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ECCV 2026

Information-Regularized Attention for Visual-Centric Reasoning

Guohao Sun, Xiaofang Wang, Yash Patel, mengchen liu, Zhiqiang Tao, Praveen Krishnan

Vision–language models (VLMs) have become a paradigmfor multimodal learning, yet remain unstable due to object hallucination,weak visual grounding, and catastrophic forgetting after full-parameterinstruction tuning. We claim these failures result from a lack of explicitcontrol over visual representation learning during the standard next-token prediction objective. As a result, visual embeddings thus becomepassively optimized and prone to injecting redundant or spurious signals.To counter this, we introduce Information-Regularized Attention (IRA),a stochastic attention mechanism that explicitly regulates the amount ofvisual information injected into the hidden states of intermediate trans-former layers. This local reparameterization translates uncertainty aboutvisual representations into local noise that is independent across datapoints. Beyond evaluating model performance, we also quantify embed-ding properties, where IRA produces smoother curvature trajectoriesand suppresses attention-sink across all layers, indicating a more stabletransformation of the visual signal. Our results suggest that stochasticattention is not merely a regularizer but a key contributor to represen-tation learning in a generative architecture, offering a new direction forbuilding more reliable VLMs.

ECCV 2026

Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation

Sujun Sun, Mingwu Ren, Haofeng Zhang

Cross-domain Few-shot Segmentation (CD-FSS) aims to learn generalizable segmentation capability from abundant annotated samples in the source domain, enabling accurate segmentation of novel classes in the target domain with only a few annotated samples. Existing CDFSS methods mainly focus on mitigating feature distribution shifts caused by style gaps while ignoring significant differences in class semantic granularity and discriminative attributes across domains, leading to two key degradations in support-query matching: semantic over-alignment and attribute over-alignment. To this end, we propose the Dual Hierarchical Aggregation Network (DHANet), which comprises three key modules. First, the Hierarchical Spatial Aggregation (HSA) module performs multi-scale region aggregation of pixel features along the spatial dimension, generating hierarchical semantic-enhanced features to alleviate semantic over-alignment. Additionally, the HCA module conducts multi-scale attribute aggregation along the channel dimension, generating hierarchical attribute-enhanced features to mitigate attribute overalignment. Finally, we propose the Online Probabilistic Semantic Bank (OPSB), which progressively constructs and updates class probability distributions from query predictions during inference, and samples multiple pseudo-prototypes as additional support information to mitigate insufficient support. Extensive experiments on four target-domain datasets demonstrate that our method achieves state-of-the-art performance.

ECCV 2026

GeoNVS: Geometry Grounded Video Diffusion for Novel View Synthesis

Minjun Kang, Inkyu Shin, Taeyeop Lee, Myungchul Kim, In Kweon, Kuk-Jin Yoon

Novel view synthesis requires strong 3D geometric consis-tency and the ability to generate visually coherent images across di-verse viewpoints. While recent camera-controlled video diffusion modelsshow promising results, they often suffer from geometric distortions andlimited camera controllability. To overcome these challenges, we intro-duce GeoNVS, a geometry-grounded novel-view synthesizer that en-hances both geometric fidelity and camera controllability through ex-plicit 3D geometric guidance. Our key innovation is the Gaussian Splat-ting Feature Adapter (GS-Adapter), which lifts input-view diffusion fea-tures into 3D Gaussian representations, renders geometry-constrainednovel-view features, and adaptively fuses them with diffusion features tocorrect geometrically inconsistent representations. Unlike prior methodsthat inject geometry at the input level, GS-Adapter operates in featurespace, avoiding view-dependent color noise that degrades structural con-sistency. Its plug-and-play design enables zero-shot compatibility withdiverse feed-forward geometry models without additional training, andcan be adapted to other video diffusion backbones. Experiments across 9scenes and 18 settings demonstrate state-of-the-art performance, achiev-ing 11.3% and 14.9% improvements over SEVA and CameraCtrl, withup to 2× reduction in translation error and 7× in Chamfer Distance.

ECCV 2026

Geometric Foundation Model Distillation for Efficient Lunar 3D Reconstruction

Clémentine Grethen

Large 3D foundation models such as MASt3R achieve state-of-the-art stereo reconstruction but are computationally demanding fordeployment under strict hardware constraints—a critical limitation indomains such as planetary exploration, where onboard computing isseverely restricted. We study how far such models can be compressedthrough knowledge distillation, using lunar stereo reconstruction as achallenging and practically relevant case study. Starting from a 688M-parameter MASt3R teacher fine-tuned on lunar imagery, we distill itsdense geometric predictions into a family of lightweight students span-ning different encoder types (CNN vs. ViT), decoder widths and depths,and training strategies. To bridge the dimensional mismatch betweenteacher and student, we propose a structured SVD-based initializationthat projects the teacher’s decoder weights into the student’s smallerlatent space, yielding a warm start that significantly improves conver-gence and final performance. Based on our results on lunar data, wecan obtain a distilled student that retains most of teacher’s reconstruc-tion accuracy while reducing the model size up to 7×, and even out-performs a baseline trained directly with sparse ground-truth annota-tions. Beyond compression, our study highlights both principles andpractical insights for distilling geometric foundation models: a convo-lutional encoder underperforms transformer-based alternatives (thoughpretraining availability remains a confounding factor), preserving en-coder capacity is more critical than maintaining a large decoder, feature-level distillation consistently outperforms output-only supervision, andSVD-based initialization improves optimisation stability. These findingsprovide practical guidelines for deploying 3D reconstruction models inresource-constrained environments. Code is publicly available at https://clementinegrethen.github.io/publications/ECCV.html.

ECCV 2026

GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents

Xinyu Zhang, Yixin Wu, Boyang Zhang, Chenhao Lin, Chao Shen, Michael Backes, Yang Zhang

Images shared on social media often expose geographic cues.While early geolocation methods required expert effort and lacked gen-eralization, the rise of Large Vision Language Models (LVLMs) now en-ables accurate geolocation even for ordinary users. However, existingapproaches are not optimized for this task. To explore the full potentialand associated privacy risks, we present GEO-Detective, an agent thatmimics human reasoning and tool use for image geolocation inference.It follows a procedure with four steps that adaptively selects strategiesbased on image difficulty and is equipped with specialized tools suchas visual reverse search, which emulates how humans gather externalgeographic clues. Experimental results show that GEO-Detective out-performs baseline large vision language models (LVLMs) overall, par-ticularly on images lacking visible geographic features. In country levelgeolocation tasks, it achieves an improvement of over 11.1% compared tobaseline LLMs, and even at finer grained levels, it still provides around a5.2% performance gain. Meanwhile, when equipped with external clues,GEO-Detective becomes more likely to produce accurate predictions, re-ducing the “unknown” prediction rate by more than 50.6%. We furtherexplore multiple defense strategies and find that GEO-Detective exhibitsstronger robustness, highlighting the need for more effective privacy safe-guards.

ECCV 2026

DART: Difficulty-Adaptive Routing for Zero-Shot Video Temporal Grounding

Zhengbo Zhang, Mark H. Huang, Zhigang Tu, Ming-Hsuan Yang

Zero-shot video temporal grounding (VTG) localizes eventsin untrimmed videos from natural language queries without task-specifictraining. Existing methods rely on frame-query feature matching, whichsuffices for simple events but struggles with complex multi-stage queriesthat require understanding temporal ordering and causal structure—a disparity we call the reasoning gap. We propose DART (Difficulty-Adaptive Routing for Temporal Grounding), which bridges this gapby coupling difficulty-aware routing with structured reasoning in largevision-language models. A query-conditioned Determinantal Point Pro-cess (DPP) serves a dual role: selecting diverse, query-relevant keyframesas temporal evidence, and providing spectral entropy as a difficulty indi-cator. Simple queries are routed to a Fast path for direct prediction, whilecomplex queries follow a Slow path with Temporal Markup Prompting,which decomposes localization into global event analysis, per-frame tem-poral role annotation, and boundary extraction. On Charades-STA andActivityNet Captions, DART achieves state-of-the-art zero-shot perfor-mance across both identically distributed and multiple out-of-distributionsettings, improving mIoU by up to 3.5 points over the strongest baselinewhile using over 7× fewer frames. The project homepage is available athttps://dart-vtg.github.io/.

ECCV 2026

FUMO: Prior-Modulated Diffusion for Single Image Reflection Removal

Telang Xu, Chaoyang Zhang, Guangtao Zhai, Xiaohong Liu

Single image reflection removal (SIRR) is challenging in realscenes, where reflection strength varies spatially and reflection patternsare tightly entangled with transmission structures. This paper presentsa diffusion model with prior modulation framework (FUMO) that in-troduces explicit priors for spatially adaptive conditioning and struc-turally faithful restoration. Two priors are extracted directly from themixed image, an intensity prior that estimates spatial reflection sever-ity and a high-frequency prior that captures detail-sensitive responsesvia multi-scale residual aggregation. We propose a coarse-to-fine train-ing paradigm. In the first stage, these cues are combined to gate theconditional residual injections, focusing the conditioning on regions thatare both reflection-dominant and structure-sensitive. In the second stage,a fine-grained refinement network corrects local misalignment and sharp-ens fine details in the image space. Experiments conducted on both stan-dard benchmarks and challenging images in the wild demonstrate com-petitive quantitative results and consistently improved perceptual qual-ity. The code is released at https://github.com/Lucious-Desmon/FUMO.

ECCV 2026

From Illusion to Intention: Visual Rationale Learning for Reliable Evidence Acquisition

Changpeng Wang, Liu Junhan, Xi Chen, Haozhe Wang, Donglian Qi, Yunfeng Yan

“Thinking with images” has recently emerged as a promisingdirection for vision-language models, where models actively interact withimages via iterative zoom-ins to acquire visual evidence for answering.Yet, prevailing frameworks ignore the quality of intermediate zoom-ins,boosting metrics but leading to an action–outcome disconnect. This fail-ure mode creates the illusion of thinking with images, where modelsappear visually grounded while relying on context-agnostic or redun-dant crops. We address this problem by introducing Visual RationaleLearning (ViRL), an end-to-end reinforcement learning paradigm thatgrounds the evidence acquisition process. ViRL ensures action faithful-ness and efficiency through: (1) process supervision with ground-truthvisual rationales, (2) rationale fidelity rewards that incentivize evidence-grounded zoom-ins, and (3) fine-grained credit assignment to penalizeredundant or erroneous steps. Experimental results show that ViRLachieves state-of-the-art performance across benchmarks spanning per-ception, hallucination, and perception-grounded reasoning. We also dis-sect the illusion of thinking with images and position visual rationales asa verifiable foundation for building trustworthy vision-language models.

ECCV 2026

Thinking in Streaming Video

Zikang Liu, Longteng Guo, Handong Li, Ru Zhen, Xingjian He, Ruyi Ji, Xiaoming Ren, Yanhao Zhang, Haonan Lu, Jing Liu

Real-time understanding of continuous video streams is es-sential for interactive assistants and multimodal agents operating in dy-namic environments. However, most existing video reasoning approachesfollow a batch paradigm that defers reasoning until the full video con-text is observed, resulting in high latency and growing computationalcost that are incompatible with streaming scenarios. In this paper, weintroduce ThinkStream, a framework for streaming video reasoningbased on a Watch–Think–Speak paradigm that enables models toincrementally update their understanding as new video observations ar-rive. At each step, the model performs a short reasoning update and de-cides whether sufficient evidence has accumulated to produce a response.To support long-horizon streaming, we propose Reasoning-CompressedStreaming Memory (RCSM), which treats intermediate reasoning tracesas compact semantic memory that replaces outdated visual tokens whilepreserving essential context. We further train the model using a Stream-ing Reinforcement Learning with Verifiable Rewards scheme that alignsincremental reasoning and response timing with the requirements ofstreaming interaction. Experiments on multiple streaming video bench-marks show that ThinkStream significantly outperforms existing onlinevideo models while maintaining low latency and memory usage. Code,models and data are available at https://github.com/CASIA- IVA-Lab/ThinkStream.

ECCV 2026

From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models

Meng Luo, Yicheng Liu, Jiahao Wang, Yuanxing Zhang, Xin Tao, Pengfei Wan, Kun Gai, Hao Fei

Video generation has advanced to produce visually compelling and temporally coherent results. Yet, whether these models can genuinely think with video—executing symbolic rules, respecting physical laws, and pursuing intentional goals—remains an open question. Existing benchmarks only partially address this, often conflating visual quality with cognitive correctness. We introduce VWG-Bench (Video World Generalist Benchmark), a comprehensive benchmark spanning 9 reasoning dimensions and 38 fine-grained tasks. To enable precise diagnosis, we design a three-level VLM-as-Judge protocol that independently assesses video-level fluency, task-level rule adherence, and samplelevel goal realization. Evaluations of leading models reveal a striking gap: while models achieve strong rendering scores, they consistently fail on logic-heavy and rule-constrained tasks. To address this, we propose Vid-PRE (Video Prompt Reasoner and Enhancer), a model-agnostic prompt rewriter that offloads the cognitive burden of reasoning to a dedicated VLM. Trained via reinforcement learning with purely textbased rewards, Vid-PRE produces concise, constraint-aware prompts without the instability of video-level reward signals. Experiments show that Vid-PRE yields substantial reasoning improvements across multiple generators without architectural modifications. Together, VWG-Bench and Vid-PRE offer a rigorous diagnostic lens and a scalable path toward true think-with-video capabilities. All data and code are publicly available at https://huggingface.co/datasets/KlingTeam/VWG-Bench.

ECCV 2026

Fourier Self-Supervision for Fine-Grained Generalized Category Discovery

Sarah Rastegar, Mina Ghadimi Atigh, Pascal Mettes, Yuki Asano, Cees Snoek

Generalized Category Discovery aims to recognize knowncategories while identifying novel ones within unlabeled data. Existingmethods, typically based on self-supervision and contrastive learning,often struggle to capture fine-grained distinctions, relying on superficialvisual cues rather than the intrinsic attributes humans use for categoriza-tion. We introduce Fourier Self-Supervision, that leverages the Fouriertransform of images to enhance the discrimination of subtle differencesand support the discovery of new categories. Our method employs adual frequency filtering strategy: a low-pass filter first extracts broad,abstract attributes that capture high-level category information, whilea high-pass filter emphasizes fine details such as edges and texturesthat are essential for fine-grained recognition. Each operates on a dedi-cated latent space, and their overlapping representations together yielda richer, more complete feature space. This dual-frequency approachnot only refines feature extraction to identify novel categories, but alsostrengthens the model’s discriminative power in fine-grained categorydiscovery. Experiments on multiple fine-grained datasets show that incor-porating Fourier Self-Supervision outperforms state-of-the-art methods,even when the number of classes is unknown, demonstrating its effec-tiveness for Generalized Category Discovery. Our code is available at:https://github.com/SarahRastegar/FourEx.

ECCV 2026

FlowDec: Temporal Conditional Flow Decorruptor for Robust Continuous Vision-Language Navigation

Yufei Zhang, Changhao Chen

Vision-and-Language Navigation in Continuous Environments(VLN-CE) requires agents to follow natural-language instructions in un-seen scenes. While Large Models (LMs) have advanced VLN-CE, theirperformance remains severely degraded by real-world visual corruptions,a critical yet underexplored domain constraint. We introduce Tempo-ral Conditional Flow Decorruptor (FlowDec), a novel image restorationframework tailored for LM-based VLN-CE. FlowDec integrates a hybridtemporal conditioning strategy to align the generative flow path withhistorical context and employs action-centroid guided filtering to dy-namically assess and integrate outputs. Extensive experiments demon-strate that FlowDec outperforms state-of-the-art decorruption methodsin both navigation accuracy and generation latency. Our approach es-tablishes a robust, efficient paradigm for resilient embodied navigationin unpredictable real-world conditions.

ECCV 2026

Flash-DD: An Ultra Parameter-Efficient Approach to Dataset Distillation

Ruonan Yu, Songhua Liu, Xinchao Wang

Dataset distillation (DD) aims to create a smaller datasetthat encapsulates the essential knowledge of a larger dataset, therebyreducing storage demands and accelerating downstream training. Forlarge-scale dataset distillation, state-of-the-art methods achieve satisfac-tory performance by using soft labels generated by well-trained teachermodels during downstream training. However, it will cause some issues:(1) a substantial amount of additional storage is required to retain theteacher models, often significantly exceeding the storage needed for thesynthetic images; (2) generating labels through these teacher modelsslows down the downstream training process, counteracting the efficiencygoals of dataset distillation; and (3) downstream training guided by theseteacher models, according to our studies, yields suboptimal performance.Focusing on these drawbacks, in this paper, we propose plug-and-playparameter-efficient label generation techniques for dataset distillation,which maximizes the benefits of limited model parameters and can begeneralized to different DD methods, datasets, and settings. Specifically,we propose a DD-oriented model parameter reduction method that au-tomatically determines the optimal capacity of teacher models and elim-inates redundant parameters for dataset distillation tasks. Furthermore,for additional parameter space, we turn to model ensemble strategiesand propose guidelines to optimize the utilization efficiency of the ad-ditional space. Compared to the state-of-the-art methods, Flash-DD re-quires only 0.03% of the additional storage and significantly acceleratesdownstream label generation by 843.81× while maintaining compara-ble performance. Alternatively, with a mere 1.8% storage budget, it canboost accuracy by up to 13.4% over previous leading methods.

ECCV 2026

EMAG: Self-Rectifying Diffusion Sampling with Exponential Moving Average Guidance

Ankit Yadav, Huy Ta, Lingqiao Liu

In diffusion and flow-matching generative models, guidance techniques are widely used to improve sample quality and consistency. Classifier-free guidance (CFG) is the de facto choice in modern systems and achieves this by contrasting conditional and unconditional samples. Recent work explores contrasting negative samples at inference using a weaker model, via strong/weak model pairs, attention-based masking, stochastic block dropping, or perturbations to the self-attention energy landscape. While these strategies refine the generation quality, they still lack a reliable control over the granularity or difficulty of the negative samples, and target-layer selection is often fixed. We propose Exponential Moving Average Guidance (EMAG), a training-free mechanism that modifies attention at inference time in diffusion transformers, with a statistics-based, adaptive layer-selection rule. Unlike prior methods, EMAG produces harder, semantically faithful negatives (finegrained degradations), surfacing difficult failure modes, enabling the denoiser to refine subtle artifacts, boosting the quality and human preference score (HPS) by +0.54 over CFG. We further demonstrate that EMAG naturally composes with advanced orthogonal guidance techniques, such as APG and CADS, further improving HPS. Code is available at https://github.com/drkkgy/EMAG.

ECCV 2026

Diversity-Aware View Partitioning for Scalable VGGT

Jinsoo Park, Donggyu Choi, Ahyun Seo, Minsu Cho, Jeany Son

Geometry transformers such as VGGT achieve strong per-formance by jointly reasoning over multiple views with global attention.However, scaling them to large view collections remains challenging dueto the quadratic cost of attention. Moreover, our empirical analysis re-veals that the reconstruction quality in VGGT is sensitive to the distri-bution of viewpoints. Simply increasing the number of views without suf-ficient viewpoint diversity can even degrade performance, as redundantviews introduce highly similar tokens that dilute informative geometricsignals in the attention mechanism. Motivated by this observation, wepropose a training-free and plug-and-play VGGT inference frameworkthat organizes views into diversity-aware balanced chunks. The chunksare constructed through combinatorial graph partitioning over visualdissimilarity and spatial dispersion. This view organization allows thetransformer to focus attention on geometrically informative views whilereducing redundant attention interactions. To estimate spatial dispersionwithout full pose estimation, we approximate spatial relationships via asoft pose propagation strategy based on visual similarity from a smallset of seed frames. Extensive experiments demonstrate improved perfor-mance in camera pose estimation, multi-view depth prediction, and 3Dreconstruction while reducing memory usage and inference latency. Ourframework also complements existing VGGT variants, enabling scalablemulti-view reconstruction without sacrificing geometric fidelity.

ECCV 2026

Discrete Diffusion Models with MLLMs for Unified Medical Multimodal Generation

Jiawei Mao, Yuhan Wang, lifeng chen, Can Zhao, Yucheng Tang, Dong Yang, Liangqiong Qu, Daguang Xu, Yuyin Zhou

Recent advances in generative medical models are often con-strained by modality-specific scenarios that hinder the integration ofcomplementary evidence, such as imaging, pathology, and clinical notes.This fragmentation limits their development to true foundation modelsthat empower medical AI agents to learn from and predict across thefull spectrum of biomedical knowledge. To address these challenges, wepropose MeDiM, the first medical discrete diffusion model that learnsshared distributions across different medical modalities without requiringmodality-specific components. MeDiM unifies multiple generative tasks:it flexibly translates between images and text or jointly produces im-age–report pairs across domains in response to user prompts. It builds ona discrete diffusion framework that unifies vision and language represen-tations by modeling their shared probabilistic distribution. To empowerthe diffusion process to support unified and versatile medical generation,we employ a multimodal large language model (MLLM) as the diffusionbackbone, leveraging its rich prior knowledge and cross-modal reason-ing abilities. Because MLLMs are trained with causal (autoregressive)masking while diffusion denoising benefits from bidirectional context,MeDiM introduces two adaptation designs: 1) removing the causal at-tention mask to enable a fully bidirectional information flow essentialfor mutual alignment, and 2) injecting continuous timestep embeddingsto make the MLLM aware of the diffusion steps. Extensive experimentsvalidate MeDiM as a unified foundation model capable of high-fidelitymedical generation across various modalities, including medical imagegeneration (16.60 FID on MIMIC-CXR; 24.19 FID on PathGen) and re-port generation (0.2650 METEOR on MIMIC-CXR; 0.2580 METEOR onPathGen). In addition, the jointly generated medical image-report pairsimprove the downstream task performance (+6.43% BLEU-1, +18.57%BLEU-2, +31.58% BLEU-3, and +4.80% METEOR in PathGen), en-abling the use of multimodal inputs and the production of coherent,clinically grounded outputs.

ECCV 2026

Revisiting Autoregressive Models for Generative Image Classification

Ilia Sudakov, Artem Babenko, Dmitry Baranchuk

Class-conditional generative models have emerged as accu-rate and robust classifiers, with diffusion models demonstrating clear ad-vantages over other visual generative paradigms, including autoregressive(AR) models. In this work, we revisit visual AR-based generative clas-sifiers and identify an important limitation of prior approaches: theirreliance on a fixed token order, which imposes a restrictive inductivebias for image understanding. We observe that single-order predictionsrely more on partial discriminative cues, while averaging over multipletoken orders provides a more comprehensive signal. Based on this insight,we leverage recent any-order AR models to estimate order-marginalizedpredictions, unlocking the high classification potential of AR models.Our approach consistently outperforms diffusion-based classifiers acrossdiverse image classification benchmarks, while being up to 25× more ef-ficient. Compared to state-of-the-art self-supervised discriminative mod-els, our method delivers competitive classification performance – a no-table achievement for generative classifiers. The code and models areavailable at: https://github.com/yandex-research/ar-classifier.

ECCV 2026

DiGS-Avatar: Single-Image Animatable 3D Human Reconstruction via UV-Space Diffusion

Jiakun Li, Li Fang, Hao Zhu, FEI HU, Long Ye, Yuan Zhang, Jinyao Yan

Single-image 3D human reconstruction often suffers fromover-smoothed textures and geometric inconsistencies. While diffusionmodels improve generative quality, their reliance on multi-view synthe-sis prior to 3D reconstruction is computationally expensive and proneto view inconsistency. We propose DiGS-Avatar, which reformulates thistask as an efficient, diffusion-based UV-latent completion task, ensur-ing 3D consistency by design. To capture accurate spatial structure, weintroduce a teacher-student framework where a multi-view teacher pro-vides geometrically aligned pseudo-ground-truth latents to supervise asingle-view diffusion student. Treating this inferred latent as a robuststructural skeleton, our method injects high-level semantic features to ac-curately recover fine textural details without disrupting spatial integrity.The refined representation is then decoded into 3D Gaussian primitives.Extensive experiments demonstrate that DiGS-Avatar achieves state-of-the-art or highly competitive visual fidelity and zero-shot generalization,while reconstructing a fully animatable 3D avatar in just 0.71 seconds.Code is available at https://github.com/KLMAV-CUC/DiGS-Avatar.

ECCV 2026

Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging

Shuhong Liu, Xining Ge, Ziying Gu, Quanfeng Xu, Ziteng Cui, Lin Gu, Xuangeng Chu, Jun Liu, Dong Li, Tatsuya Harada

Astronomical imaging remains noise-limited under practicalobserving conditions. Standard calibration pipelines remove structuredartifacts but largely leave stochastic noise unresolved. Although learning-based denoising has shown strong potential, progress is constrained byscarce paired training data and the requirement for physically inter-pretable models in scientific workflows. We propose a physics-based noisesynthesis framework tailored to CCD noise formation in the telescope.The pipeline models photon shot noise, photo-response non-uniformity,dark-current noise, readout effects, and localized outliers arising fromcosmic-ray hits and hot pixels. To obtain low-noise inputs for synthesis,we stack multiple unregistered exposures to produce high-SNR bases. Re-alistic noisy counterparts synthesized from these bases using our noisemodel enable the construction of abundant paired datasets for supervisedlearning. Extensive experiments on our real-world multi-band datasetcurated from two ground-based telescopes demonstrate the effectivenessof our framework in both photometric and scientific accuracy. Code isavailable at https://github.com/ShuhongLL/Denoising-Deep-Sky.

ECCV 2026

CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation

Apoorva Vashisth, Manav Kulshrestha, Pranav Bakshi, Damon Conover, Guillaume Sartoretti, Aniket Bera

Robot navigation typically assumes an obstacle-free path exists between start and goal. In real environments, however, clutter may block all routes. We introduce Lifelong Interactive Navigation, where a mobile robot with manipulation capabilities must move objects to forge paths and complete sequential object-placement tasks. Because environment modifications persist, decisions impact future navigability and task difficulty. We propose CoReLIN, an LLM-driven constraintbased reasoning framework with active perception. CoReLIN reasons over a structured scene graph to decide which objects to relocate, where to place them, and where to explore next. A standard motion planner executes reliable navigation and manipulation primitives. To evaluate longhorizon behavior, we introduce 2 new metrics - Long-term Efficiency Score (LES), a unified metric capturing success, execution efficiency, environment optimality, captured by Price of Clutter. In ProcTHOR- 10k, CoReLIN outperforms best baseline by 16% under standard metrics and LES, and transfers to real-world hardware5.

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