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 143
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentially.Surface Reconstruction from Range Scans Under Visibility Constraints
Surface reconstruction from range scans is challenging in the presence of noise and outliers, as fidelity to observations must be balanced against sensitivity to measurement errors.Dynamic Interpolation for Training-Time Prediction Reshaping in Low-Light Image Enhancement
Low-light image enhancement (LLIE) is an important image/video processing problem for reliable visual systems under poor illumination, yet it remains challenging due to the strong coupling among…FlowSim: Rendering-Free Closed-Loop Simulation for Evaluating End-to-End Driving Systems
Closed-loop evaluation of end-to-end (E2E) driving systems typically relies on generating sensor observations and re-processing them through the perception encoder, introducing substantial…COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection
Generalizable AI-generated image (AIGI) and deepfake detection remain challenging due to the asymmetry phenomenon, where adaptation to limited training distributions can degrade the transferable…Data-Free Continual Learning for World Action Model with Rectified Generative Replay
World action models (WAMs) built on pretrained video generation models adapt quickly to new robotic tasks, but they forget previously learned tasks under sequential fine-tuning.Scaling Vision Transformers Without Training: Prune-and-Merge Token Compression
Vision Transformers achieve strong performance but incur high computational costs from quadratic token interactions.LensErase: A Foundation Model for Erase Anything
We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios.APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation
Post-training is crucial for improving text-to-image models, yet existing pipelines typically rely on manually curated training data and fixed training distributions.SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction
Multimodal medical data captures complementary aspects of disease.H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression
Training-free diffusion-based compression methods repurpose the generative priors of a diffusion or a flow model to compress data without retraining them as task-specific codecs.Human-Inspired Asymmetric Collaboration for Dual-Student Semi-Supervised Medical Image Segmentation
Dual-student learning enriches predictive diversity in SSMIS by introducing a strongly augmented view alongside a weakly augmented view.FlowCred: Hierarchical Credit Assignment and Functional Coordination for Joint Text-to-Video Post-Training
Prompt optimization and generator post-training are two complementary ways to improve text-to-video (T2V) generation, and recent systems increasingly train both modules jointly.Benchmarking Multimodal Large Language Models for Zero-Shot Human-Object Interaction
Multimodal Large Language Models (MLLMs) have demonstrated strong zero-shot reasoning across diverse domains, but their potential for Human-Object Interaction (HOI) remains unexplored.VARI: Robust Text-to-Video Generation via Group-Structured Intent Alignment
Modern text-to-video (T2V) models generate visually compelling videos, yet meaning-preserving changes in prompt wording can substantially alter semantic realization and overall quality.Closing the Estimation Gap: Training-Free Transductive Inference for Cross-Domain Few-Shot Action Recognition
Cross-domain few-shot action recognition (CD-FSAR) aims to recognise unseen action classes in a target domain from a handful of labelled videos.A Controlled Evaluation of Curvature Inspired Scalar Feature Scaling for Domain Generalization
Domain-specific curvature is an appealing idea for domain generalization (DG), but curvature learning can denote substantially different computations.Heading Hierarchy Reconstruction for Complex and Long Documents with Level-Wise Multimodal Memory
Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract page-level elements.LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT
Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunistic screening because of its low cost and…FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection
Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora.