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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: 4,999 arXiv preprints and 1 OpenAlex record.
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Newest papers, page 116
Scene-Adaptive VLA: Efficient Autonomous Driving via Dynamic Layer Routing
Vision-Language-Action (VLA) models offer a promising paradigm for autonomous driving.Severity-Channel Conditioning: An Injection-Level Study for Consumer-Device Deblurring
Motion-deblurring models lose substantial accuracy when transferred from professional cameras to smartphone-derived imagery.Dynamic self-ensemble on multi-exit residual vision transformer for small-data image classification
Vision Transformer (ViT) has garnered increased attention for image classification in recent years.SCOPE-VSR: A Broad-Coverage Training Dataset for Video Super-Resolution
Open-domain video super-resolution (VSR) requires temporally coherent details across widely varying scenes and subjects.IntriAdapt: Generalizable Intrinsic-Guided Test-Time Adaptation for Metric Geometry Estimation
Recovering metric 3D geometry from images is fundamentally ill-posed due to perspective projection ambiguity.Beyond Geometric Closure: Measuring and Diagnosing Mechanical Consistency in Visual Dynamics
Visual accuracy, geometric validity, and agreement with applied forces are distinct requirements for visual dynamics.Spectral-Aware Proposal Enhancement for Diffusion-Based Multispectral UAV Multi-Object Tracking
Multispectral UAV multi-object tracking (MOT) with oriented bounding boxes remains challenging due to small targets, cluttered backgrounds, and unstable inter-frame associations.Modeling Uncertainty Propagation in 3D Morphable Models
We study the problem of mapping parameter-space variance of a 3D Morphable Model (3DMM) to per-vertex variance.Knowledge Diversion: A Novel Paradigm for Pre-training Decomposable Image Generation Models
Large-scale pre-trained models have become foundational to modern machine learning, yet conventional pre-training objectives primarily optimize source-domain performance without explicitly…From Views to Worlds: Active Exploration over 3D Worlds for Vision-Language Models
Vision-language models (VLMs) have achieved remarkable progress in 2D visual understanding tasks, yet the passive view-centric perception paradigm limits their capacity for 3D spatial reasoning.MESS: Multi-Exposure Sequence Synthesis for Generalizable Image Enhancement
Despite the significant progress in image enhancement under adverse illuminations, such as low-light image enhancement, exposure correction, and backlit image enhancement, existing models show…Geometry-Aware Interpolation for Inverse Problems
Recovering missing visual information from incomplete or degraded images remains a challenging problem, particularly when spatial variations and complex image structures affect reconstruction…Editing Images into Reusable Layers via Compositional Edit-Prior Distillation
High-quality image editing requires precise modification of target regions while preserving the content of unrelated regions.CorrFlow: Self-Correcting Flow Matching
Flow-matching models generate samples through iterative velocity predictions, but typically do not revisit these predictions using the intermediate representations that produced them.CorrCLIPv2: Harnessing Unlabeled Images for Open-Vocabulary Semantic Segmentation
Open-vocabulary semantic segmentation aims to classify pixels without being restricted to a fixed set of categories.Design Anything, Design at Scale
As multimodal resources are transformed into media outputs, agentic AI is expected to stay aligned with design intent over long horizons.Evidence Field: Adaptive Covering Programs for Medical Imaging
Medical image classifiers typically return predictions without a native contract for independently reconstructing their spatial decision computation.ATLAS: Learning to Adapt LVLM Attention for Reasoning Video Segmentation
Reasoning Video Object Segmentation (RVOS) aims to localize and segment objects referred to by natural-language expressions throughout a video.Video-Language Entailment for Video Similarity Estimation
Comparing videos to assess their semantic similarity remains a challenging task for current Large Multimodal Models (LMMs), which are primarily trained with single-video objectives.UniAVC: Unified Audio-Video Creation via Progressive Training and Asymmetric Scheduling
Unified audio-video creation requires a single model capable of text-conditioned joint synthesis, bidirectional cross-modal generation, and instruction-guided editing across both modalities.