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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 23
AVID: A 5T fMRI Dataset for Benchmarking Auditory-induced Visual Mental Imagery Decoding
Visual mental imagery (VMI) decoding uses brain activity to reconstruct internally generated visual scenes, providing a potential pathway for externalizing internal experiences and developing…Camera-Free Object Discovery and Interaction with GazeTags
We present GazeTags, a camera- and computer-vision-free approach for worn head-gaze object awareness.BGD-SF PolySegNet: boundary-guided dynamic selective fusion network for robust polyp segmentation in colonoscopy images
Effective polyp segmentation in colonoscopy images is crucial for early detection of colorectal cancer, a leading cause of cancer-related death worldwide.Latent Diffusion-GAN: Adversarial Learning in the Autoencoded Latent Space
Diffusion models are powerful generative frameworks for producing high-quality images by denoising latent variables from random noise.UniCross3D: Unified Cross-View and Cross-Domain Diffusion for Consistent Single-Image 3D Generation
Reconstructing detailed geometry and realistic appearance from a single RGB image is essential yet fundamentally challenging due to inherent ambiguities such as occlusion, lighting variations, and…ReinSeg: Retrieval-Guided Bounded Multimodal Refinement for 3-D Road Anomaly Segmentation
3-D road anomaly segmentation is essential for risk-aware perception in autonomous driving.Computer Vision Applications for Art History: Reflections and paradigms for future research
One of the contributing factors to the continuing debate among art historians over the use of computational methods in art history research is that they do not consider the core of today's art…Probe-Guided Gradient Balancing for Multimodal Learning
Jointly trained multimodal networks frequently underutilize weaker modalities, as faster-learning modalities dominate the shared fusion objective and suppress gradients reaching slower ones.VerdictAD: Few-Shot Anomaly Detection via Decision-Guided Refinement
Detecting defects in real-world industrial inspection often requires learning from only a few normal images per category.OctreeSplat: Hierarchical Octree-Guided Feed-Forward Gaussian Splatting
3D Gaussian Splatting (3DGS) has shown considerable potential for novel view synthesis (NVS) and 3D reconstruction.Parametric CAD Bench v3: Evaluating Agentic Parametric CAD Authoring from Text and Engineering Drawings
Producing an executable CAD script is not sufficient to establish that an agent has satisfied an engineering design request.PriCBMs: Multi-granular Concept Bottleneck Vision-Language Models for Visual Privacy Recognition
Widespread image sharing on social media has increased the need for visual privacy recognition to identify inadvertently exposed privacy information.TME-ContextNet: Class-Conditioned Spatial Supervision and Grouped Decoding for Joint Nuclei Instance Segmentation and Classification
Joint nuclei instance segmentation and classification requires precise boundaries and neighborhood information that helps distinguish morphologically similar cell types.Geometry-Constrained Dynamic Hypergraph Convolutional Network with Contrastive Score Refinement for Skeleton-Based Action Recognition
Skeleton-based action recognition is a pivotal task in computer vision; however, conventional Graph Convolutional Networks (GCNs) are often constrained by pairwise joint modeling, which fails to…Discovering Semantic Granularity in Images through Clustering of Pretrained Visual Representations
Semantic granularity, which refers to the levels at which visual representations organize image content into meaningful entities, is fundamental to image understanding.Human-Oriented World Model
Supporting a person over repeated interactions requires interpreting their current state in context and anticipating its subsequent evolution.3L-D: A Low-Light Level Dataset from a Sensor-Agnostic, Alignment-Preserving Acquisition Framework
Supervised low-light image and video enhancement has achieved remarkable progress, yet current models remain fundamentally constrained by the limitations of existing datasets.Attention Residual U-Net for Enhanced Cross-Dataset Polyp Localization in Endoscopy
Precise localization of pathologies in endoscopy is a critical task in medical imaging, with deep learning (DL) frameworks now serving as the primary methodology for automated lesion detection.MoviEdit: Long-form Multi-Shot Video Editing Made Easy
Long-form multi-shot video editing requires an intended edit to remain coherent across abrupt changes in scene, viewpoint, and subject appearance, while processing substantially longer visual…Subspace Representations for Synthetic-to-Real Transfer in 3-D Surface Inspection
This paper studies what subspace representations contribute to synthetic-to-real transfer in 3-D surface inspection, where models learn from synthetic defects and the only real data before…