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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 33
NAS-PO: Native Attention-Strategy Policy Optimization for Vision-Language Models
Reinforcement learning with verifiable rewards (RLVR) has become an effective post-training paradigm for vision-language models (VLMs).Reconstruction as Spatial Evaluation: Diagnosing Cross-View Spatial Intelligence in Large Vision-Language Models
The spatial intelligence of large vision-language models (LVLMs) requires the ability to combine local visual observations into a geometrically consistent representation of the physical world.4D Triangle Splatting Reconstruction of Dynamic Endoscopic Scenes from Monocular Videos
Robust 4D reconstruction of deformable endoscopic scenes from monocular videos remains challenging due to non-rigid tissue deformation, illumination changes, occlusions, and unknown camera…Scalar Smoothness Is One Point on a Besov Spectrum
Studies of neural network generalization and robustness often summarize smoothness with a single number, such as a Lipschitz constant or a Hessian curvature statistic.Unified Loss-Aware Density Control for 3D Gaussian Splatting
The efficiency and fidelity of 3D Gaussian Splatting critically depend on adaptive density control, which grows and suppresses Gaussian primitives throughout optimization.UniGS-SLAM: Monocular Gaussian SLAM with Enhanced Geometry and Open-Vocabulary Semantics
Monocular semantic SLAM aims to reconstruct scene geometry and build semantic maps from RGB observations.Learning to Look: A Foundation for Visual Understanding
Vision-language models (VLMs) suffer from visual hallucination, such as saying an object that is not present in an image.Do Embodied VLMs Actually Use Their Scene-Graph Memory?
Embodied agents that answer questions about their route need a memory of what they saw, and scene graphs are a natural form for it.Scene-Adaptive Readout and Decoding in Diffusion Driving VLAs
Diffusion driving vision-language-action models repeatedly execute the backbone at every denoising stage, incurring substantial inference cost, while it remains unclear whether all scenes require…EarthJev: Calibrated, Non-Generative Answers to Natural-Language Questions about Sentinel-2 Land Cover
Vision-language models for remote sensing answer questions about satellite images by generating text: every answer costs a decoding loop, has to be parsed back to an option, and comes without a…When Delay Meets Drift: Physical Safety of Robot Foundation Models Is a Cross-Layer Problem
Robot foundation models now act in a closed loop, yet their safety is evaluated one variation at a time: robustness benchmarks perturb the scene while assuming instantaneous inference, and real-time…CNN-Based Retinal OCT Classification: Research Gap Analysis and a From-Scratch Baseline, ED126-A
Automated classification of retinal pathologies from Optical Coherence Tomography (OCT) images remains challenging because diagnostically relevant morphological patterns may exhibit substantial…Testing concept-level audits of melanoma classifiers with diffusion counterfactuals and matched positive controls
Concept-based explanations of melanoma classifiers are rarely tested against controls with known targets.Refining Roadside 4D Radar Vehicle Classification Using Point-Cloud Dispersion
Roadside sensing infrastructure is essential for Intelligent Transportation Systems (ITS), but cameras and legacy sensors are limited in range, weather robustness, and depth accuracy.Conditional Diffusion-Based Generation of T2-Weighted Prostate MRI for Clinically Significant Prostate Cancer Classification
Scarce and unrepresentative training data limit the performance of prostate MRI prediction models.Depth Anyviewpoint? Evaluating Monocular Depth Over Viewpoint Sequences
Monocular depth estimation aims to predict depth maps from single RGB images (forgoing the need for stereo camera pairs) and has seen impressive leaps in performance in recent work due to the…Learning from high-resolution 2D images to reconstruct 3D microstructures
High-resolution three-dimensional (3D) microstructure characterization remains difficult to undertake at scale because volumetric imaging at the required spatial resolution is expensive…Forecasting Earth Surface Change in Embedding Space
Repeated satellite imagery permits understanding of how our planet changes over time, letting us predict its future and plan mitigating actions.Modeling Visual Cortex with Layerwise Multiscale Manifold Capacity Maximization
Task-optimized deep neural networks have risen to prominence as the most predictive phenomenological models of responses in primate visual cortex, but leave much to be desired from the perspective…SVD-Prune: Spectral Vision Token Pruning under Extreme Compression
Vision-Language Models (VLMs) and Vision-Language-Action (VLA) models process hundreds of visual tokens, making visual representations a major computational bottleneck.