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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 5
Spatiotemporal Dynamics-Aware Industrial Anomaly Detection and Reasoning
Industrial Anomaly Detection (IAD) is a long-established problem that aims to detect defects in industrial products during manufacturing for quality control.TerraMesh-Masks: Open-Vocabulary Segmentation for Earth Observation
Open-vocabulary segmentation (OVS) enables models to segment a variety of concepts specified in natural language, removing the need for fixed label sets and fine-tuning.ATHAAR: A Medieval Scientific Arabic Manuscript Dataset for Handwritten Text Recognition
Despite recent progress in handwritten text recognition, medieval Arabic scientific manuscripts remain an open challenge for current vision-language models (VLMs) due to cursive ligatures, variable…National-scale mapping of tree species composition in Switzerland from high-resolution aerial imagery and multimodal data
Tree-species composition shapes forest productivity and how forests respond to climate disturbances such as drought, windthrow and insect outbreaks.Towards Practical 20 kbps Generative Video Compression via Adaptive Tokenization and Entropy Modeling
Video compression at ultra-low bitrates remains challenging due to the loss of perceptually important details under limited bit budgets.Reinforcing End-to-End Autonomous Driving Safety via Negative-Aware Planning in a Unified World-Action Model
World-action models (WAMs) jointly model future traffic states and driving actions for end-to-end autonomous driving in intelligent transportation systems.ProtoGestalt: Interpretable Prototypes for Facial Phenotyping and Diagnosis of Rare Genetic Disorders
Rare genetic disorders are difficult to diagnose due to their diversity and overlapping clinical features.FreeOcc: Decoupling Ego-Motion for Efficient 4D Occupancy Forecasting via Continuous Flow Matching
Current 4D occupancy world models use heavy cross-attention or deep recurrence to disentangle ego-motion from scene dynamics.How Far Can Untrained Neural Networks Go in Anomaly Detection? From Tables to Pixels
Anomaly detection (AD) is often framed as a representation-learning problem, where strong performance is expected to require learned features or AD-specific optimization.Describe Your Facial Expressions by Linking Image Encoders and Large Language Models
This paper presents a novel task of describing human facial expressions of a facial image in natural language, which captures the nuances of facial actions and emotional states beyond traditional…Fault-Guided Cross-Layer Prediction for Unsupervised Anomaly Detection
Feature reconstruction is widely used for unsupervised industrial anomaly detection in the single-class setting, where only normal images from one product category are available for training.MCUFlowNet: Hardware-Aware Neural Architecture Search for Optical Flow on Microcontrollers
Dense optical flow allows small drones to avoid obstacles.Detecting Weather Fronts from Satellite Observations: What Does Reanalysis Add?
Weather fronts are transition regions between air masses with contrasting thermodynamic properties and are associated with mid-latitude precipitation and severe weather.Interpretable Multimodal MRI-Clinical Fusion for Functional Outcome Prediction After Acute Ischemic Stroke
This study evaluates the added prognostic value of day-1 multimodal MRI for predicting three-month functional outcome after acute ischemic stroke (AIS).La-JEPA: Compact Robot Policies with Latent World Modeling
World-action models adapt pretrained video generators, and vision-language-action models build on pretrained vision-language backbones; both tie policy architecture and inference cost to large…Improving Context-Shift Robustness of Convolutional Networks via Context-Regularized Cross-Entropy
Image classification models often encounter objects in diverse and unpredictable contexts at test time, which may differ substantially from those seen during training.Multimodal Hierarchical Mixture-of-Experts for 3D Medical Vision-Language Modeling
Medical 3D scans and reports are inherently hierarchical, with diagnostic cues concentrated in specific anatomical regions across multiple levels of organization.ProQuant: Progressive Quantization-aware Training for Edge MLLMs
Multi-modal large language models (MLLMs) with impressive perception and understanding capabilities will enable a range of downstream applications.Beyond Quadratic Attention: Separable Gaussian Operators for ODE Vision Transformers
Self-attention carries a quadratic cost in token count, which limits how far Vision Transformers can scale with input resolution.FewStepRL: Fine-tuning Few-step Diffusion Models with Reinforcement Learning
Reinforcement learning (RL) has become a standard post-training method for diffusion models, but most RL objectives roll out the current policy at every iteration, and with a many-step sampler these…