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Computer Vision
The newest papers in Computer Vision that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 29 September 2026 to 5 November 2026: 4,996 arXiv preprints and 4 OpenAlex records.
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Newest papers, page 218
A Theory of Shape Reconstruction from Heat Conduction and Shading
Shape from shading using a single image of a Lam- bertian surface is inherently ambiguous.Evaluating Zone-Guided Front Extraction for Glacier Calving-Front Delineation in SAR Imagery
Automatic calving-front delineation from synthetic aperture radar imagery is challenging because the front is a thin and often ambiguous boundary between glacier ice, ocean, and surrounding rock or…Why Convolution Still Matters: Evaluating Inductive Biases in Cryospheric Image Classification
Recent advances in attention-based deep learning have motivated their adoption for remote sensing image classification; however, their benefits for cryospheric imagery, where surface states are…VCURF: Virtual Camera-based Uncertainty of Radiance Fields
Radiance fields, implemented with either implicit (NeRF) or explicit (Gaussian Splatting) representations, are advancing the state of the art in novel view synthesis at a rapid pace.Dependable AI-Assisted Engineering: A Formal Framework for AI Participation and Assurance in Safety-Critical Workflows
Generative AI can produce engineering artefacts, but generation alone does not determine whether or how those artefacts should enter safety-critical workflows.UniBRep: Learning Unified Geometry and Topology for Image-conditioned B-Rep Generation
Generating a boundary representation (B-rep) conditioned on a single image requires faithful reconstruction of geometry, valid topology, and support for complex shapes.Scaling 3D Visual Grounding in Abdominal CT
Visual grounding models can enhance radiology workflows by linking report findings to image regions.Where Does the Semantic Gain Come From? A Reproduction and Extension of Semantic Knowledge-driven Contrastive Learning for Long-Tailed Recognition
Semantic Knowledge-driven Contrastive Learning (SKCL) uses a language model to decide which classes are related, and pulls each image towards the prototypes of its semantic neighbours.From Sight to Foresight: Predictive Spatial Reasoning in Vision-Language Models
Predicting future spatial states supports collision avoidance and timely decision-making in dynamic environments.Watermarks and Fingerprints as Soft Bindings for Content Provenance: An Open-Licence Benchmark for Images, Audio and Video
Content-provenance standards such as C2PA let a platform recover a stripped manifest through a soft binding: an invisible watermark read from the content, or a fingerprint looked up in a registry.Kepler4D: Controllable Future Video Generation via 4D Scene State Evolution
Video world models aim to preserve scene structure and predict how dynamic objects evolve beyond visual observations.Anchor Divergence for Semantic Geometry in Contrastive Learning
This paper concerns how semantic context determines geometry in learned vector representations.STRIDE: Spatial-Temporal Representation for Interval-conditioned Disease Evolution in Longitudinal Glioblastoma MRI
Glioblastoma (GBM), an aggressive primary brain tumor, is routinely monitored with longitudinal MRI after treatment.Abdominal Ultrasound Simulation from Semantic Labels using Paired Label-to-Physics-Based Image Translation
Current abdominal ultrasound (US) simulation methods often require CT-based anatomical references for ray-casting, limiting deformation and pathology variability.Geometric Similarity in VLM Low-Level Vision Representations
Vision-language models (VLMs) have emerged as powerful candidates for universal vision backbones, with representative architectures including autoregressive (AR) models and diffusion transformers…SmoothOperator: Enhancing Representations for Fine-grained Open-set Recognition via Modulated Label Smoothing
Open Set Recognition (OSR) aims to enable models to accurately classify known classes while rejecting samples from unseen classes.Lang3DSeg: Annotation-Free Open-Vocabulary 3D Segmentation with Point Transformers
Accurate 3D semantic perception is critical for safe autonomous navigation.CtrlWAM: Controllable World Action Models with Aligned Intent and Foresight
World action models (WAMs) jointly predict actions (intent) and visual future (foresight).MorphoBranch: A Fine-Structure-Preserving Workbench for Morphometric Analysis of Branched Cellular Structures
Background and Objectives: Fluorescence-labeled cellular arbors provide readouts of neuronal and microglial morphology, but fine and weakly labeled processes are prone to fragmentation and false…Don't Waste the Noise: Importance-Guided Perturbation Allocation under Joint Global and Local Constraints
Adversarial optimization under a shared $\ell_1$ budget requires deciding not only how much perturbation to use, but also where that limited budget should be spent.