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 88
STEP: Surgical Point Tracking from Sparse Endpoints and Pseudo-trajectories
Surgical point tracking requires persistent correspondence through tissue deformation and occlusion, yet dense trajectory annotations are costly to obtain.Spectra: Multi-Scale Implicit Decomposition for Deepfake Detection
Deepfakes increasingly undermine trust in digital media, yet existing detectors often struggle to generalize across unseen manipulation techniques, compression levels, and dataset shifts.Interpreting Physics in Video Encoders using Multi-Object Interaction Videos
Video encoder models are increasingly used as world models for physical AI, where a policy plans by rolling out the latent, so what physics the latent holds bounds what the agent can plan over.OpenHome: From Perception to Insight in Smart-Home Camera Videos
Vision language models (VLMs) have been increasingly adopted in consumer smart-home camera systems to interpret recorded videos and provide insights.Evidence-Guided Selection of Retinal Vessel Segmentation Pipelines: A Cross-Dataset Benchmark of Performance, Structure, Failure Modes, and Reproducibility
Retinal vessel segmentation is widely used in retinal image analysis, yet model selection based only on aggregate overlap scores can overlook structural fidelity, cross-dataset robustness, and…T-DIG: Text-Conditioned Diversity for Image Generation
Text-to-image (T2I) platforms typically return several images per prompt so users can explore different visual interpretations and choose one that matches their intent.Enhancing Cross-Grade Generalization in Banana Variety Classification via Hybrid CNN-Transformers and Knowledge Self-Distillation
In recent years, automated fruit classification systems have become a more critical component in precision agriculture for efficient yield estimation, quality control, and supply chain optimization.Single-Image Food Mass Estimators Do Not Transfer Their Gram Scale Across Datasets
Estimating food mass from a photograph is central to image-based dietary assessment, but whether trained models transfer to new cameras and foods is unclear.HumanCentric-Scenes-AI: A Human-Curated Multimodal Dataset of AI-Generated Human-Centric Scenes Across Surveillance, Occupational Portraits, AI-Edited Images, and Short-Form Video
We introduce HumanCentric-Scenes-AI, a human-curated dataset of 296 AI-generated stimuli that depict people across four contexts in which people encounter AI-generated media.Weakly Supervised Fish Segmentation through Point-Supervised Tracking and Motion-Informed Language
Understanding the properties of fish populations is increasingly important.OccJEPA: Occlusion as Masking for Amodal Video Segmentation
Amodal video segmentation asks for the complete mask of an object in every frame, including the parts hidden behind other objects.ReSeg: Retraining-free segmentation of abdominal organs via generalizable semantic representation learning
Abdominal organ segmentation is essential in modern medical applications.UNIVERS: Learning UNIversal Voxel-wise Embedding via Randomized Synthesis for generalizable one-shot landmark localization
One-shot landmark localization in volume scans with varying fields of view (FOVs) is challenging.Diffusion Posterior Sampling with Uncertain Forward Models
Diffusion posterior samplers reconstruct signals by combining a pretrained diffusion prior with a measurement likelihood specified by a forward model and a noise model.From Canon to Nikon: Camera Domain Shift and Potential Equity Implications in Retinal AI
Diabetic retinopathy is a leading cause of preventable blindness, yet access to regular screening remains limited worldwide.DriftNeRF: Cross-Ray Density Drifting for Sparse-View Neural Radiance Fields
Neural Radiance Fields (NeRF) synthesize photorealistic novel views from dense image collections, but degrade sharply when few views are available.PhyMo: Learning Physical Dynamics with Accurate and Continuous Motion from Multi-View Videos
Recovering dynamic 3D scene geometry, appearance, and the physical motion states that govern scene evolution from multi-view videos is important yet challenging.WaFIR: Wavelet-Fourier Mixture-of-Experts with Subband-Adaptive Routing for Blind All-in-One Image Restoration
Blind all-in-one image restoration must adapt a shared model to heterogeneous and sometimes mixed degradations without a task label at test time.Two for One: Privileged Learning for Resource-Constrained Screening
Machine learning systems are often developed under data-rich conditions but deployed when only incomplete information is available.DGCM-SSFA: Dual-Granularity Cross-Modulation and Sparse Strip Frequency Attention for Efficient 3T-to-7T MRI Synthesis
Synthesizing high-fidelity 7T MRI from routinely acquired 3T scans offers a practical pathway to enhanced neuroimaging without the prohibitive cost of ultra-high-field scanners.