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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 192
CCDF: A Benchmark Dataset for Deepfake Detection in Real-World Surveillance Footage
Due to rapid advances in Generative AI, commercial video generation tools can be used to produce fabricated surveillance footage that can fool both human viewers and automated synthetic video…UltraDiff: Differentiable Ray Tracing in Ultrasound for Shape Optimization
Physically-based differentiable rendering enables gradient-based optimization of scene parameters by matching rendered images to measurements, but has so far mainly focused on light transport.Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction
Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning.DensiTok: Making Feed-Forward 3D Gaussian Splatting See More Views Than It Is Given
Feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene in a single forward pass, replacing per-scene optimization with a network trained across many scenes.EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation
Scaling robotic foundation models requires diverse training data and reliable evaluation environments.M3SunAgent: Monocular 3D Spatial Understanding Agent for Metric Depth Estimation and 3D Visual Grounding
Monocular metric depth estimation and 3D visual grounding represent the two complementary cornerstones of monocular 3D spatial understanding (M3Sun), from which the fundamental 3D spatial…Decide Before You Look: Learning Which Retrieved Memories Deserve Pixels
Multimodal assistants answer questions from long-term memories that contain images.VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs
Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch tokens.A Broader Look at Model Merging: Rethinking Implicit Regularization Induced by Task Arithmetic
Model merging aims to build a multi-task model cheaply by combining the weights of individual task-specific models.PhysTacGen: Physics-Aware Visual-Tactile Sensor Image Generation
Realistic physical interaction is a cornerstone of embodied intelligence, yet collecting paired visual--tactile data remains costly.Two Halves are More than One: Phase-wise Velocity Distillation for Fast and High-Quality Image Generation
Recent diffusion-based image generation backbones have grown substantially in scale, making the network inference cost increase rapidly.Optimization Encoders: Rethinking Second-Order Meta-Learning for Neural Fields
Conditional neural fields represent signals continuously, but their effectiveness depends on how the conditional latent representations are inferred from observed data.Multi-Dataset Diagnostic Utility of Clinical Visual Concepts in AI Systems for Dermatology
The clinical integration of AI systems in digital dermatology relies heavily on human trust.Beyond Training from Scratch: Foundation Models for Data-Efficient and Generalizable Cardiac MRI Reconstruction
Cardiac magnetic resonance imaging reconstruction aims to recover high-quality images from undersampled acquisitions, enabling faster scans while preserving diagnostic fidelity.Supermarket Product Detection and Recognition: Utilizing Deep Learning with Rectified Imagery
Product Identification has sprung up to become one of the most challenging problems in the automation of the retail industry.Mu-DisCoCat: A Variational Pipeline for Compositional Generalization on Quantum Processors
Achieving compositional concept generalization (CoCoGen), the ability to understand novel situations by recombining learned primitives, remains a fundamental challenge in artificial intelligence.VLA-ACL: Action-Consistent Visual Token Pruning for Efficient Vision-Language-Action Models
Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but incur high computational costs from processing long token sequences at every control step, limiting real-time…Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering
AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered.The Failure Is in the Readout: Fine-Grained Emotion Recognition Benchmarks Measure Elicitation, Not Perception
Fine-grained emotion recognition supports therapy tools and social robots, but it needs facial data, which raises privacy and data-protection concerns.Visual Orchestration Tax in Agentic VLM Pipelines: Auditing and Certifying Visual Evidence Reuse
Agentic VLM pipelines increasingly pass the same static visual evidence through multiple specialist agents and tools.