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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 154
ConDenseDrive: Knowledge-Dense, Token-Sparse Visual Encoder for End-to-End Autonomous Driving
End-to-end driving requires rich spatiotemporal representations to support scene understanding and trajectory planning under strict latency constraints.FOCUS: A Subspecialty Foundation Model for Intraoperative Central Nervous System Pathology
Intraoperative frozen-section diagnosis of central nervous system (CNS) lesions guides surgical decisions but is challenged by artefacts, diagnostic complexity and time constraints.UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models
Recent advances in multimodal foundation models, exemplified by GPT-6 Astra, raise the prospect of general-purpose embodied reasoning and planning.DDVR-Net: Degradation-Aware Decoupling and Visibility-Guided Restoration for Historical Document Recognition
Historical document analysis is crucial for cultural heritage preservation and low-resource language digitization.Proactive Physical AI through Continuous Observation and Timely Intervention
Physical AI is commonly formulated around an explicitly specified task: given a goal or instruction, a system perceives, reasons, and acts.UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation
Latent Action Models (LAMs) can capture manipulation priors from action-free videos, but discrete latents usually impose a granularity bottleneck, limiting their ability to represent fine-grained…The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors
Visual in-context learning (ICL) enables vision-language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends critically on the examples selected.AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding
Vision-language-action (VLA) models show strong potential for end-to-end autonomous driving by leveraging world knowledge and understanding to excel in long-tail scenarios.Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior
Visual features extracted from vision models like DINOv2 or Stable Diffusion enable high quality semantic correspondence between images.MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation
Reinforcement learning (RL) has shown strong potential for aligning generative models with human intent, but adapting RL to masked generative models (MGMs) remains largely underexplored.MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation
Functional brain connectome represents neural connectivity as a matrix of pairwise interactions between brain regions.DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding
Scaling up data and model capacity has driven substantial progress in visual representation learning.VL-DocIR: A Benchmark for Vision-Based Long Document Information Retrieval
Vision-based document retrieval has improved rapidly, but current evaluation settings still emphasize short documents and single relevant pages.PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation
Virtual cell models should capture phenotypes induced by specific perturbations.A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery
Automating aerial wildlife surveys requires reliable animal detection, consistent delineation of observation strips, and correction for repeated coverage between successive images.Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?
Knowledge-based Visual Question Answering (VQA) requires reasoning that extends beyond visible image content, demanding external commonsense and factual knowledge.Scaling Unified Multimodal Models to Long-Horizon Interleaved Generation
Unified multimodal models (UMMs) provide an interface for interleaved text-image generation, where generated visual states can directly inform subsequent reasoning and synthesis.Contrastive learning for video reuse detection
Recent trends in the ``remixing'' of social media content and the development of bespoke stock libraries for specialist causes mean that methods to identify content reuse will have a range of…EgoProbe: Benchmarking Long-Range and Exact Memory in Streaming Egocentric Videos
Egocentric video provides a continuous first-person record of everyday activities, making it a natural foundation for wearable AI assistants.MARS: Systematic Multi-Level Acceleration for World Action Models
World Action Models (WAMs) have emerged as a promising alternative for embodied intelligence by jointly predicting future world states and robot actions.