by two obstacles. First, current motion tokenizers useroot-relative, non-causal, frame-level encodings that discard world-spaceinformation, break consistency with forward-only AR decoding, and scalepoorly—confining existing discrete AR models to text–motion pairs insingle-human settings. Second, multi-modal motion data with paired textand audio annotations remains scarce. We address both obstacles. Weintroduce VerMoVQ, a causal spatio-temporal VQ-VAE that factorizeseach frame into low-dimensional body tokens while preserving world-space root trajectories, naturally enabling multi-person tokenization viaper-agent concatenation and arbitrary-length encoding through stricttemporal causality. We curate MotionHub, a \sim 380K-clip corpus in unifiedSMPL representation with large-scale multi-modal annotations spanningnine motion tasks. Built on these foundations, we train VersatileMo-tion, a single decoder-only AR Transformer that covers text\leftrightarrow motion,music\leftrightarrow dance, speech

ECCV 2026

Unmasking-Time Visual Calibration for Hallucination Mitigation in Multimodal Discrete Diffusion Language Models

Tian Qin, Junzhe Chen, TIANSHU ZHANG, Lijie Wen

Despite the recent success of multimodal discrete diffusionlanguage models (multimodal dLLMs) in generating text through iter-ative demasking with bidirectional attention, object hallucination re-mains a critical challenge. Existing hallucination mitigation methods aredesigned for autoregressive generation and encounter fundamental mis-matches when applied to multimodal dLLMs, either doubling inferencecost through contrastive decoding or suffering from ambiguous step andposition alignment under bidirectional context. To address this, we pro-pose Unmasking-Time Visual Calibration (UVC), a lightweight, training-free framework that mitigates hallucinations natively within the demask-ing process. UVC extracts multi-granularity contrastive activation shiftvectors offline by comparing model responses to clean and visually de-graded inputs, identifies the most visually informative attention headsvia per-head AUC scoring, and injects the pre-computed calibration sig-nals exclusively at still-masked positions during inference, requiring noadditional forward passes. Extensive experiments on POPE and MMEwith two representative multimodal dLLMs demonstrate that UVC con-sistently reduces hallucinations by a substantial margin while introducingless than one percent inference overhead. Additional evaluation on open-ended captioning with the CHAIR metric further confirms that UVC sub-stantially lowers hallucination rates without affecting generation length,and generalizes effectively across benchmarks without retraining. Codeis available at https://github.com/THU-BPM/UVC.

ECCV 2026

Unified Prediction and Planning via Conflict-Aware Disjoint Parameter Training

Taewon Seo, Seonae Jeon, Giwon Lee, Kuk-Jin Yoon, Daehee Park

Accurate motion prediction of surrounding agents and safemotion planning are two closely coupled key tasks for social robot nav-igation in crowded environments. Deploying these systems on resource-constrained edge devices necessitates compact, unified models that canperform both tasks simultaneously. However, within these compact sharedencoders, recent unified models often overlook severe representationalconflicts that arise from the distinct objectives of predicting neighbor be-haviors versus ego-centric safety planning. To address this issue, we firstidentify the Skill Conflict—a phenomenon where overlapping parameterassignments cause distinct tasks to compete for the same weights, pre-venting the model from fully specializing in individual skills. To resolvethis, we propose a novel model-merging-based framework, Disjoint Pa-rameter Training (DPT). DPT mitigates performance degradationcaused by Skill Conflict through distributed parameter learning, whichseparates the key parameter regions of each task while preserving theircore capabilities prior to merging. In addition, we observe that sparsemerging, which selectively integrates only the most influential param-eters for each task rather than combining all task-specific parameters,yields optimal performance by preventing interference among adjacentfeatures and concentrating representational capacity. DPT can be ap-plied in parallel with a variety of merging methods. Evaluated on stan-dard crowd navigation benchmarks (JRDB and JTA), our frameworkdemonstrates superior performance, validating its versatility and effec-tiveness for safe, resource-efficient robot navigation. The project page isavailable at: https://dpt2026.github.io/

ECCV 2026

UniCSG: Unified High-Fidelity content-constrained style-driven generation via Staged Semantic and Frequency Disentanglement

Jingwei Yang, Ruoxi Wu, Wei Shen, Meng Li, Yulong Liu, Huimin She, Lunxi Yuan

Style transfer must match a target style while preservingcontent semantics. DiT-based diffusion models often suffer from con-tent–style entanglement, leading to reference-content leakage and unsta-ble generation. We present UniCSG, a unified framework for content-constrained, style-driven generation in both text-guided and reference-guided settings. UniCSG employs staged training: (i) a latent-space se-mantic disentanglement stage that combines low-frequency preprocess-ing with conditioning corruption to encourage content–style separation,and (ii) a latent-space frequency-aware detail reconstruction stage thatrefines details via multi-scale frequency supervision. We further incorpo-rate pixel-space reward learning to align latent objectives with percep-tual quality after decoding. Experiments demonstrate improved contentfaithfulness, style alignment, and robustness in both settings.

ECCV 2026

RGB-Pointmap Pretraining for Unified 3D Scene Understanding

Ye Mao, Weixun Luo, Ranran Huang, Junpeng Jing, Krystian Mikolajczyk

Pretraining 3D encoders through alignment with ContrastiveLanguage–Image Pre-training (CLIP) has emerged as a promising direc-tion to learn generalizable representations for 3D scene understanding. Inthis paper, we propose UniScene3D, a transformer-based framework thatlearns unified 3D scene representations from multi-view RGB–Pointmapby leveraging the priors of a pretrained 2D foundation model. For robustRGB-Pointmap representation learning, we introduce novel cross-viewgeometric alignment and grounded view alignment to enforce geome-try and semantic consistency across views. Extensive low-shot and task-specific fine-tuning across viewpoint grounding, scene retrieval, sceneclassification, and 3D visual question answering achieves state-of-the-artperformance. These results establish UniScene3D as an effective frame-work for unified 3D scene understanding.

ECCV 2026

Training-Free Refinement of Flow Matching with Divergence-based Sampling

Yeonwoo Cha, Jaehoon Yoo, Semin Kim, Yunseo Park, Jinhyeon Kwon, Seunghoon Hong

Flow-based models learn a target distribution by modelinga marginal velocity field, defined as the average of sample-wise velocitiesconnecting each sample from a simple prior to the target data. However,when sample-wise velocities conflict at the same intermediate state, thisaveraged velocity can misguide samples toward low-density regions, de-grading generation quality. To address this issue, we propose the FlowDivergence Sampler (FDS), a training-free framework that refines in-termediate states before each solver step. Our key finding reveals thatthe severity of this misguidance is quantified by the divergence of themarginal velocity field that is readily computable during inference witha well-optimized model. FDS exploits this signal to steer states towardless ambiguous regions. As a plug-and-play framework compatible withstandard solvers and off-the-shelf flow backbones, FDS consistently im-proves fidelity across various generation tasks including text-to-imagesynthesis, and inverse problems.

ECCV 2026

DepWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

Seok-Young Kim, Abdelrahman Elskhawy, Taewook Ha, Dooyoung Kim, Eunjae Shin, Benjamin Busam, Woontack Woo

We present DeWorldSG, a novel framework that generatesspatio-temporally robust 3D Semantic Scene Graphs from RGB-D se-quences. Existing methods often struggle to construct reliable 3D scenegraphs due to unstable 3D object representations and missing relationscaused by frame-wise inference. DeWorldSG addresses these issues byestimating instance-level geometric 3D Gaussian distributions throughdepth-guided filtering and representing each object as a probabilistic 3Dnode rather than a single projected point. To mitigate relational sparsityfrom frame-wise inference, our framework further aggregates spatiotem-poral evidence across object pairs and refines relations using contextualpriors derived from a world model (V-JEPA 2). Experiments on the3DSSG and ReplicaSSG datasets demonstrate state-of-the-art (SoTA)performance in both object and predicate prediction, while producingtemporally consistent scene structures. In particular, our method im-proves triplet recall by 77.4% and predicate recall by 23.2% over priorSoTA approaches, making it suitable for robotic manipulation and ARapplications. Our code and models are open-sourced.

ECCV 2026

Think While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models

Lu Wang, Zhuoran Jin, Yupu Hao, Yubo Chen, Kang Liu, Yulong Ao, Jun Zhao

Multimodal large language models (MLLMs) have demon-strated strong performance in offline video understanding tasks, but mostremain constrained to offline inference or exhibit weak online reasoningability, rendering online multi-turn interaction over continuously arriv-ing video streams challenging. Existing streaming approaches adopt aninterleaved perception-generation paradigm, which precludes concurrentperception and generation and induces early memory decay with grow-ing streams, degrading long-range dependency modeling. We proposeThink While Watching, a memory-anchored streaming video reason-ing framework that maintains continuous segment-level memory duringmulti-turn interaction. We construct a three-stage, multi-round, chain-of-thought (CoT) dataset with a stage-matched training strategy while en-forcing strict causality in streaming reasoning via a segment-level stream-ing causal mask and streaming positional encoding. At inference, we de-sign an efficient pipeline that overlaps watching and thinking and adap-tively selects the optimal attention backend. We evaluate our methodunder single-round and multi-round streaming input protocols. Basedon Qwen3-VL, we improve single-round accuracy by 2.6% on Streaming-Bench and 3.79% on OVO-Bench. In the multi-round protocol, we main-tain performance while reducing output tokens by 56%. Code is avail-able at: https://github.com/wanglu-cs/Think_While_Watching.

ECCV 2026

Test-Time Noise Guided Adaptation for Realistic Autoregressive Video Generation

Dimitrios Karageorgiou, Symeon Papadopoulos, Ioannis Kompatsiaris, Efstratios Gavves

Autoregressive video diffusion models have enabled the gener-ation of arbitrarily long videos by removing conditioning on future frames,thus greatly improving computational efficiency. Yet, they suffer from er-ror accumulation over time, as the denoised sequence gradually drifts awayfrom the conditioning distribution seen during training. Recent advancesattempt to reduce this error by anchoring each generated frame to thelearned manifold of real ones. However, even when all generated individualframes lie close to the real manifold, there are trajectories which the modellacks sufficient knowledge to continue without exiting it, thus reachinga terminal point. To prevent the model from being trapped in terminalpoints, we start from the hypothesis that for well-modeled future trajec-tories the distribution of the predicted noise should match the one of theforward noising process. To enforce such a prior at test time, we introduceTerminal points Avoidance through Noise Guided Optimization (TANGO),which uses the diffusion model as a critic of its own outputs, by predictingone step forward and requiring an isotropic Gaussian noise prediction. Weuse the deviation from this expected noise distribution to search for an al-ternative trajectory that does not lead to a terminal point. Our approachachieves a 3.1% absolute improvement on VBench over state-of-the-art,while reducing Fréchet Video Distance by 28.3% on average across 15 svideos. Our code is available on https://mever-team.github.io/tango.

ECCV 2026

TerrainGraphNet: Terrain-Constrained Graph Reasoning for Landslide Segmentation

SHAHID SHAFI DAR, Pranjal Pandey, Nagendra Kumar

Accurate landslide segmentation from high-resolution remote sensing imagery remains challenging due to weak spectral contrast and strong dependence on terrain structure. Unlike appearance-defined objects, landslides follow geomorphological patterns governed by slope continuity and elevation discontinuities. However, most existing segmentation methods treat landslides as generic semantic regions and incorporate Digital Elevation Models (DEM) through simple feature fusion without explicitly enforcing terrain-consistent spatial reasoning. To address this limitation, we reformulate landslide segmentation as a terrainconditioned structured prediction problem and propose TerrainGraphNet, a terrain-aware graph reasoning framework that embeds geomorphological constraints into representation learning and spatial propagation. The proposed method introduces terrain-modulated feature interaction, where elevation structure adaptively regulates visual representations, and terrain-aware graph construction, where spatial connectivity is defined jointly by feature similarity and slope continuity. This formulation can be interpreted as learning a terrain-weighted smoothness prior that encourages predictions to respect geomorphological coherence. Extensive experiments on three benchmark datasets demonstrate consistent improvements over strong CNN and transformer baselines. In addition to higher IoU and F1 scores, TerrainGraphNet significantly improves boundary accuracy and topological consistency, highlighting the importance of terrain-constrained reasoning for reliable landslide delineation.

ECCV 2026

Steerable Vision Transformers

Jona Ruthardt, Manu Gaur, Deva Ramanan, Makarand Tapaswi, Yuki Asano

Pretrained Vision Transformers (ViTs) such as DINOv2 andMAE provide generic image features that can be applied to a varietyof downstream tasks such as retrieval, classification, and segmentation.However, such representations tend to focus on the most salient visualcues in the image, with no way to direct them toward less prominentconcepts of interest. In contrast, Multimodal LLMs can be guided withtextual prompts, but the resulting representations tend to be language-centric and lose their effectiveness for generic visual tasks. To addressthis, we introduce Steerable Visual Representations, a new class of vi-sual representations, whose global and local features can be steered withnatural language. While most vision-language models (e.g., CLIP) fusetext with visual features after encoding (late fusion), we inject text di-rectly into the layers of the visual encoder (early fusion) via lightweightcross-attention. We introduce benchmarks for measuring representationalsteerability, and demonstrate that our steerable visual features can focuson any desired object in an image while preserving the underlying rep-resentation quality. Our method also matches or outperforms dedicatedapproaches on anomaly detection and personalized object discrimination,exhibiting zero-shot generalization to out-of-distribution tasks.Project Website: jonaruthardt.github.io/project/SteerViTPrompt CLS AttentionSteerable Visual RepresentationsGoal: control what vision features encode New Pareto FrontierEval: retrieving images w/ prompted objects Previous SoTAUse: task-specific adaptationFig. 2: SteerViT produces high-quality visual representations that can besteered by text. Left: Traditional (non-steerable) representations like DINOv2 tendto focus on the dominant object in an image and retrieve images with the same object.SteerViT can adapt to a text prompt, enabling retrieval of images even with smallobjects of interest. Right: We compare SteerViT to prior work in terms of its ability toadapt to text (measured by text-guided image retrieval (cf. Sec. 4.1)) and the qualityof the visual representation (measured by the accuracy of linear probing for the CLSfeature and semantic segmentation for patch features). While models typically tradeoff steerability for representation quality, SteerViT preserves both. By modulating agating factor (Eq. (2)), SteerViT achieves a new Pareto frontier.

ECCV 2026

OCA: ODE-Driven Cross-Attention for Image-to-Point-Cloud Registration

Pei An, Jiaqi Yang, Yulong Wang, Quan Siwen, Liangliang Nan

Cross-attention is a crucial component in learning-based imageto-point-cloud (I2P) registration. Although existing cross-attention mechanisms have achieved promising progress, attention ambiguity remains a fundamental challenge that hinders the learning of discriminative 2D- 3D correspondences. To address this problem, we revisit cross-attention and establish ordinary differential equations (ODEs) to model the ideal I2P feature interaction. Based on this formulation, we develop an ODEdriven cross-attention (OCA) module that refines feature representations and attention matrices through ODEs. In practice, OCA can be seamlessly integrated into existing I2P registration frameworks. To validate its effectiveness, we incorporate OCA into five state-of-the-art baselines and evaluate on four public benchmark datasets. Experimental results demonstrate that OCA improves registration recall by up to 5%, 9%, and 15% under the standard, fine-tuning, and zero-shot settings, respectively. Code is released at github.com/anpei96/oca-i2p-demo.

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