AI & Machine Learning · Computer Science & AI
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 22
Spectral-spatial vision test-time training for hyperspectral image classification
Hyperspectral image (HSI) classification requires the representation of contiguous spectral signatures and spatial context under limited labeled supervision.Do Visual RL Gains Scale at Test Time? A Quantile View of Post-Training
Reinforcement learning (RL) post-training improves the single-sample quality of visual generative models, but deployment often provides additional inference compute through Best-of-N sampling.IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation
Inertial sensors are moving into clothing, from sensorized sportswear to garments instrumented with hundreds of IMUs.MirrorComm: A Communication-Efficient Cooperative Perception Framework via Mirrored World Models
Cooperative perception (CP) requires substantial communication bandwidth to exchange sensing information among connected agents.TERA: Task-Evidence Restoration for Underwater Object Detection under Bandwidth-Constrained Transmission
Underwater object detectors are typically evaluated on pre-transmission images, overlooking additional degradation from bandwidth-constrained downscaling and compression.Blueprint2CAD: Benchmarking Foundation Models on Engineering Drawing-to-CAD Render Correspondence
Engineering drawings specify mechanical parts through orthographic projections.Evaluating Synthetic Skin Lesion Images for Classifier Training: A Safety-Aware Algorithm
Skin cancer classifiers need large sets of annotated images, which are scarce, especially for rare lesion types.Gnomon4D: Learning Where and When to Focus for Geometric Motion-Grounded World-Action Modeling
World-action models (WAMs) predict scene changes to guide robot actions, yet their RGB futures entangle state changes with appearance and lack 3D geometry.End-to-End Stability of Online Active Learning in Large-Scale Neural Systems
Online Active Learning (OAL) aims to extend deep learning from static inference after offline training to continuous adaptation during real-time interaction with a changing physical environment.Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics
3D semantic occupancy prediction provides dense geometric and semantic scene representations for autonomous driving, where both high accuracy and low response latency are crucial for safe downstream…QFacePlacement: From Arbitrary Tensorization to Semantic Spatial Correspondence in Modern Face Verification
Quantum-inspired computer vision often reshapes high-dimensional features into tensors and applies tensor-network models.CellPlex: Benchmarking Single-Cell Spatial Proteomics Prediction from H&E
Spatial proteomics measures tens of proteins in every cell of a tissue section, but its cost confines it to small research cohorts, whereas hematoxylin and eosin (H\&E) slides exist for nearly every…Latent Spectral Prompting for Multimodal Hyperspectral Object Tracking
Hyperspectral videos provide rich spectral signatures for robust tracking under challenging conditions such as camouflage and low illumination.MedHEB: Benchmarking Medical Embeddings Across Heterogeneous Clinical Evidence
Clinical evidence is inherently relational: medical images are interpreted with prior studies, cross-modal examinations, reports, and localized findings, and retrieving relevant evidence is…INTERACT: Learning Interaction-Aware World Dynamics from Driving Videos for Autonomous Driving
Safe autonomous driving requires more than recognizing the current scene: the ego vehicle must anticipate how surrounding traffic participants will evolve under its own motion.PixelBench: Can LLMs Understand and Generate Images Pixel by Pixel?
Large language models (LLMs) can generate visual assets through text-based formats such as SVG and CAD, but these symbolic representations describe images using high-level primitives rather than…Flat Features, Detailed Images? Simple Detail Control for Diffusion Transformers
Prompt engineering is commonly used to encourage text-to-image models to generate more detailed images.SeMo3D: Unifying 3D Generation, Editing, and Completion via Selective Modeling
3D generation, editing, and completion are typically treated as regenerating the complete 3D asset under task-specific constraints, even when only a local region needs modification.Probing Local Conditional Fit for Diffusion Model Membership Inference
Membership inference determines whether a candidate example was used to train a text-to-image diffusion model and is an important tool for auditing training-data use.SMILE: A Structured Multi-Agent Clinical Reasoning for Liver Ultrasound Understanding
Automated liver ultrasound report generation improves the diagnosis of liver diseases by producing clinically structured descriptions of anatomical and disease-related findings.