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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 124
Spectral-Spatial Interpretation
Post-hoc image explanations usually show where a model looks using spatial heatmaps or indicate which channels matter using global channel scores.ResStreamGS: Residual-Sensitive Dual-Stream Gaussian Splatting for Mobile Volumetric Video Streaming
Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and computation remains challenging.Sparse Attention for Video Generation Acceleration via Growing Sparsity and Reduced Search
Diffusion Transformers (DiTs) dominate video generation but suffer from high inference cost due to the quadratic complexity of self-attention.Trajectory Prediction Method for person under WSN with Hierarchical Envelope Adversarial Domain Adaptation
Pedestrian trajectory prediction holds significant applications across autonomous driving, intelligent surveillance, and smart cities.VisualTrust: Video Reasoning under On-Screen Claim Framing
On-screen text can describe what a video shows, or it can frame what the video is supposed to mean.Monotone Piecewise-Linear Harmonization of Multitemporal Multispectral Surface Reflectance With Unsupervised Deep Multiview Pseudoinvariant Features Learning
Surface reflectance products derived from optical satellite may exhibit systematic inconsistencies across acquisition dates, because atmospheric correction is generally performed independently for…ControlFlow3D: Distilling Multi-View Knowledge into Latent Flow Matching for Point Cloud Upsampling
Point cloud upsampling is essential for 3D applications but remains challenging due to sparse, under-constrained observations.UltraDiff: Transferring High-Fidelity Priors to Compressed Latent Spaces for High-Resolution Image Generation
High-resolution synthesis with Latent Diffusion Models (LDMs) faces a fundamental trade-off between computational cost and generative fidelity.SpaceCache: Intermediate Feature Reuse for Efficient Semantic Segmentation on Point Cloud Streams
Point-cloud streams are central to autonomous driving and robotics, yet existing segmentation networks process every frame from scratch, although consecutive frames largely overlap.DLOSAC: Joint Spatial-Geometric Image Estimation via Delaunay-Guided Local Optimization RANSAC
Reliable image geometric estimation from noisy matching points is typically addressed using RANSAC, often combined with local optimization to refine inliers and model parameters.A large-scale climate-aware satellite image dataset for domain adaptive land-cover semantic segmentation
A few well-annotated datasets for land-cover semantic segmentation have recently been introduced to advance the field of earth observation technologies.FocusOPD: Structure-Guided On-Policy Distillation for Diffusion Models
Multi-teacher on-policy distillation enables rectified-flow models with diffusion-transformer architectures to combine task-specific capabilities within a single student.Do VLAs Really Follow Instructions? Diagnosing How-Level Instruction Following in Vision-Language-Action Models
Vision-language-action (VLA) models are expected to follow instructions like their language backbones, yet prior work has focused on What-level instructions that specify the task rather than…TRACER: A Multimodal Harness for Patient Care in Hospitals
Continuous video and clinical records provide complementary information for understanding how patient behavior evolves over a hospital stay.LICOA: Learning Instruction-Conditioned Optical Actions for Mobile Spatial Augmented Reality
Spatial augmented reality (SAR) augments physical environments with projected content, but most systems assume that the projector pose, target region, or motion path has been fixed or specified.Mallea: Building, Steering, and Memorizing Worlds with Coding Agent
Code world models represent world dynamics through explicit state and executable rules.ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models
Training-free object removal commonly redirects self-attention away from the target region, but on few-step distilled diffusion models this often leaves object-shaped residuals.GeoPano: Towards Geometrically Accurate 3D Reconstruction from a Single Panorama
Panoramic 3D reconstruction enables the holistic recovery of complete 360 $^\circ$ scene geometry, offering significant benefits for robotics and AR/VR applications.Dusting the Phantom: Gaussian Allocation for Fading Dynamic Content
Dynamic Gaussian representations have made high-quality, real-time volumetric video increasingly practical, yet challenging motion can still break their reconstruction: small fast-moving objects may…VLA-$Δ$: Learning Physical State Transition for Vision-Language-Action Models
Vision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performance across various benchmarks.