ECCV 2026 论文列表

Recent Vision-Language Models (VLMs) have achieved re-markable success in visual understanding, driven by the growing avail-ability of high-quality image-text pairs. However, the performance ofVLMs often degrades in the video domain due to the essential needfor temporal understanding and the scarcity of large-scale annotatedvideo data. In this work, we focus on marine video understanding, whichbrings further challenges: first, it requires substantial domain expertise;and video VLMs usually struggle with localizing and interpreting criti-cal information from marine videos, as the informative events are typ-ically sparse, unpredictable, and unevenly distributed. To address thesechallenges, we carefully curate the first event-centric marine video un-derstanding dataset called MarineEVT, which features 20K multi-task,video-level visual question-answering pairs spanning multiple dimensionsof marine understanding and analysis. Meanwhile, based on MarineEVT,we decompose marine video understanding as an Event-centric VisualTool-integrated Reasoning process (EVT-R1 for short), where we lever-age powerful visual tools to drive the model to localize and interpretcritical information aligned with visual questions and human intent. Todemonstrate its effectiveness, we compare EVT-R1 against 11 SOTAVLMs in different settings. EVT-R1 outperforms the top open-sourceand top commercial models by 5.22 and 11.09, respectively. MarineEVTand EVT-R1 lay the foundation for ecological discovery and marine ed-ucation, fostering the development of VLMs capable of interpreting ma-rine dynamics, reasoning about ecological interactions, and supportingsustainable ocean video understanding and analysis.

Vision-Language-Action (VLA) models have emerged as apromising paradigm for building general-purpose robotic agents. How-ever, the VLA landscape remains highly fragmented and complex: asexisting approaches vary substantially in architectures, training data,embodiment configurations, and benchmark-specific engineering. In thiswork, we introduce StarVLA-α, a simple yet strong baseline designedto study VLA design choices under controlled conditions. StarVLA-αdeliberately minimizes architectural and pipeline complexity to reduceexperimental confounders and enable systematic analysis. Specifically,we re-evaluate several key design axes, including action modeling strate-gies, robot-specific pretraining, and interface engineering. Across uni-fied multi-benchmark training on LIBERO, SimplerEnv, RoboTwin, andRoboCasa, the same simple baseline remains highly competitive, indi-cating that a strong VLM backbone combined with minimal design isalready sufficient to achieve strong performance without relying on ad-ditional architectural complexity or engineering tricks. Notably, our sin-gle generalist model outperforms π0.5 by 20% on the public real-worldRoboChallenge benchmark. We expect StarVLA-α to serve as a solidstarting point for future research in the VLA regime. Code will be re-leased at https://github.com/starVLA/starVLA.

While Multimodal Large Language Models (MLLMs) ex-cel in visual understanding, the quadratic complexity imposed by densehigh-resolution visual tokens poses significant challenges for edge-devicedeployment. Current training-free pruning strategies predominantly de-pend on attention weights. However, viewed from the frequency domain,the self-attention mechanism functions as a low-pass filter, causing thediscard of essential high-frequency signals (e.g., textures and edges) andresulting in feature over-smoothing. In this paper, we present Spatial-Window Integrated Frequency-aware Token Pruning (SWIFT), a seam-less plug-and-play framework designed for efficient token compression.SWIFT leverages two novel components: a Frequency-Aware Indicator(FAI), which identifies fine-grained details by estimating high-frequencyresiduals through matrix factorization, and a Spatial-Window Integra-tion (SWI) module, which prevents spatial structure collapse and posi-tional bias via localized retention. Extensive evaluations on Qwen2.5VL-3B and LLaVA benchmarks show that under a practical 50% compressionratio, SWIFT achieves a 2.10× speedup with near-lossless performance.Under an extreme 75% compression stress test, SWIFT yields an averageperformance drop of 5.1% across 8 benchmarks, while delivering a 2.66×speedup and 69.07% KV cache reduction. SWIFT exhibits superior ro-bustness in detail-intensive tasks compared to SOTA methods, providinga potent lightweight solution for on-device MLLM applications. Our codeis available at https://github.com/damo-lgl/SWIFT.

6-DoF LiDAR-based localization is a fundamental capabilityfor autonomous systems operating in large-scale outdoor environments.Many deep-learning-based localization methods have achieved promisingperformance so far. However, as one of the always-on modules competingfor limited on-board computational resources, the localization module isexpected to consume only a small portion of the overall compute bud-get. Most existing learning-based methods are still too heavy for thispurpose. In contrast, binary neural networks (BNNs) offer an appealingsolution, but the 1-bit compression causes severe information loss andperformance drop. In this paper, we address this challenge by propos-ing Binarized LiDAR-based Localization (BiLoc), the first binary neuralnetwork framework for 6-DoF LiDAR localization. Specifically, we rein-terpret the training of BNNs from the perspective of the information-bottleneck principle, aiming at retaining minimal yet sufficient represen-tations for pose estimation while suppressing redundant variations. Andwe introduce an auxiliary objective that adaptively regulates informationretention in the binary encoder, effectively mitigating the informationloss caused by binarization. This auxiliary objective provides additionaloptimization signals that compensate for the limited representationalcapacity and the gradient mismatch inherent in BNNs. Extensive exper-iments on large-scale outdoor LiDAR datasets demonstrate that BiLocestablishes a new state of the art for LiDAR localization with BNNs.

Tool-augmented vision–language models (VLMs) can solve multi-modal, multi-step tasks by calling external tools, yet they remain fragile in prac-tice. Existing works have two common gaps. Supervised fine-tuning (SFT) is builtmostly on successful trajectories and offers little signal for recovery after tool fail-ures, while sparse trajectory-level RL rewards provide limited guidance on whichstep failed and how to repair it. We introduce ReGRPO (Reflection-augmentedGroup Relative Policy Optimization), a framework that learns reflection-guidedcorrection in tool-using agents. ReGRPO starts with a structured reflective dataengine: we execute near-miss actions to collect grounded failure observations,then build Reflection-of-Thought triplets (ErrorType, Evidence, FixPlan) pairedwith corrected actions for warm-start SFT. We then optimize reflection tokens andcorrective actions jointly within local trajectories using group-relative advantages,and include a reflection-cost term to reduce unnecessary reflection. Experimentson GTA and GAIA show that, under the same backbone and tool suite, ReGRPOconsistently outperforms strong open-source baselines and achieves the best resultsamong the compared open-source controllers. Code and RoT data are available athttps://github.com/showlab/ReGRPO.

Vision-Language-Action (VLA) models have advancedrapidly, yet most gains still come from larger backbones, larger embod-ied datasets, or expensive iterative decoders, while action representa-tion remains under-explored. We argue that action space design is aprimary bottleneck and present LEAP (Latent-Enhanced ActionPrototyping)-VLA, a two-stage framework that learns a structuredaction latent space from demonstrations via Multi-level Soft ResidualQuantization (MSRQ), then trains a lightweight VLM-external aligner topredict actions by prototype-aware soft selection in that space. This de-sign replaces discrete code assignment and multi-step denoising with fullydifferentiable single-pass latent prediction, preserving pretrained vision-language alignment without modifying the VLM. Empirically, LEAP-VLA outperforms the state-of-the-art methods with substantially fewertrainable parameters and no embodied pretraining, showing that a well-structured continuous action latent space can offset model scale and datarequirements.

On-device learning is essential for personalization, privacy,and long-term adaptation in resource-constrained environments. Achiev-ing this requires efficient learning, both fine-tuning existing models andcontinually acquiring new tasks without catastrophic forgetting. Yet bothsettings are constrained by high memory cost of storing activations dur-ing backpropagation. Existing activation compression methods reducethis cost but rely on repeated low-rank decompositions, introducing com-putational overhead. Also, such methods have not been explored forcontinual learning. We propose LANCE (Low-rank Activation Compres-sion), a framework that performs one-shot higher-order Singular ValueDecomposition (SVD) to obtain a reusable low-rank subspace for ac-tivation projection. This eliminates repeated decompositions, reducingboth memory and computation. Moreover, fixed low-rank subspaces fur-ther enable on-device continual learning by allocating tasks to orthogo-nal subspaces without storing large task-specific matrices. Experimentsshow that LANCE reduces activation storage by 25× to 380× depend-ing on the architecture, while maintaining accuracy comparable to fullbackpropagation on CIFAR-10/100, Oxford-IIIT Pets, Flowers102, andCUB-200 datasets. On continual learning benchmarks (Split CIFAR-100,Split MiniImageNet, 5-Datasets), it performs competitively with orthog-onal gradient projection methods at a fraction of the memory cost. Theseresults position LANCE as a practical and scalable solution for efficientfine-tuning and continual learning on edge devices.3

The intrinsic link between facial motion and speech is oftenoverlooked in generative modeling, where talking head synthesis andtext-to-speech (TTS) are typically addressed as separate tasks. Thispaper introduces JAM-Flow, a unified framework to simultaneously syn-thesize and condition on both facial motion and speech. Our approachleverages flow matching and a novel Multi-Modal Diffusion Transformerarchitecture, integrating specialized Motion-DiT and Audio-DiT modules.These are coupled via selective joint attention layers and incorporate keyarchitectural choices, such as temporally aligned positional embeddingsand localized joint attention masking, to enable effective cross-modalinteraction while preserving modality-specific strengths. By analyzing andleveraging pretrained representation embeddings, JAM-Flow is designedfor efficient, near real-time sampling. Trained with an inpainting-styleobjective, JAM-Flow supports a wide array of conditioning inputs (in-cluding text, reference audio, and reference motion) facilitating taskssuch as synchronized talking head generation from text, audio-drivenanimation, and much more, within a single, coherent model. JAM-Flowsignificantly advances multi-modal generative modeling by providing apractical solution for holistic audio-visual synthesis. Project website

Unsupervised physical parameter estimation from video lacksa common benchmark: existing methods evaluate on non-overlappingsynthetic data, the sole real-world dataset is restricted to single-bodysystems, and no established protocol addresses governing-equation identi-fication. This work introduces IRIS, a high-fidelity benchmark comprising240 real-world videos captured at 4K resolution and 60 fps, spanning bothsingle- and multi-body dynamics with independently measured ground-truth parameters and uncertainty estimates. Each dynamical systemis recorded under controlled laboratory conditions and paired with itsgoverning equations, enabling principled evaluation. A standardized evalu-ation protocol is defined encompassing parameter accuracy, identifiability,extrapolation, robustness, and governing-equation selection. Multiplebaselines are evaluated, including a multi-step physics loss formulationand four complementary equation-identification strategies (VLM tempo-ral reasoning, describe-then-classify prompting, CNN-based classification,and path-based labelling), establishing reference performance across allIRIS scenarios and exposing systematic failure modes that motivate futureresearch. The dataset, annotations, evaluation toolkit, and all baselineimplementations are publicly released.

Di!usion models have emerged as a dominant paradigm ingenerative modeling, enabling high-fidelity sampling from complex datadistributions. Despite impressive capabilities, controlling di!usion mod-els to produce outputs aligned with user intent remains an open chal-lenge, especially when balancing global coherence with local precision.Existing control mechanisms vary in the granularity of their conditioningsignals. For example, textual prompts guide generation globally throughhigh-level semantics, while ControlNet-like approaches secure precise lo-cal structure via dense conditions. In this work, we introduce Histogram-constrained Image Generation (HIG), a novel control mechanism thatfalls into the middle ground of control granularity. Our framework en-forces user-specified distributional constraints (e.g., color histograms orlatent token distributions) during the generation process with exact pre-cision. We model such control as an optimal transport (OT) problemand apply explicit guidance transformations during sampling, therebydriving the di!usion trajectory to align with the desired histogram. Wedemonstrate the versatility of HIG across diverse applications, includingconstrained generation via color/latent histograms and high-capacity in-formation embedding through histogram-level encoding. Our findings un-derscore the promise of distributional control, a flexible and interpretablecontrol scheme that is fully compatible with existing control mechanisms,diversifying the hybrid strategies for controllable image generation. Ourproject page is available at: https://maps-research.github.io/hig/.

Robotic imitation learning faces a fundamental trade-off be-tween modeling long-horizon dependencies and enabling fine-grained closed-loop control. Existing fixed-frequency action chunking approaches strug-gle to achieve both. Building on this insight, we propose HiPolicy, ahierarchical multi-frequency action chunking framework that jointly pre-dicts action sequences at different frequencies to capture both coarsehigh-level plans and precise reactive motions. We extract and fuse hi-erarchical features from history observations aligned to each frequencyfor multi-frequency chunk generation, and introduce an entropy-guidedexecution mechanism that adaptively balances long-horizon planningwith fine-grained control based on action uncertainty. Experiments ondiverse simulated benchmarks and real-world manipulation tasks showthat HiPolicy can be seamlessly integrated into existing 2D and 3D gen-erative policies, delivering consistent improvements in performance whilesignificantly enhancing execution efficiency.

Real-world video super-resolution (VSR) faces in-the-wilddegradations whose artifacts accumulate over time, yet edge deploymentrequires static execution, and quantization parameters must be fixed af-ter compilation for low-overhead inference. We propose Temporal-AwareQuantization (TAQ), a novel static post-training quantization frame-work that uses video structure only in offline calibration. TAQ calibratessequence-specific activation bounds, refines them with a temporal con-sistency objective we propose that aligns inter-frame changes betweenfloating-point and quantized outputs without weight retraining, and en-sembles the refined bounds into one deployable set of static param-eters. Across REDS, SPMCS, UDM10, and VideoLQ, TAQ improvesperceptual and temporal quality, reducing LPIPS by up to 0.0334 andTLPIPS by up to 4.7344 over Static PTQ baselines under identical cal-ibration. On NVIDIA Jetson Orin Nano, TAQ enables TensorRT INT8with up to 3.56× speedup (1.15 fps), while a dynamic INT8 baseline isslower than FP32 (0.79×, 0.266 fps vs. 0.325 fps). Code is available athttps://github.com/imaboybut/TAQ.

We present SLAM-Former, a neural approach that integratesfull SLAM capabilities into a single transformer. Similar to traditionalSLAM systems, SLAM-Former comprises both a frontend and a back-end that operate in tandem. The frontend processes sequential monoc-ular images in real-time for incremental mapping and tracking, whilethe backend performs global refinement to ensure a geometrically con-sistent result. This alternating execution allows the frontend and back-end to mutually promote one another, enhancing overall system perfor-mance. Comprehensive experimental results demonstrate that SLAM-Former achieves superior or highly competitive performance comparedto state-of-the-art dense SLAM methods.

Implicit Neural Representations (INRs) are widely used formodeling continuous 2D images, enabling high-fidelity reconstruction,super-resolution, and compression. Architectures such as SIREN, WIRE,and FINER demonstrate their ability to capture fine image details. How-ever, conventional INRs lack explicit geometric structure, limiting localediting, and integration with physical simulation. To address these lim-itations, we propose GaINeR (Geometry-Aware Implicit Network Rep-resentation), a novel framework for 2D images that combines trainableGaussian distributions with a neural network-based INR. For a givenimage coordinate, the model retrieves the K nearest Gaussians, aggre-gates distance-weighted embeddings, and predicts the RGB value viaa neural network. This design enables continuous image representation,interpretable geometric structure, and flexible local editing, providing afoundation for physically aware and interactive image manipulation. Ourmethod supports geometry-consistent transformations, seamless super-resolution, and integration with physics-based simulations. Moreover, theGaussian representation allows lifting a single 2D image into a geometry-aware 3D representation, enabling depth-guided editing. Experimentsdemonstrate that GaINeR achieves state-of-the-art reconstruction qual-ity while maintaining flexible and physically consistent image editing.

We present FoundationGeo, a two-stage framework that ex-plicitly bridges relative and metric prediction via spatial calibration andprincipled data design. Stage 1 learns a high-fidelity, affine-invariant ge-ometry model by initializing with DINOv3 and training on a curated10.2M-sample multi-domain corpus with complementary local–detail su-pervision, yielding sharp boundaries and strong cross-domain general-ization. Stage 2 moves beyond global scaling by introducing lightweightpixel-wise calibration fields for metric estimation: a scale field for spa-tially varying metric alignment and a ray-direction correction field thatmitigates directional bias in point-map geometry, together producingmetrically consistent 3D point maps. Beyond model design, we identifycamera intrinsic coverage, especially focal length distribution mismatchbetween training and test data, as a key bottleneck for zero-shot met-ric generalization: performance drops sharply when test intrinsics falloutside the training distribution. To address this, we synthesize addi-tional training data across diverse focal lengths using a Blender-baseddata engine, repairing under-covered focal regimes and improving robust-ness under intrinsic shift. Extensive zero-shot evaluations across sevenbenchmarks show that FoundationGeo significantly strengthens cross-domain robustness, staying near the top across diverse domains whileavoiding the sharp cross-domain performance drops observed in othermethods. This consistency translates into the best overall performance,surpassing heavier baselines by over 5.2% on average. Project page:https://mx-liu6.github.io/FoundationGeo-web/

Vision-language models (VLMs) commonly formulate visualgrounding and detection as a coordinate-token generation problem, serial-izing each 2D box into multiple 1D tokens that are learned and decodedlargely independently. This token-by-token decoding mismatches the cou-pled structure of box geometry and creates a practical inference bottleneckdue to strictly sequential generation. We introduce LocateAnything,a unified generative grounding and detection framework based on Par-allel Box Decoding (PBD). By decoding geometric elements such asbounding boxes and points as atomic units in a single step, LocateAny-thing preserves intra-box geometric coherence and unlocks substantialparallelism. We show that PBD improves both decoding throughput andlocalization accuracy. We further develop a scalable data engine andcurate LocateAnything-Data, a large-scale dataset with more than138 million training samples, substantially increasing data diversity forhigh-precision localization. Extensive evaluations show that LocateAny-thing advances the speed–accuracy frontier, achieving significantly higherdecoding throughput while improving high-IoU localization quality acrossdiverse benchmarks. The results highlight the complementary benefits ofParallel Box Decoding and large-scale training data in enabling efficientand precise unified visual grounding and detection.

Vision–Language–Action (VLA) models have become a com-pelling route toward robotic foundation models by mapping multimodalperception and language instructions directly into executable actions. Be-yond the prevalent flow-matching VLAs, discrete diffusion VLA (dVLA)models further unify perception and control in a single discrete space,offering inherent advantages in unified multimodal alignment and under-standing, while better preserving the prior knowledge of vision-languagemodels. Yet this strength comes with a practical obstacle that inferenceis far too slow for real-time control, as the parallel decoding prevents theutilization of Key-Value (KV) cache. In this paper, we aim to tackle thischallenge by leveraging an intriguing observation that the dVLA withbidirectional attention still adheres to a block-wise left-to-right order,which motivates the application of block diffusion. However, directlyapplying block diffusion precludes inter-block parallelism, a key accel-eration factor. To address the limitation, we introduce Fast-dVLA, anblock-wise diffusion acceleration strategy. Fast-dVLA takes the full actiontoken sequence at each timestep as an action block and denoises themtogether. It then decodes different blocks in an autoregressive mannerto allow KV cache reusing, while allowing inter-block parallel decodingin a diffusion-forcing manner. For efficient training, we directly conductan asymmetric distillation on a finetuned dVLA. Extensive evaluationson Discrete Diffusion VLA, Dream-VLA, and UD-VLA across diversesimulated benchmarks validate that our Fast-dVLA achieves 2.8×-4.1×speedup and maintains state-of-the-art performance. Moreover, the resultson real-world high-dynamics tasks demonstrate the potential of real-timedeployment and application of our Fast-dVLA.

Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its fundamental challenge lies in the conflict between the unbounded nature of streaming media input and the limited context window of Multimodal Large Language Models (MLLMs). Current methods primarily rely on passive processing, which often face a trade-off between maintaining long-range context and capturing the fine-grained details necessary for complex tasks. To address this, we introduce EventMemAgent, an active online video agent framework based on a hierarchical memory module. Our framework employs a dual-layer strategy for online videos: short-term memory detects event boundaries and utilizes event-granular reservoir sampling to process streaming video frames within a fixed-length buffer dynamically; long-term memory structuredly archives past observations on an event-by-event basis. Furthermore, we integrate a multi-granular perception toolkit for active, iterative evidence capture and employ Agentic Reinforcement Learning (Agentic RL) to end-to-end internalize reasoning and tool-use strategies into the agent’s intrinsic capabilities. Experiments show that EventMemAgent achieves competitive results on online video benchmarks. The code will be released here: https://github.com/lingcco/EventMemAgent.

We introduce EgoPolice, a carefully curated dataset of real,egocentric police–civilian interactions, sourced from publicly availablebody-worn camera videos. We select police-civilian action labels that arecritical for police behavioral research and annotate them at a second-by-second granularity. The videos feature rapid and irregular cameramotion, dense human interactions, and rare high-stakes events, mak-ing the dataset a challenging benchmark for motion-robust and context-aware egocentric perception. We provide two different tasks, classifica-tion and multiple-choice question-answering, and benchmark both open-source and closed-source models. We find that even the best video modelslike Gemini 2.5 Pro still struggle to accurately predict high-risk actionssuch as “Weapon Out”. Beyond serving as a benchmark, EgoPolice pro-vides a foundation for developing models capable of identifying eventsof interest in large-scale body-worn camera video repositories, enablingmore efficient downstream human review.Content Warning: This paper includes real police body-worn camerafootage, including potentially distressing scenes.