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computer vision

3,080 papers

#artificial intelligence Preprint Sep 2026

WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory

WorldCrafter is a video world model that learns a camera-queryable implicit 3D-aware memory that enables streaming scene exploration from a single input image or text prompt and shows substantial gains in long-horizon consistency and camera-control accuracy while preserving visual quality during minute-scale exploratio...

Wang-Bo Yu, Kunhao Liu, Wen-Bo Hu et al. · 1 citation
#artificial intelligence Preprint Sep 2026

DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation

A visuo-tactile WAM that encodes each fingertip independently, aggregates the resulting features through a finger- and pose-aware tactile compressor, and injects the tactile latent into a video diffusion world model for joint visuo-tactile world modeling is presented.

Hao-Ran Yuan, Ze-Kai Wang, Boning Shao et al. · 0 citations
#artificial intelligence Preprint Open access Sep 2026

What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization

Latent diffusion models now dominate medical image generation, and every such pipeline rests on a \emph{tokenizer} that compresses images into the latent codes for image generation to operate on. Thereby, the tokenizer choice bounds every downstream task from reconstruction fidelity and generation quality to the repres...

Niklas Bubeck, Yundi Zhang, Vasiliki Sideri-Lampretsa et al. · 0 citations
#artificial intelligence Preprint Open access Sep 2026

FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation

Multi-organ segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and diverse annotated datasets. Privacy constraints further prevent institutions from sharing patient data to overcome this limitation. Moreover, due to the labor-intensive...

Ashkan Moradi, Bendik Skarre Abrahamsen, Mattijs Elschot · 0 citations
#artificial intelligence Preprint Sep 2026

AgentSTAR: Agentic Shape Tracking and Reconstruction from Monocular Videos

This work presents a method for shape reconstruction and tracking from video via agentic analysis-by-synthesis that infers a structured 3D object model, including its geometry and kinematic structure, and uses this model to optimise object track estimates over time.

Kirill Mazur, Nikita Karaev, Matthew Chang et al. · 1 citation
#artificial intelligence Preprint Sep 2026

VPRune: Efficient Training-free Pre-LLM Visual Token Pruning

Experiments on FastVLM-1.5B across multiple vision-language benchmarks demonstrate that VPRune achieves a favorable accuracy--compression trade-off, with particularly pronounced advantages under aggressive compression, demonstrating its practicality for resource-constrained LVLM deployment.

Guang-Chuan Lv, Dian-Xing Shi, Ding-Jie Fu · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Do LiDAR Language Models Really Understand Spatio-temporal Relationships?

Recent 4D LiDAR language models aim to reason about objects and their evolving spatial relationships. Yet, in our evaluation, always selecting the same option nearly matches the multiple-choice accuracy of two B4DL-derived configurations. We introduce LiDAR-Hallu, a geometry-referenced benchmark and diagnostic protocol...

Runyi Yang, Murat Akkoyun, Di Wen et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Estimating Accurate Hand Pose in Camera Space with Vision Transformer

This framework proposes two key innovations: Transformation-Isomorphism Supervision for hand-depth information extraction and Perspective Information Embedding for resolving above coupling effect of local pose and wrist position, both integrated within the mainstream encoder-decoder architecture.

Kai-Wen Ren, Yi-Ran Jiang, Yong-Jing Ye et al. · 1 citation
#artificial intelligence Preprint Open access Sep 2026

Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection

Image forensics is increasingly an open-world problem: manipulations range from fully synthetic images to localized edits, splicing and swapping, while most forensic detectors remain specialized to a single manipulation family. Agentic AI has recently emerged as a promising solution. In principle, such systems can asse...

Xianlong Li (IMT School for Advanced Studies Lucca, Italy), Pietro Bongini (University of Siena et al. · 0 citations
#artificial intelligence Review Sep 2026

Graded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale

Visual search on large e-commerce catalogs must serve both"similarity"queries that ask for items resembling an uploaded image and"modifier"queries that comprise an image and text describing a desired modification (e.g. a color change or style swap). The latter is the setting known as composed image retrieval (CIR). Exi...

Anubhav Gupta, Hrushikesh Mohapatra, Prijith Chandra et al. · 0 citations
#artificial intelligence Preprint Sep 2026

STAR: Scene- and Task-Aware 4D Radar Preprocessing Towards End-to-End Cognitive Radar

Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements while remaining robust to illumination changes and adverse weather conditions. However, conventional Radar preprocessing methods, such as constant false alarm rate (CFAR) d...

Seung-Hyun Song, Dong-Hee Paek, Seung-Hyun Kong · 0 citations

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Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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