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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

ReflectCAP: Detailed Image Captioning with Reflective Memory

Kyungmin Min, Minbeom Kim, Kang Il Lee, Seunghyun Yoon, Kyomin Jung

Detailed image captioning demands both factual ground-ing and fine-grained coverage, yet existing methods have struggled toachieve them simultaneously. We address this tension with ReflectiveNote-Guided Captioning (ReflectCAP), where a multi-agent pipeline an-alyzes what the target large vision-language model (LVLM) consistentlyhallucinates and what it systematically overlooks, distilling these pat-terns into reusable guidelines called Structured Reflection Notes. At in-ference time, these notes steer the captioning model along both axes—whatto avoid and what to attend to—yielding detailed captions that jointlyimprove factuality and coverage. Applying this method to 8 LVLMs span-ning the GPT-4.1 family, Qwen series, and InternVL variants, Reflect-CAP reaches the Pareto frontier of the trade-off between factuality andcoverage, and delivers substantial gains on CapArena-Auto, where gen-erated captions are judged head-to-head against strong reference models.Moreover, ReflectCAP offers a more favorable trade-off between captionquality and compute cost than model scaling or existing multi-agentpipelines, which incur 21–36% greater overhead. This makes high-qualitydetailed captioning viable under real-world cost and latency constraints.

ECCV 2026

Recurrent Autoregressive Diffusion: Global Memory Meets Local Attention

Taiye Chen, Zihan Ding, Anjian Li, Christina Zhang, Zeqi Xiao, Yisen Wang, Chi Jin

Recent advancements in video generation has shifted from bidi-rectional models for short videos to autoregressive ones for ultra long videogeneration. Previous models, which usually use sliding window attention torestrict inference cost, lack effective memory compression and retrieval forlong-term generation beyond the window size, leading to issues of forgettingand spatiotemporal inconsistencies. To enhance the retention of historicalinformation with a fixed memory budget, we additionally incorporate tem-poral recurrent neural network (RNN) layers into the diffusion transformer(DiT) model. Specifically, we found that a LSTM layer after attention at eachDiT layer achieves comparable performance to other state-of-the-art RNNblocks, such as Test-Time Training (TTT) and Mamba2. Moreover, existingdiffusion-RNN approaches often suffer from performance degradation dueto training-inference gap or the lack of overlap across windows. To addressthese limitations, we propose a novel Recurrent Autoregressive Diffusion(RAD) framework, which leverages recurrent blocks for memory update andretrieval and preserves local details by full attention on overlapping slidingwindows, with no training and inference gap. Experiments on Memory Mazeand Minecraft datasets demonstrate the superiority for long video generationby our framework with global memory and local attention.Project page: https://yeyutaihan.github.io/recurrent-autoregressive-diffusion/

ECCV 2026

NeuIDO: Neural Intrinsic Dynamics Operator for Physics-Informed 4D World Models

Jiajing Lin, Xin Zhang, Jianhua Sun

World models aim to capture environmental dynamics andpredict future trajectories, showing growing potential for embodied in-telligence. Physics-informed 4D generation integrates physical simulationto predict 3D object interactions, offering a promising pathway towardworld models. However, this paradigm relies on manually imposed dy-namical assumptions rather than internalizing world dynamics, and thusstill leaves a gap toward a true world model. To bridge this gap, we pro-pose NeuIDO, a novel world dynamics modeling framework that learnsa unified intrinsic dynamics representation from visual observations, ad-vancing physics-informed 4D generation toward a world model. Specifi-cally, we formulate world modeling as a neural operator learning problemand introduce a two-stage training strategy to learn a generalizable map-ping from the visual observation distribution to the intrinsic dynamicsdistribution. Building on this observation-dynamics mapping, NeuIDOenables zero-shot dynamics inference directly from videos and can be fur-ther aligned with complex real-world dynamics via few-shot adaptation.Extensive experiments demonstrate that NeuIDO effectively unifies theintrinsic dynamics underlying diverse visual observations into a sharedrepresentation and rapidly infers dynamics in novel scenes.

ECCV 2026

Quantile‑Adaptive Temperature Scaling for Confidence Calibration

Omprakash Chakraborty, Leo Fillioux, Ismail Ayed, Jose Dolz

Deep neural networks often produce poorly calibrated confi-dence estimates, overstating their certainty even when predictions are in-correct. Temperature Scaling (TS) remains the most widely used post-hoccalibration method due to its simplicity and e!ectiveness, yet its global,uniform rescaling of logits fails to correct the highly heterogeneous struc-ture of miscalibration observed across the confidence spectrum. In par-ticular, the largest correctness–confidence discrepancies arise in di!er-ent quantile regions depending on the setting, which standard TS leaveslargely unaddressed. We introduce Quantile-Adaptive Temperature Scal-ing (QaTS), a simple and e"cient post-hoc calibration method thatadapts the temperature as a function of a prediction’s empirical con-fidence quantile. By mapping confidences into the quantile space, QaTSnormalizes the calibration problem, makes the structure of miscalibrationexplicit, and enables a monotone temperature function that adapts acrossquantiles while leaving well-calibrated high-confidence predictions largelyunchanged. This quantile-aware formulation aligns naturally with a repa-rameterized Expected Calibration Error (ECE) objective and yields asample-wise temperature that is robust across a variety of challengingscenarios, such as class imbalance and distributional shifts. Across abroad range of datasets, architectures, evaluation scenarios and diversetasks, QaTS consistently, and substantially, outperforms state-of-the-artpost-hoc calibration methods, delivering more reliable and trustworthyconfidence estimates without modifying model predictions.

ECCV 2026

QASA: Quality-Guided K-Adaptive Slot Attention for Unsupervised Object-Centric Learning

Tianran Ouyang, Xingping Dong, Jing Zhang, Mang Ye, Kaihao Zhang, Bo Du

Slot Attention, an approach that binds different objects in ascene to a set of "slots", has become a leading method in unsupervisedobject-centric learning. Most methods assume a fixed slot count K, andto better accommodate the dynamic nature of object cardinality, a fewworks have explored K-adaptive variants. However, existing K-adaptivemethods still suffer from two limitations. First, they do not explicitlyconstrain slot-binding quality, so low-quality slots lead to ambiguousfeature attribution. Second, adding a slot-count penalty to the recon-struction objective creates conflicting optimization goals between reduc-ing the number of active slots and maintaining reconstruction fidelity.As a result, they still lag significantly behind strong K-fixed baselines.To address these challenges, we propose Quality-Guided K-AdaptiveSlot Attention (QASA). First, we decouple slot selection from recon-struction, eliminating the mutual constraints between the two objectives.Then, we propose an unsupervised Slot-Quality metric to assess per-slotquality, providing a principled signal for fine-grained slot–object binding.Based on this metric, we design a Quality-Guided Slot Selection schemethat dynamically selects a subset of high-quality slots and feeds them intoour newly designed gated decoder for reconstruction during training. Atinference, token-wise competition on slot attention yields a K-adaptiveoutcome. We conduct experiments on both object discovery and objectproperty prediction. Results show that QASA substantially outperformsexisting K-adaptive methods on both real and synthetic datasets. More-over, on real-world datasets, QASA surpasses K-fixed methods. The codeis available at https://github.com/ouyangtianran/QASA-tianran.Fig. 1: mBOi vs. slot count K on COCO [25]. K-fixed methods, such as SPOT [20]and DINOSAUR [33], show large performance fluctuations as K varies. By contrast,K-adaptive methods do not rely on a carefully tuned K. Our method substantiallyoutperforms existing K-adaptive baselines, including MetaSlot [26] and AdaSlot [16],and even surpasses strong K-fixed baselines.

ECCV 2026

Q-REAL: Towards Naturalness and Distortion Evaluation for AI-Generated Content

Shushi Wang, Zicheng Zhang, Chunyi Li, Wei Wang, Liya Ma, Xiaoyu Li, FengJiao Chen, Xuezhi Cao, Guangtao Zhai, Xiaohong Liu

Quality assessment of AI-generated content is vital for modeloptimization, yet most existing evaluation datasets and models rely oncoarse-grained single scores, failing to provide targeted guidance. Tobridge this gap, we introduce Q-Real, a fine-grained quality assessmentdataset specifically designed for AI-generated images. This dataset com-prises 10K images generated by multiple models, annotated along twocritical dimensions, naturalness and distortion which are widely re-garded as the most significant aspects of AI-generated image quality.For each image, we localize major entities and provide a set of judgmentquestions and attribution descriptions along these dimensions to facili-tate comprehensive evaluation. Based on this dataset, we establish theQ-Real Bench to rigorously evaluate models on the challenging tasks ofjudgment and grounding with reasoning. And to tackle these challenges,we further propose a fine-grained training pipeline for Multimodal LargeLanguage Models (MLLMs), empowering them to judge, localize prob-lematic entities with detailed analysis, and predict quality score. Ex-perimental results demonstrate the high quality and significance of ourdataset, as well as the effectiveness of our proposed training pipeline.The dataset is available at https://huggingface.co/datasets/AGI-Eval/Q-Real.

ECCV 2026

Probe, Anchor, and Amend: Active Test-Time Adaptation of Vision-Language Models

Jiaqi Lin, Chaoqi Chen, Xiasi Wang, Mingfu Yan, Jiancheng Huang, Jianzhuang Liu, Wenming Yang, Qingmin Liao

Vision-language models (VLMs) excel at zero-shot recogni-tion, yet their performance degrades under distribution shifts during in-ference. Unsupervised test-time adaptation (TTA) often relies on noisypseudo-labels or heuristics, offering limited guidance for where decisionboundaries should move; existing active TTA (ATTA) methods typi-cally treat annotations as local fixes rather than structured updates tothe shared semantics of VLMs. We introduce PAA (Probe, Anchor, andAmend), an ATTA framework that treats visual and textual prototypesas an evolving state and updates this state through a three-step cycle. Ineach cycle, the Probe step performs prototype-guided querying under aclass-balanced budget, selecting both central samples to preserve sourceknowledge and boundary samples to track distribution shifts. Using onlybinary verifications, the Anchor step applies a prototype-regularized up-date that couples cross-modal alignment with intra-class compactnessand inter-class separation, yielding a decision space that is stable yetplastic. As a slow controller, the Amend step revisits past hard casesstored in memory and converts temporal discrepancies into memory-driven self-refinement. Across VLM test-time benchmarks, PAA deliversconsistent gains over strong TTA and ATTA baselines under comparableannotation budgets and compute, turning sparse, delayed feedback intostructured evolution of the prototype-anchored decision space.

ECCV 2026

Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis

Chamuditha Jayanga Ahangama Galappaththige, Thomas Gottwald, Peter Stehr, Edgar Heinert, Niko Suenderhauf, Dimity Miller, Matthias Rottmann

Recent advances in 3D Gaussian Splatting have enabled im-pressive photorealistic novel view synthesis. However, to transition froma pure rendering engine to a reliable spatial map for autonomous agentsand safety-critical applications, knowing where the representation is un-certain is as important as the rendering fidelity itself. We bridge thiscritical gap by introducing a lightweight, plug-and-play framework forpixel-wise, view-dependent predictive uncertainty estimation. Our post-hoc method formulates uncertainty as a Bayesian-regularized linear least-squares optimization over reconstruction residuals. This architecture-agnostic approach extracts a per-primitive uncertainty channel withoutmodifying the underlying scene representation or degrading baseline vi-sual fidelity. Crucially, we demonstrate that providing this actionablereliability signal successfully translates 3D Gaussian splatting into atrustworthy spatial map, further improving state-of-the-art performanceacross three critical downstream perception tasks: active view selection,pose-agnostic scene change detection, and pose-agnostic anomaly detec-tion. Code is available at github.io/3DGS-Uncertainty.

ECCV 2026

Practice Makes Perfect: From Explicit Decomposition to Reinforced Latent Planning in Text-to-Human Motion

Ronghao Yu, Yang Liu, Juncheng Wang, Chao Xu, Yimo Shao, Baigui Sun, Yong Liu, Shan Luo

Recent work has shown that Chain-of-Thought (CoT) rea-soning improves text-to-motion generation, yet generating explicit rea-soning tokens at inference time introduces significant latency. We presentLaCT-Motion (Latent Chain-of-Thought for Motion), the first methodto transfer latent reasoning from language to LLM based human mo-tion generation, achieving explicit-CoT-level quality without its infer-ence overhead. Our three-stage recipe mirrors human skill acquisition:practice learns explicit CoT reasoning via supervised fine-tuning; in-ternalization progressively compresses variable-length reasoning stepsinto compact continuous latent tokens through a stage-based curricu-lum with stochastic scheduling; and perfection refines the fully latentpolicy with GRPO using format, motion similarity, and semantic simi-larity rewards. At inference, the model reasons entirely through latenttokens with no explicit text generated. Experiments on HumanML3Dshow that latent internalization not only matches but surpasses explicitCoT reasoning (R-Precision Top-1: 0.577 vs. 0.515 for Motion-R1, FID:0.167), suggesting that continuous latent tokens encode spatio-temporalreasoning more effectively than discrete text. Code will be released athttps://github.com/Erwin2233/LaCT-Motion.

ECCV 2026

Parallel Vision Token Scheduling for Fast and Accurate Multimodal LMMs Inference

Wengyi Zhan, Mingbao Lin, Zhihang Lin, Rongrong Ji

Multimodal large language models (MLLMs) deliver impressive vision-language reasoning, but suffer steep inference latency because self-attention scales quadratically with sequence length and thousands of visual tokens contributed by high-resolution images. Naively pruning less-informative visual tokens reduces this burden, yet indiscriminate removal can erase contextual cues essential for background or fine-grained questions, undermining accuracy. In this paper, we present ParVTS (Parallel Vision Token Scheduling), a training-free scheduling framework that partitions visual tokens into subject and non-subject groups, processes them in parallel to transfer their semantics into question tokens, and discards the non-subject path mid-inference to reduce computation. This scheduling reduces computational complexity, requires no heuristics or additional modules, and is compatible with diverse existing MLLM architectures. Experiments across multiple MLLM backbones show that ParVTS prunes up to 88.9% of visual tokens with minimal performance drop, achieving 1.77× speedup and 70% FLOPs reduction. Our code is released at https://github.com/CrispyFeSo4/ParVTS.

ECCV 2026

One Slide, Many Views: Unifying Complementary Foundation Model Perspectives for WSI Analysis

Yihui Wang, Yingxue Xu, Shu Yang, Yequan Bie, Jiabo Ma, Fengtao Zhou, Hao Chen

Recent advances in foundation models (FMs) have led to aproliferation of Whole Slide Image (WSI) feature extractors, yet WSIanalysis still faces a practical challenge: no single FM consistently per-forms best across all tasks, and the performance gap among differentMultiple Instance Learning (MIL) aggregators built on a single strongFM can become limited in some settings. Recent research further showsthat simple ensembles of FMs can outperform the best individual FM,indicating that different FMs capture partially overlapping yet comple-mentary information. This motivates a shift from selecting a single FMtoward coordinating and exploiting multiple FMs jointly. However, exist-ing multi-FM approaches mostly rely on naive feature concatenation orpatch-level selection, operating on raw correlated FM features withoutexplicitly disentangling shared and view-specific information. To addressthis challenge, we reformulate FM ensembling as a Multi-View Learn-ing (MVL) problem. We propose Multi-View Multiple Instance Learning(MV-MIL), a plug-and-play framework that treats features from eachFM as a distinct view of a WSI. MV-MIL introduces an information-theoretic disentanglement module to exploit cross-view complementar-ity while suppressing redundancy, together with a Multi-Branch Predic-tion Head that integrates shared and view-specific representations withinMIL pipelines. Extensive experiments on 14 public benchmarks showthat MV-MIL consistently achieves state-of-the-art performance, withits plug-and-play design yielding robust gains across diverse MIL meth-ods and FM combinations.

ECCV 2026

Occlusion-Resilient Category-Agnostic Pose Estimation with Conditional Flow Matching

Jiyong Rao, Shengjie Zhao, Yu Wang, Hao Deng

Category-Agnostic Pose Estimation (CAPE) aims to local-ize category-specific keypoints on a query image from only a few anno-tated support examples. Existing CAPE methods are largely based ondirect coordinate regression or local heatmap matching, which becomeunreliable under severe occlusion because missing keypoints cannot berecovered from local evidence alone. We present FlowCape, a condi-tional flow-matching framework for occlusion-resilient CAPE. FlowCapefirst extracts query-image features and combines them with keypoint-description embeddings in a Heatmap-Guided Initialization module, pro-ducing a stable initial pose for transport. A shared Riemannian PoseHead then predicts the conditional velocity field, while its internal GraphFlow Encoder fuses query image, textual keypoint semantics, and skele-ton topology to enforce structured pose evolution. The final predictionis obtained by probability-flow ODE rollout, and training is driven byhybrid flow-matching supervision together with initialization and roll-out consistency constraints. To better evaluate robustness under severeocclusion, we further introduce Occ80, an occlusion-focused benchmarkspanning 80 categories. Experiments on MP-100 and Occ80 show thatFlowCape consistently outperforms strong CAPE baselines, reach thestate-of-the-art under occluded scenario.

ECCV 2026

Modality-Aware Out-of-Distribution Detection for Multi-Modal Action Recognition

Lars Doorenbos, Duc Vu, Serdar Ozsoy, Juergen Gall

The incorporation of additional modalities into action recog-nition models increases their performance across a wide range of settings.However, how this additional information can contribute to making themodels more robust remains underexplored, particularly for the case ofmulti-modal out-of-distribution (OOD) detection. While methods existthat regularize the multi-modal training process with OOD detection inmind, they still apply off-the-shelf OOD detectors designed for the uni-modal case during inference, discarding important information. Basedon an interesting relationship we find between the multi-modal and uni-modal predictions, we propose to use this signal to build a post-hocdetector explicitly designed for the multi-modal scenario. We combinethis new source of information with a feature-space score, which detectsoff-manifold samples in the multi-modal space, and normalize them bythe multi-modal logits. In doing so, the proposed hybrid detector is com-patible with existing training-time approaches and consistently improvesperformance. Experiments on a wide range of established datasets fromthe MultiOOD benchmark show that, on average, our approach outper-forms the state of the art. Our results show the importance of explicitlyconsidering the different modalities at inference time for multi-modalOOD detection.

ECCV 2026

MobileSAM2: Lightweight Segment Anything in Images and Videos via Hypergraphical Knowledge Distillation

Kai Jiang, Jiaxing Huang, Jingyi Zhang, Weiying Xie, Yunsong Li, Yufei Wang, Aoran Xiao, Dacheng Tao

The recent large video foundation model, SAM2, enablessegment anything in both images and videos, serving as a powerful basemodel for various applications. However, many of such use cases requireto operate on resource-constrained devices like mobile phones and laptops.In this work, we aim to make SAM2 more mobile-friendly by distillingthe heavyweight SAM2 into a lightweight model, facilitating segmentanything in both images and videos on mobile devices. To this end, wepropose Hypergraphical Knowledge Distill (HyperKD), which introducesthe idea of hypergraph into knowledge distillation, aiming to effectivelymodel and transfer SAM2’s generalizable and comprehensive knowledge.HyperKD consists of Temporal HyperKD and Granularity HyperKD thatconstruct hypergraphs to explicitly model and extract the generalizabletemporal knowledge and the comprehensive multi-granularity knowledgefrom SAM2 respectively, which are then distilled into the lightweightstudent model by aligning it with the constructed hypergraphs. Besides,we present MobileSAM2, a new family of lightweight SAM2 that balancesefficiency and effectiveness via searching the best model architectures withHyperKD during model size reduction. Extensive experiments validateMobileSAM2 across multiple benchmarks and show promising generaliza-tion performance on embodied AI tasks.

ECCV 2026

LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models

Sojung An, Junha Lee, Sujeong You, Nam Ik Cho, Donghyun Kim

Pre-trained Vision Foundation Models (VFMs) provide strongvisual representations for diverse downstream tasks. The key challenge ofVFM adaptation stems from the prohibitive costs of full fine-tuning andcatastrophic forgetting. To address this, Low-Rank Adaptation (LoRA)has emerged as the prevailing paradigm for Parameter-Efficient Fine-Tuning (PEFT). However, LoRA is typically designed for transformerself-attention layers parameterized by 2D matrices. Since convolutionalkernels inherently couple spatial and channel information within a 4Dtensor, forcing them into a monolithic 2D matrix disrupts the inher-ent spatial topology. In this paper, we propose Low-Rank ConvolutionalAdaptation (LoCA), a convolution-aware PEFT framework that addressesspatial-channel entanglement by decoupling channel and spatial adapta-tion. LoCA introduces a low-rank channel adaptation for dense cross-channel mixing and refines spatial bases extracted from pre-trained ker-nels via Singular Value Decomposition (SVD). Experimental results showthat LoCA preserves pre-trained spatial priors and achieves competitiveor state-of-the-art performance across fine-grained classification, domain-generalized semantic segmentation, and generative benchmarks.

ECCV 2026

CARA: Collision-Aware Resolution Adaptation for Multiresolution Hash Encoding Based Image Fitting

Linfeng Ye, Zhixiang Chi, Shayan Mohajer Hamidi, En-Hui Yang, Konstantinos Plataniotis

Multiresolution hash encodings have recently enabled fast and high-fidelity implicit neural representations by storing multi-scale features in fixed-size hash tables along a geometric resolution schedule. However, the standard design is data-agnostic: different resolution levels receive identical hash-table capacity despite large differences in image frequency content. As a result, some levels experience severe hash collisions while others underutilize parameters, leading to inefficient capacity allocation. To address this issue, we propose Collision-Aware Resolution Adaptation (CARA), a method that assigns per-level resolutions by balancing the effective information load across hash levels. This adaptive allocation reduces capacity bottlenecks and improves parameter efficiency. In addition, we introduce an invertible pixel-shuffle transform that reduces hash load factors by redistributing spatial information, thereby mitigating collision-induced information loss without enlarging the hash tables. To support evaluation on extremely high-resolution data, we also curate, to the best of our knowledge, the first uncompressed wholeslide image dataset for academic research. Experiments on Kodak images, gigapixel natural images, and raw whole-slide images demonstrate that CARA consistently improves the fidelity–parameter trade-off. Our method matches state-of-the-art performance while using only 27.76% of the parameters, and achieves up to 6.11dB PSNR improvement at comparable parameter counts. Code is provided in the supplementary.

ECCV 2026

Let's Reward Step-by-Step: Step-Aware Contrastive Alignment for Vision-Language Navigation in Continuous Environments

Haoyuan Li, Rui Liu, Hehe Fan, Yi Yang

Vision-Language Navigation in Continuous Environments(VLN-CE) requires agents to learn complex reasoning from long-horizonhuman interactions. While multi-modal large language models (MLLMs)have driven recent progress, current training paradigms struggle to bal-ance generalization capability, error recovery, and training stability. Specif-ically, (i) policies derived from supervised fine-tuning (SFT) suffer fromcompounding errors, struggling to recover from out-of-distribution states,and (ii) Reinforcement fine-tuning (RFT) methods such as GRPO arebottlenecked by sparse outcome rewards. Their binary feedback fails toassign credit to individual steps, leading to gradient signal collapse infailure-dominant batches. To address these challenges, we introduce Step-Aware Contrastive Alignment (SACA), a framework designed to extractdense supervision from imperfect trajectories. At its core, the Perception-Grounded Step-Aware auditor evaluates progress step-by-step, disentan-gling failed trajectories into valid prefixes and exact divergence points.Leveraging these signals, the Scenario-Conditioned Group Constructionmechanism dynamically routes batches to specialized resampling andoptimization strategies. Extensive experiments on VLN-CE benchmarksdemonstrate that SACA achieves state-of-the-art performance.

ECCV 2026

KineticGS: Momentum-driven Coherent 4D Gaussian Splatting for Monocular Dynamic Scene Reconstruction

Yunqing Wang, Baoyao Yang, Si-Qi Liu, Chong Yin, Yihua Shao, Hao Tang

We present KineticGS, a novel approach for reconstructingdynamic scenes from monocular videos through momentum-driven 4DGaussian Splatting. While recent advances in motion modeling have im-proved dynamic reconstruction, they often produce temporal inconsis-tencies and non-physical deformations due to the lack of physical pri-ors constraining motion trajectories. KineticGS addresses this with aphysically grounded momentum hierarchy that models Gaussian parti-cles as momentum-carrying entities, thereby establishing a hierarchicalmotion representation from pixel observations to object-level coherence.Specifically, Gaussian particles serve as momentum carriers, with dy-namics driven by local motion energy. We perform momentum-guidedparticle sampling, concentrating representation in high-energy regionsto capture fine-grained non-rigid motions. A learnable energy-flow mod-ule then predicts per-particle temporal activation, ensuring continuousand physically plausible motion propagation. Further, a holistic kineticsynchronization mechanism enforces consistent motion evolution amongcorrelated particles, preserving structural integrity without explicit disin-tegration. Experiments on dynamic scene datasets show that KineticGSimproves reconstruction fidelity, temporal coherence, and physical real-ism, achieving +0.91 dB average PSNR gain in dynamic-object regionsover the second-best.

ECCV 2026

IConE: Batch Independent Collapse Prevention for Self-Supervised Representation Learning

Konstantinos Almpanakis, Anna Kreshuk

Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Architectures (JEAs) emerging as an effective approach for capturing semantic features. Existing JEAs rely on implicit or explicit batch interaction – via negative sampling or statistical regularization – to prevent representation collapse. This reliance becomes problematic in regimes where batch sizes must be small, such as high-dimensional scientific data, where memory constraints and class imbalance make large, well-balanced batches infeasible. We introduce IConE (Instance-Contrasted Embeddings), a framework that decouples collapse prevention from the training batch size. Rather than enforcing diversity through batch statistics, IConE maintains a global set of learnable auxiliary instance embeddings regularized by an explicit diversity objective. This transfers the anti-collapse mechanism from the transient batch to a dataset-level embedding space, allowing stable training even when batch statistics are unreliable, down to batch size 1. Across diverse 2D and 3D biomedical modalities, IConE outperforms strong contrastive and non-contrastive baselines throughout the small-batch regime (from B = 1 to B = 64) and demonstrates marked robustness to severe class imbalance. Geometric analysis shows that IConE preserves high intrinsic dimensionality in the learned representations, preventing the collapse observed in existing JEAs as batch sizes shrink.

ECCV 2026

Ice Cloud Geometry Retrieval with Calibrated Uncertainty from Passive Satellite Imagery

Ayush Prasad

We present a dense prediction system for ice cloud geom-etry retrieval from extremely sparse supervision, where active satellitesensors (EarthCARE radar/lidar) provide accurate but spatially narrowtraining labels (∼1.6% of pixels) and passive imagers (VIIRS, VisibleInfrared Imaging Radiometer Suite) observe the full globe. A ConvNex-tUNet trained on co-located tracks predicts eight targets at every pixelwith calibrated 90% prediction intervals using conformalized quantileregression (CQR). The best configuration, a five-member quantile en-semble, achieves R2= 0.742 with prediction quality constant regardlessof distance to the supervision track, confirming that the model learnsa per-pixel spectral retrieval rather than interpolating from nearby la-bels. Calibration is robust across latitudes and cloud types, with deepconvective clouds as the main failure mode. Deployed globally, the sys-tem produces 609M predictions from one day of VIIRS data in 2.4 hourson a single GPU, enabling dense 3D cloud characterization at planetaryscale.1

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