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版本更新 · v5arXiv:2507.01835

Filter-Enhanced Smartphone Hyperspectral Imaging

Daniil Reutsky, Daniil Vladimirov, Yasin Mamedov, Georgy Perevozchikov, Nancy Mehta, Egor Ershov, Radu Timofte

原文摘要

Hyperspectral reconstruction from RGB imagery offers an affordable alternative to costly hyperspectral acquisition devices. However, its accuracy is fundamentally constrained by the limited spectral resolution of standard RGB cameras. In this paper, we propose a new approach that leverages the multi-camera systems integrated into most modern smartphones and enhances their capabilities by modulating their spectral response functions using carefully selected spectral filters, enabling a smartphone to capture richer spectral information. We propose a neural network able to exploit auxiliary RGB images with diverse spectral characteristics, enabling more accurate hyperspectral reconstruction compared to conventional single-image methods. Training is supported by a newly collected dataset, Doomer, which consists of multiple misaligned RGB images alongside corresponding hyperspectral data. We also propose a device-adaptation strategy that conditions the multi-image hyperspectral reconstruction network on camera color correction matrices available in standard DNG metadata, requiring no real images from the target device. We evaluate it on three physical smartphones, two of them unseen during training.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.29630

BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation

Johann-Ludwig Herzog, Mathis Jürgen Adler, Leonard Hackel, Yan Shu, Angelos Zavras, Ioannis Papoutsis, Paolo Rota, Begüm Demir

原文摘要

Vision-langugage models (VLMs) have shown strong performance in computer vision (CV), yet their performance on remote sensing (RS) data remains limited due to the lack of large-scale, multi-sensor RS image-text datasets with diverse textual annotations. Existing datasets predominantly include aerial Red-Green-Blue imagery, with short or weakly grounded captions, and provide limited diversity in annotation types. To address this limitation, we introduce BigEarthNet$.$txt, a large-scale, multi-sensor image-text dataset designed to advance instruction-driven image-text learning in Earth observation across multiple tasks. It contains 464044 co-registered Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral images with 9.6M text annotations, including: i) geographically anchored captions describing land-use/land-cover (LULC) classes, their spatial relations, and environmental context; ii) visual question answering pairs relevant for different tasks; and iii) referring expression detection instructions for bounding box prediction. A comparative statistical analysis shows that BigEarthNet$.$txt surpasses existing RS image-text datasets in textual richness and annotation type variety. We further establish a manually-verified benchmark split and extensively evaluate existing VLMs in RS and CV. The results show the limitations of these models on tasks that involve complex LULC classes, whereas fine-tuning using BigEarthNet$.$txt results in consistent performance gains across all considered tasks. Our code and data are publicly available at https://txt.bigearth.net.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13752

Word-Level Alignment of OCR and Ground Truth under Zoning and Reading-Order Errors

Fonseca Cacho, Jorge Ramón

原文摘要

OCR post-processing needs OCR text aligned word by word with its ground truth. Data sets rarely provide that mapping, and a monotone aligner can lose its place when zoning errors permute the reading order. We revisit this problem, the Zoning Problem of our 2019 paper, with a protocol that tests correctness under such permutations. We apply OCR-side reading-order permutations with construction-time word-level gold and score correct-match F1, cascade length and recovery. We compare the 2019 location-aware aligner, a re-anchoring extension of it, a RETAS-style baseline (RETAS-lite), a multi-region aligner that combines unique-word and unique n-gram anchors with chunk-to-window assignment, genalog's RETAS and the original Java RETAS. Experiments use synthetic character noise with block moves, paragraph swaps and column interleaving, historical newspaper reading-order permutations, geometry-derived gold for Tesseract OCR, and a small word-box set. Synthetic tests show where local tracking fails after large displacements and where unique-anchor methods collapse when unique anchors are scarce. On our synthetic boilerplate page the 2019 method had the highest F1 among those tested. Historical newspaper and geometry-derived evaluations carry the limitations of approximate gold and the article-order convention. The first multi-region version has a quadratic scaling cost. Local tracking remains fragile under large reading-order changes; the tested anchor-based methods handle many of those cases but fail when unique anchors are scarce. Approximate gold, the article-order convention, missing residual annotation, and the lack of modern multi-column word-level ground truth remain limitations.

cs.CL · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13739

Effects of Laser Beam Width and Measurement Noise on Tree Diameter Estimation in Laser Scanning

Jesse Muhojoki, Nuutti Ervasti, Eric Hyyppä, Aapo Erkkilä, Eppu Honkanen, Tuomas Yrttimaa, Mikko Vastaranta, Juha Hyyppä

原文摘要

Laser scanning has been utilized for decades to measure tree structure and geometry. Despite continuous advances in sensor technology, uncertainties arising from scanner characteristics and measurement conditions has remained relevant sources of error. In particular, factors such as laser beam diameter can introduce both systematic and random errors in the reconstructed tree geometry. This study investigates the main sources of these errors in mobile laser scanning (MLS) and quantifies their effects on stem curve (stem diameter) estimation. We discovered through simulations and experiments that the heteroscedasticity (difference in variance in ranging and scan angle) in the measurement noise is a key factor along with the beam width of the lidar, which cause the bias of the diameter estimates to depend not only on distance, but also on the tree diameter. We demonstrated that this bias can be reliably predicted and negated for trees with a diameter at the breast height greater than 10 cm using technical specifications of the sensor without any additional calibration. In experiments, the proposed bias compensation method reduced the root-mean-square error of stem diameter estimates to sub-centimeter level on boreal forest test sites. However, for small trees with diameter below 10 cm, the diameter bias becomes sensitive to the diameter of the tree$\unicode{x2013}$an unknown factor$\unicode{x2013}$and other factors, such as different bark and out-of-roundness of the stem, limiting the precision of lidar for measuring geometry of trees and applicability of the presented method.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13700

CJRR-Net: A Candidate-Joint Reprojection Ranking for Future-View Selection in Human Pose Estimation

Huiting Xi, Marsha Mariya Kappan, Eduardo Benitez Sandoval, Francisco Cruz

原文摘要

Multi-camera systems support performance recording, sports and human-movement analysis, where selecting an informative viewpoint is important for reliable human pose estimation. We propose the Candidate-Joint Reprojection Ranking Network (CJRR-Net) for future-view selection among eight calibrated cameras in MPI-INF-3DHP. The model combines current-view pose observations, camera configuration, and candidate-specific geometric features obtained by reprojecting the estimated 3D pose and its uncertainty into each candidate view. CJRR-Net achieves a selected-view 2D mean per-joint position error (MPJPE) of 105.18 pixels, compared with 114.64 pixels for the shared-input MLP and 117.56 pixels for adapted PoseErrNet. It also achieves the lowest mean error among the listed policies in each evaluated occlusion group. A blender rollout illustrates repeated camera selection. These findings support candidate-specific geometry for single-view selection in the evaluated fixed eight-camera setting.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13528

GEMOR-GS: Global Extrinsic-calibration-refined Mapping and Online Relocalization for 3D Gaussian-Splatting SLAM

Yuchen Yang, Ming Ding, Xinyu Wu, Hesheng Wang

原文摘要

Photorealistic and metrically consistent world representations are a primary requirement for LiDAR--inertial--visual SLAM systems that serve robots over continuous operation. 3D Gaussian splatting makes such representations attainable by supplying an explicit, renderable, and queryable scene model. We present GEMOR-GS (Global Extrinsic-calibration-refined Mapping and Online Relocalization for 3D Gaussian-Splatting SLAM) to make the Gaussian map geometrically accurate, globally refined, and re-enterable, providing a better appearance for robot perception and decision-making. To improve the 3DGS map, the LiDAR--camera extrinsic is first estimated offline by direct-method alignment over dedicated calibration segments and reused by all following online and offline processes. Under the calibrated geometry, keyframe poses and Gaussian parameters are refined alternately under a gated differentiable-rendering and LiDAR--projection objective. The final global reconstruction is then rebuilt from the refined keyframe schema with all online constraints lifted. For the robot SLAM lifecycle, the method recovers the pose from the 3DGS map after tracking loss through an XFeat bypass map, RANSAC-PnP coarse localization, and Gaussian RGB-D warp refinement. Experiments on one self-collected and five public sequences quantify each element: the offline reconstruction wins all paired keyframes against the online map by 14.3--19.9 dB, improves PSNR by approximately 9 dB over Gaussian-LIC2, and achieves 86--100% held-out relocalization success tied to extrinsic quality. The results show that GEMOR-GS improves the geometric accuracy, appearance fidelity, and re-enterability of the 3DGS map, serving as a dependable robot world model for SLAM and downstream tasks.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12668

Controlled Binary Assignment Flows

Paul Fröhling, Christoph Schnörr

原文摘要

This paper introduces a geometric approach to the representation and learning of time-variant parametrizations of deep assignment flows, for binary data labelings on graphs. Each edge carries an optimally controlled parameter flow in a 2-dimensional totally geodesic submanifold, based on objectives derived from a non-local linear scale space representation of the input data. The aggregation of triplets of such flows in 5-dimensional totally geodesic submanifolds is learned from data. The fusion of these flows determines the trajectory in the high-dimensional symmetric parameter space and by geometric integration the layers of binary deep assignment flows. Numerical results demonstrate the expressivity and the limitation of our highly parallel geometric local synthesis approach to high-dimensional deep assignment flow parametrizations.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31228

Lightweight Frame Compensation for Real-Time Polyp Segmentation under Frame Skipping

Weihao Gao

原文摘要

Real-time polyp segmentation in computer-assisted colonoscopy is hampered by the difficulty of deploying high-accuracy models under strict latency budgets. Frame skipping, which performs inference every K frames and reuses or interpolates the rest, improves throughput but introduces motion blur and lesion misalignment that degrade temporal consistency. To close this gap, we propose the Lightweight Frame Compensator (LFC), a 77K-parameter plug-and-play module inserted before a frozen segmentation backbone. Operating on a three-frame window, LFC fuses consecutive frames via spatio-temporal attention into a single compensated frame, and is trained only on pseudo-labels generated by a differently seeded model, without any expert annotation. Experiments on three polyp video benchmarks show that LFC recovers most of the accuracy degraded by direct frame skipping and, in several settings, reaches the accuracy of frame-by-frame inference, while retaining a 2.05X--2.55X throughput gain. We further evaluate the temporal stability of three backbones spanning CNN, pyramid Transformer, and global-attention Transformer architectures, revealing a spectrum of sensitivity to frame skipping that arises from how locally or globally each architecture aggregates misaligned input. The compensation gain of LFC follows this spectrum: it is largest for high-capacity Transformer backbones, which achieve the best static accuracy but suffer the most under skip-frame inference. LFC thus offers a lightweight, pluggable, and deployment-friendly route to real-time polyp segmentation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12162

Same Physics, Different Gradients: A State-of-the-Art Report on Differentiable Physics Simulation

Lei Shu, Zizhou Huang, Yunuo Chen, Yifei Li, Tao Du, Minchen Li, Tuur Stuyck, Miles Macklin 等 18 位作者

原文摘要

Differentiable simulation propagates derivatives through a physics simulator, turning animation, system identification, control, and computational design into gradient-based optimization. Engines differ in the physics they model and, less visibly, in which mathematical object they differentiate. We separate two questions the literature conflates. The referent is which object the returned derivative belongs to: the finite computation that executed, the converged equation it approximates, or the continuous dynamics. The realization describes the organization of the backward pass: automatically generated differentiation, an equation-level adjoint solve, or a custom adjoint. The referent defines the target against which exactness is assessed; the implementation determines how accurately that target is approximated. Cost depends on the realization together with how the states needed by the backward pass are retained or recovered. Crossing the axes places every method and engine we classify in a cell, separates claims the literature merges, and shows that the apparent trade-off between exactness and memory follows from how methods have been built rather than from a constraint. A second, orthogonal taxonomy covers contact, where the map is non-smooth. We then ask whether a correct gradient helps. Four mechanisms by which useful gradients are lost are cataloged and measured on one testbed, where the advantage of the exact derivative over the equation-level adjoint depends on where the fitting target comes from as much as on the solver budget, and we review the evidence that zeroth-order methods can outperform simulator gradients in the regimes where those mechanisms act. The model classes, systems techniques and applications are surveyed through this lens, and we close with a reporting checklist, a released diagnostic that computes it, and a research agenda.

cs.GR · cs.CV · cs.LG · cs.RO · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12129

RAPA: Ray-Aware Physics Augmentation for Robust Camera-LiDAR Calibration

Kyung-Rin Oh, Ji-Hoon Kim, TaeJoon Ha, Jae-Sang Hyun

原文摘要

Camera-LiDAR calibration is essential for reliable perception in robotic and autonomous systems. Most target-based calibration methods rely on checkerboard observations, but accurate checkerboard feature extraction from LiDAR point clouds remains challenging due to measurement sparsity. Existing approaches either require manual annotation of 3D corners or depend on geometric feature extraction methods, both of which can become unreliable when checkerboard features are insufficiently observed. In this paper, we formulate checkerboard localization as a 2D detection problem using cylindrical LiDAR reflectivity images. To overcome the limited availability of labeled training data, we propose ray-aware physics augmentation (RAPA), which generates diverse synthetic samples from a single checkerboard observation. The generated data are used to improve detector robustness and generalization under varying sensing conditions. Across four independently captured indoor and outdoor locations, the proposed synthetic augmentation exceeds the strongest evaluated image-space augmentation baseline by 16.1-43.1 percentage points in mAP@0.5. Compared with training-free target-based methods on 242 observations, RAPA yields the largest number of valid target localizations, with 96.4% of its returned detections yielding valid target poses. These reliable localizations support downstream camera-LiDAR calibration across all four evaluated locations. Overall, RAPA addresses a key bottleneck in target-based calibration by enabling automated LiDAR-side target localization with only a single annotated target observation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2506.16129

Neurosymbolic Object-Centric Learning with Distant Supervision

Stefano Colamonaco, David Debot, Giuseppe Marra

原文摘要

Neurosymbolic learning can use symbolic rules to provide supervision for latent concepts from weak labels, but it commonly assumes that the entities referenced by these rules are already specified. Object-centric models decompose images into slot-like representations; however, such slots are not necessarily aligned with the predicates required for symbolic reasoning. We investigate object-centric neurosymbolic learning under distant supervision, where the object-level arguments of a logic program are learned directly from images using only global task labels. We introduce DeepPbjectLog, a probabilistic neurosymbolic model that integrates a slot-based perceptual encoder with a probabilistic logic layer. The encoder predicts objectness and class probabilities for candidate object representations, while the logic layer marginalizes over latent objectness and class assignments to compute the likelihood of the observed label. This formulation provides a differentiable task-level learning signal for object-centric perception without requiring per-object labels, masks, bounding boxes, or heuristic set matching. Evaluations across diverse visual reasoning tasks demonstrate that DeepObjectLog achieves superior out-of-distribution generalization to compositional, object-count, and rule shifts compared to neural object-centric and standard neurosymbolic baselines.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12102

SPARK: Simple Post-training for Adapting pRetrained Knowledge to Robot Control

Taekyung Kim, Jeongeun Park, Byeongho Heo, Sungjoon Choi, Sangdoo Yun, Dongyoon Han

原文摘要

Large-scale representation learning has produced public foundation models that provide strong general-purpose visual features. However, their pretraining objectives are not designed for robot representation learning, so the resulting embeddings tend to emphasize global semantics rather than the temporally sensitive representations required for robot control. Training robot models from scratch is also undesirable due to the limited scale and high collection cost of robotics data, as well as the substantial computational burden of pre-training. In this paper, we propose SPARK (Simple Post-training for Adapting pRetrained Knowledge), a post-training method that adapts foundation models to robot control. SPARK is built on two principles: temporal abstraction, which encourages the encoder to derive compact features that preserve information essential for understanding temporal changes and action-relevant scene structure, and knowledge preservation, which aligns patch-level representations with those of the original foundation model to retain useful pretrained semantics. These objectives yield compact visual state representations that remain semantically meaningful while preserving useful prior knowledge. Experiments across multiple robotics benchmarks show that SPARK consistently improves success rates and generalization over vanilla foundation models, and further demonstrate that these gains transfer to real-world robot manipulation.

cs.RO · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.19911

CitySTAR: Structured and Topology-Aware Reasoning for Open-Vocabulary Urban 3D Grounding

Shuai Zhang, Hongye Hou, Qinghe Liu, Zhuoxiao Li, Dongli Wu, Jing Ou, Yuan Liu, Wufan Zhao

原文摘要

3D grounding aims to localize target entities in complex scenes from natural language and plays a fundamental role in embodied perception and spatial reasoning. However, existing approaches mostly rely on feature similarity or direct matching, making it difficult to connect natural-language intent with the implicit semantic and geometric structures hidden in billion-scale urban point clouds. We reformulate city-scale 3D grounding as structured constraint reasoning, where description semantics are organized into computable cross-modal constraints over open-vocabulary 3D entities, attributes, and spatial relations. We present CitySTAR, a training-free framework for reasoning-driven urban 3D grounding. CitySTAR lifts raw billion-scale urban point clouds into a query-ready scene graph of open-vocabulary 3D instances, with CodeLLM-driven tools supplying multimodal evidence for node attributes and 3D spatial relations. It then models target-context topology with paired hypergraphs and performs bidirectional topology verification for structural disambiguation. Finally, a Reflective Cross-modal Grounding module integrates topology consistency and candidate-centered 2D visual evidence to make decisions over a metric-aware 3D context graph. To further support this setting, we introduce CitySTAR-3D, an enhanced benchmark that improves semantic coverage, instance completeness, bounding-box fidelity, and spatial-relation complexity in city-scale 3D grounding. Extensive experiments show that CitySTAR consistently improves open-world urban 3D grounding while maintaining strong interpretability and generalization.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08477

EgoGeneralist: Unifying Egocentric Action-Video Tasks through Video-Native Modeling

Yanlin Jin, Bowen Yang, Cheng Zhang, Yifan Yang, Zhe Li, Jianfei Cai, Kai Zhu

原文摘要

Modern video models possess strong visual priors, yet do not natively represent human action in metric 3D space. We present EgoGeneralist, which adapts pretrained video generators to unify action-conditioned video generation, pose estimation, and joint action-video generation for egocentric manipulation. Our role-adaptive action renderer provides video-native representations for multidirectional action-video learning, making metric motion usable as both a spatial control signal and a recoverable generation target. Cross-stream action-video coupling establishes close correspondence between metric motion and visual dynamics through lightweight adaptation while preserving the pretrained visual prior. Together, these designs establish a generalist framework spanning six action-video tasks. With only narrow-domain data, EgoGeneralist generalizes to diverse unseen scenes and interactions with effective action control and visual quality, bridging pretrained visual knowledge and metric 3D human motion.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11926

Text Alone Predicts Which Classes a Segmentation Readout Keeps

David Yan

原文摘要

An open-vocabulary segmentation readout silently loses a class deployed beyond its training categories. It still emits masks with high confidence, but the class never wins a single pixel. The training loss falls anyway, and mIoU can even rise while classes die. We show that which classes collapse is written in the class-name list alone. Before any image is seen, we rank each class's collapse risk from its text similarity to the training vocabulary alone. The ranking holds at AUROC 0.69-0.96 across nine model families, twelve backbones and eight pretraining recipes. Two further families, measured only after the predictions were written down, land in the same band. What the list writes, it can rewrite. With identical images and a class's counterpart moved in or out of the training list, retraining flips that class's gain over the frozen baseline by 21 IoU points; swapping in unrelated names changes nothing. We find a vocabulary-distance mechanism behind the collapse. Collapsed classes lose every vote to near-name neighbors, but in the frozen features they survive. Turning down the readout's amplitude, or stopping training early, brings them back, up to +16 points, without retraining; trained with its amplitude held down, the readout never collapses.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37364HF 社区推荐

EmbeddingGemma 2: Unified and Efficient Multimodal Representations

Henrique Schechter Vera, Sahil Dua, Jovan Andonov, Sara Smoot, Sebastian Russo, Ivan Llanos, Max Matveev, Jae Hyeon Yoo 等 77 位作者

原文摘要

We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space. Our approach introduces improvements such as simplified per-layer embeddings and a multi-stage curriculum with modality and objective progression which ensure strong quality across unimodal, cross-modal, and mixed-modal evaluations. Notably, in comprehensive embedding benchmarks across all modalities, EmbeddingGemma 2 establishes a new Pareto frontier, outperforming both specialist models and models two to twelve times larger. Furthermore, our design offers flexibility for resource-constrained environments, supporting selective modality encoder loading, adjustable vision token budgets, truncatable embedding dimensions, and quantized weights. We present ablations analyzing key decisions and release EmbeddingGemma 2 to promote further research.

cs.LG · cs.CL · cs.CV · cs.SD

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.08612

Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions

Spyridon Georgiou, Aggelos Psiris, Spyridon Evangelatos, Thomas Lagkas, Vasileios Argyriou, Panagiotis Sarigiannidis, Iraklis Varlamis, Georgios Th. Papadopoulos

原文摘要

Facial Expression Recognition (FER) has advanced rapidly over the last decade, driven by the shift from handcrafted descriptors and shallow classifiers to deep convolutional, attention-based, vision-language, and foundation-model architectures, and by the parallel growth of large-scale in-the-wild benchmarks spanning categorical, dimensional, compound, micro-expression, Action Unit (AU), and intensity-estimation tasks. Yet the deep learning-based FER landscape has so far been reviewed only along narrow task-, architecture-, or application-specific axes, leaving a holistic, systematically organized account of its recent advances missing. This survey addresses that gap with a comprehensive review of recent deep learning-based FER, explicitly linked to the wider Facial Affect Recognition (FAR) domain. Its main contributions are: a) A description of FER's evolution into five distinct phases, from handcrafted features and classical machine learning to attention-based, vision-language, and foundation-model approaches, with the key milestone works of each, b) A multi-criteria taxonomy analyzing the literature along seven complementary axes: recognition task, input modality, face pre-processing pipeline, network architecture, learning strategy, acquisition setting, and application domain, c) A per-criterion comparative analysis, with critical insights into the strengths and limitations of each category under in-the-wild conditions, d) A task-organized review of public FER datasets, with their annotation schemes, modalities, and evaluation protocols, e) A compilation of performance metrics and a per-task quantitative comparison of representative state-of-the-art methods on widely adopted benchmarks, and f) A discussion of current challenges and promising future directions.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11500

ForeAct: Geometry-Guided Action Learning from World Representations

Wei Lu, Jiaxi Zhang, Yunheng Wang, Taowen Wang, Yuetong Fang, Zhenhui Zhang, Shuning Zhang, Zizhao Yuan 等 11 位作者

原文摘要

Robotic manipulation requires representations that capture both scene geometry and its evolution during interaction. Existing approaches have largely treated these two aspects separately. World action models capture such dynamics by coupling action prediction with future video generation, but their reliance on iterative diffusion or rectified-flow inference leads to high computational cost, while their representations lack explicit spatial structure. Explicitly modeling scene geometry typically requires auxiliary modules that increase both parameter count and computational cost. Conversely, geometry-centric policies primarily recover observed spatial structure and often lack explicit supervision for anticipating future geometric changes during interaction. To address these limitations, we introduce ForeAct, which repurposes a pretrained video world model as a single-pass representation backbone for low-latency control. ForeAct directly processes the clean latent of the current observation in one forward pass, eliminating future-frame synthesis and iterative denoising while retaining the backbone's pretrained spatiotemporal priors. Rather than introducing a separate geometry backbone, ForeAct uses learnable tokens supervised by next-frame relative depth to shape the shared video representation through bidirectional self-attention. This compact interface turns the spatial knowledge already encoded in the backbone into a future-aware representation for action generation, with little parameter overhead. The lightweight geometry readout is used only during training and removed at deployment, enabling low-latency, geometry-aware control without depth input or an external geometry backbone. Without additional large-scale pretraining, ForeAct achieves 96.6% average success on LIBERO, 63.5% on SimplerEnv-Bridge, and a 74.4% success rate across 90 real-world rollouts.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.12658

UniDoc: Towards Unified Thinking in Documents

Qixin Xu, Haozhe Wang, Che Liu, Fangzhen Lin, Wenhu Chen

原文摘要

Answering questions over long, visually rich documents demands both cross-page structural reasoning and fine-grained visual understanding. Existing approaches sacrifice one for the other: retrieval-augmented methods fragment documents and lose inter-page structure; monolithic vision-language models preserve structure but scale poorly beyond a few pages. This impasse stems from a false premise: that the macroscopic task of evidence retrieval and the microscopic task of detailed reasoning must either be split into separate systems or be processed identically. By decoupling their perceptual resolutions rather than their architectures, a single model can achieve the structural holism of monolithic processing alongside the efficiency of targeted retrieval. UniDoc realizes this through two resolution-adaptive stages: a Fast Reading stage processes all pages at low resolution to structurally identify evidence pages, and a Focused Thinking stage re-processes only those pages at high resolution for multimodal inference. To train this pipeline, we construct 30K multi-hop reasoning instances with stage-level supervision. On MMLongBench-Doc, UniDoc yields relative accuracy gains of 55.7% on Qwen2.5-VL-7B and 16.9% on Qwen3-VL-8B, while significantly reducing token cost relative to uniform high-resolution processing. Error analysis identifies page retrieval and fine-grained visual reasoning as the remaining bottlenecks, delineating concrete targets for future work.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11272

BiRefNet-V2: Rethinking Bilateral Reference for High-Resolution Dichotomous Image Segmentation

Peng Zheng, Deng-Ping Fan, Binglu Wang, Nicu Sebe

原文摘要

Dichotomous image segmentation requires pixel-accurate foreground-background separation while preserving fine structures such as thin filaments, sharp edges, and intricate textures. BiRefNet addressed this challenge through bilateral references, combining image patches and gradient information, but its inward reference loses local spatial structure and lacks semantic guidance, while its outward reference relies on single-channel gradient attention. We recast this paradigm as controlled information recovery - locality determines what spatial information survives, semantic selectivity what information enters, and focused boundary supervision where refinement occurs - and propose BiRefNet-V2 to implement it: (1) locality-preserving and semantically guided detail injection through space-to-depth transformation, overlapping patches, and a Semantically Guided Gate; (2) a learned boundary stream with focused supervision that generates full-channel residual attention without requiring boundary annotations; and (3) a dual-branch decoder integrating depth-wise boundary convolutions with large-kernel attention. A modernized framework - LayerNorm, GELU, the Lion optimizer, and a DINOv3 backbone - cuts training from 600 to 50 epochs. Extensive experiments demonstrate state-of-the-art performance on DIS5K, camouflaged object detection, and high-resolution salient object detection across all 13 evaluation sets. Code is available at https://github.com/ZhengPeng7/BiRefNet-V2.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11210

DynOLAT: OLAT Video Prediction for Controllable Dynamic Full-Body Relighting

Taoming Liu, Yinji ShenTu, Guangzhao He, Yudong Jin, Yukang Ye, Siyu Zhang, Fan Jia, Wei Hua 等 10 位作者

原文摘要

Accurate lighting response is essential for realistic dynamic human relighting, because shadows, highlights, and color changes should follow the target illumination in both direction and relative strength. Current learning-based relighting methods can generate visually plausible videos, but they often exhibit imprecise lighting responses: shadows and highlights may drift away from the target direction, and the output brightness may not faithfully follow the relative intensities in the target environment, making the result closer to appearance harmonization than controllable relighting. To address this issue, we propose DynOLAT, a generalizable OLAT video prediction framework for dynamic full-body humans. Instead of directly synthesizing a single relit appearance, our method predicts temporally consistent One-Light-At-a-Time (OLAT) basis videos from flat-lit inputs, and then linearly combines the predicted bases according to the target environment map. This OLAT-based formulation makes the directional response explicit in the predicted bases, while the relative lighting strength is determined by the linear weights derived from the environment map. We further introduce a dynamic capture pipeline and the first large-scale dynamic full-body OLAT dataset, comprising over 24 million frames from 224 identity-outfit combinations, where different outfits of the same person define separate combinations. Experiments show that DynOLAT responds more faithfully to variations in target lighting direction and relative intensity than the tested baselines, enabling controllable video relighting while preserving fine appearance details and temporal coherence. The dataset and processing tools will be publicly released to foster future research.

cs.CV

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版本更新 · v2arXiv:2605.23522

Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models

Jade Zou, Tao Huang, Weijie Kong, Junzhe Li, Yue Wu, Qi Tian, Jiangfeng Xiong, Jianwei Zhang 等 10 位作者

原文摘要

Reinforcement learning (RL) has become an effective way to improve prompt alignment and perceptual quality in diffusion and flow-matching generators. A critical step for applying online RL to flow matching is turning the deterministic sampling trajectory into a stochastic policy, typically by replacing the reverse-time Ordinary Differential Equation (ODE) with a Stochastic Differential Equation (SDE). The stochastic sampler, controlling the exploration behavior and denoising dynamics, is thus part of the policy, and its design can significantly affect the reward optimization performance. We break down the sampler design into two interdependent components: choosing the right amount of stochastic exploration, and discretizing the resulting SDE faithfully at the small step counts used in RL. To address the first component, we analyze the inherent tension between exploration and stability in denoising and derive an SDE schedule that balances the two. Turning to the discretization challenge, we use a toy example to show that existing samplers can deviate from the flow-matching process, either by introducing excessive discretization noise or by relying on heuristic rules that do not guarantee convergence to the data distribution. To address these issues, we propose Precise, a new stochastic sampler that balances effective exploration with stability. Crucially, Precise keeps the denoising trajectory SDE-consistent through a novel approximation that freezes the clean-latent posterior mean, resolving the excess noise issue in standard samplers. Extensive experiments demonstrate that this formulation leads to significantly faster and more stable reward optimization via reinforcement learning, achieving state-of-the-art alignment scores (e.g., PickScore, HPSv2.1) while requiring 13.1-53.2% less wall-clock training time to match the best in-domain performance of prior samplers.

cs.LG · cs.AI · cs.CV

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版本更新 · v8arXiv:2603.26783

Can We Change "Stroke Size" to Improve Flow Matching?

Yunwei Bai, Ying Kiat Tan, Tsuhan Chen

原文摘要

Deep-learning-based image generation models are useful but difficult to train. Inspired by the varying stroke sizes used in human painting, we hypothesize that applying ``rough strokes'' can attenuate prediction biases and improve flow-matching image generation. We develop MultiStroke, which progressively varies the spatial granularity of the clean-signal component in the velocity regression target. A lightweight corrector aligns adjacent roughness levels during sampling. On ImageNet-1K, MultiStroke reduces FID by $22.5\%$ relative to a matched flow-matching baseline, despite the limited corrector capacity. Code is available at https://github.com/DaChengRuoQue/MultiStroke-Code. A quick video explanation is available at https://dachengruoque.github.io/MultiStroke-Web/.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10771

WorldFoundry: Towards Unified Infrastructure for World Intelligence

Juanxi Tian, Jiadong Hou, Baiang Li, Xin Lin, Zhengbo Jiao, Weijia Zhang, Yeying Jin, Truong Nguyen 等 16 位作者

原文摘要

Visual generative models increasingly blur the line between synthesizing observations and simulating environments. Yet visual plausibility alone does not constitute successful world modeling: compelling outputs may lack persistent state, physical dynamics, or causally consistent responses to interactions. We define a generative world model as a predictive dynamical system that maintains a persistent, history-conditioned representation of an environment, updates that representation in response to actions or interventions, and generates temporally coherent trajectories that reflect outcomes under observed and counterfactual interventions. From this definition, we derive an intelligence-oriented framework spanning perceptual grounding, persistent state modeling, controllable transformation, dynamics-aware prediction, and closed-loop interaction. We present WorldFoundry, open-source infrastructure for reproducible inference and evaluation across video, 3D and 4D, embodied action, and interactive world systems. WorldFoundry unifies model and benchmark discovery, runtime and asset preparation, execution orchestration, artifact management, and interactive visualization, while modality-specific adapters preserve native inputs, execution semantics, output representations, and evaluation protocols. Explicit manifests, durable artifact contracts, and evidence-aware scorecards separate execution readiness from evaluative claims, making each claim traceable to its supporting artifacts. The catalog contains 270 model entries, 74 benchmarks, and 62 registered metric entries, with readiness and execution evidence recorded separately. WorldFoundry enables reproducible comparison of persistent, controllable dynamics across heterogeneous systems, advancing the field from perceptual synthesis toward measurable world intelligence.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2505.05848

RefRef: A Dataset and Benchmark for Reconstructing Refractive and Reflective Objects

Yue Yin, Enze Tao, Weijian Deng, Dylan Campbell

原文摘要

Modern 3D reconstruction and novel view synthesis approaches have demonstrated strong performance on scenes with opaque, non-refractive objects. However, most assume straight light paths and therefore cannot properly handle refractive and reflective materials. The lack of datasets specialized for these effects has impeded efforts to fairly and thoroughly evaluate performance and thereby make progress in this domain. Most existing datasets focus on opaque scenes, while those targeting refractive objects are often limited to thin glass with negligible ray bending, untinted single-material objects, or backgrounds treated as infinitely-distant, leaving key failure modes under strong refraction untested. To address this gap, we introduce the RefRef dataset. It contains 150 synthetic and 60 real scenes spanning varying levels of geometric complexity and diverse background types that expose the limitations of existing methods. For the purpose of benchmarking, we also provide an oracle method that, given the object geometry and refractive indices, calculates accurate light paths for neural rendering, and a simple two-stage baseline that relaxes these assumptions. We evaluate these against state-of-the-art methods and show that the task is far from solved.

cs.CV

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新论文 · v1arXiv:2611.10728

3D Segmentation in the Clinic: A Grand Challenge

Bram van Ginneken, Tobias Heimann, Martin Styner

原文摘要

This paper describes the set-up of a segmentation competition for automatic and semi-automatic extraction of the liver from computed tomography scans and the caudate nucleus from brain MRI data. This competition was held in the form of a workshop at the 2007 Medical Image Computing and Computer Assisted Intervention conference. The rationale for organizing the competition is discussed, the training and test data sets for both segmentation tasks are described and the scoring system used to evaluate the segmentation is presented.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10699

PLS-GS: Probabilistic Lidar Scaffolds for Compact, Surface-Aligned Gaussian Splatting

Krishnan Harikumar, Alejandro Fontan, Thierry Peynot, Emili Hernández, Niko Sünderhauf

原文摘要

While 3D Gaussian Splatting (3DGS) is typically optimized from images only, many robotics and mapping applications provide both camera and high-definition LiDAR data, combining rich visual appearance with accurate geometric structure. We introduce PLS-GS, a probabilistic LiDAR scaffold for Gaussian Splatting that exploits this complementary sensing setting to construct a compact 3DGS representation aligned to the measured surface. PLS-GS fits a Hierarchical Gaussian Mixture Model jointly over position and color, yielding a continuous and adaptive scaffold that eliminates the need for discrete scene partitioning such as voxel grids or octrees. The fitted mixture then guides the renderable 3DGS Gaussians: each rendering Gaussian is associated with a GMM component and constrained in the local surface frame induced by that component's covariance. A Mahalanobis support loss bounds the deviation of the rendering Gaussians from the corresponding LiDAR-derived surface. Across five large outdoor scenes, PLS-GS places primitives markedly closer to the LiDAR measurements than both image- and LiDAR-initialized baselines, while employing substantially fewer primitives, yielding a considerably more compact model and faster rendering. At matched capacity, PLS-GS places primitives closer to the LiDAR measurements, with comparable rendering quality. Code is available at https://github.com/k-harikumar/PLS-GS.

cs.CV · cs.GR · cs.RO

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版本更新 · v2arXiv:2602.00123

Visual Affect Analysis: Predicting Emotions of Image Viewers with Vision-Language Models

Filip Nowicki, Hubert Marciniak, Jakub Łączkowski, Bartosz Wilczek, Krzysztof Jassem, Tomasz Górecki, Vimala Balakrishnan, Desmond C. Ong 等 9 位作者

原文摘要

Vision-language models (VLMs) show promise for large-scale affect inference from images, but their alignment with human affective ratings remains unclear. We benchmarked 16 proprietary and open-source VLMs on three validated affective image datasets: the International Affective Picture System, the Nencki Affective Picture System, and the Library of AI-Generated Affective Images. In a zero-shot setting, models performed two tasks: (i) top-emotion classification and (ii) prediction of human ratings on 1--7/9 Likert scales for discrete emotions and affective dimensions. We also evaluated rater-conditioned prompting on the LAI-GAI dataset using de-identified participant metadata. Best models showed strong discrete emotion classification (60%--80% on six-emotion tasks; 60%--75% on a 12-category task), with anger and surprise predicted least accurately. For continuous ratings, models aligned moderately to strongly with human judgments ($r > 0.75$) but showed consistent biases, especially weaker performance on arousal and overestimation of response intensity. Rater-conditioned prompting had only small, inconsistent effects. Overall, VLMs captured broad affective patterns but sometimes lacked the nuance, although newer model generations performed better.

cs.HC · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10690

JoVox: Joint Hierarchical Flow Matching for Lightweight Voxel Generation

Xiangjun Tang, Jianqi Chen, Peter Wonka

原文摘要

Generating high-fidelity voxels while keeping their lightweight nature remains an open problem in computer vision. Current generators are either high-fidelity but computationally heavy, or lightweight but of lower fidelity. Hierarchical methods keep the lightweight nature of voxels. However, their coarse levels, which decide where all finer cells can appear, are generated with the least information about the shape and are never revisited, so they are often plausible but wrong. Our key insight is to let finer cells take part in deciding coarse cells. To this end, we propose JoVox, a discrete flow matching framework that learns the joint distribution of all levels of the voxel hierarchy, instead of a separate distribution for each level, so that coarse cells can be predicted with finer cells already generated nearby. Within this framework, we further introduce a two-level prediction, which outputs the cells two levels finer at once instead of one level finer, halving the number of levels that the network processes and yielding voxels of higher fidelity. We also introduce an unmasking strategy that revisits early decisions once finer cells are generated and corrects inconsistencies in masked diffusion models. Our framework outperforms the baselines on both unconditional and image-conditioned voxel generation. Compared with the current state-of-the-art TRELLIS.2, we achieve 8x higher training throughput, 1/15 of the FLOPs in a forward pass, train on 4 vs. 32 GPUs, and obtain higher quality with an 11% lower Chamfer distance.

cs.CV · cs.GT

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.08039

LINE: LLM-based Iterative Neuron Explanations for Vision Models

Vladimir Zaigrajew, Michał Piechota, Gaspar Sekula, Paweł Gelar, Przemysław Biecek

原文摘要

Interpreting the concepts encoded by individual neurons in deep neural networks is a crucial step towards understanding their complex decision-making processes and ensuring AI safety. Despite recent progress in neuron labeling, existing methods often limit the search space to predefined concept vocabularies or produce overly specific descriptions that fail to capture higher-order, global concepts. We introduce LINE, a novel, training-free iterative approach tailored for open-vocabulary concept labeling in vision models. Operating in a strictly gray-box setting, LINE leverages a large language model and a text-to-image generator to iteratively propose and refine concepts in a closed loop, guided by activation history. We demonstrate that LINE achieves state-of-the-art performance across multiple model architectures, yielding AUC improvements of up to 0.15 on ImageNet and 0.05 on Places365, while discovering, on average, 25% of new concepts missed by predefined vocabularies. Beyond identifying the top concept, LINE provides a complete generation history, enabling polysemanticity evaluation and producing supporting visual explanations that rival gradient-dependent activation maximization methods. The LINE source code is available at https://github.com/CredibleAI/LINE

cs.CV · cs.AI · cs.LG

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