This page shows the most relevant public items for Computer Vision, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Blog • Mar 5, 2026 • Medium • Marcus Sterling
For the past year, web automation agents have relied heavily on parsing HTML DOM structures, making them notoriously brittle whenever a website updates its layout. The release of OpenClaw 3.0 this ...
Paper • Feb 28, 2026 • arXiv • Qiang Wang, Xinyuan Gao, SongLin Dong, Jizhou Han, Jiangyang Li, Yuhang He, Yihong Gong
Generating highly detailed, temporally accurate video captions requires models to understand complex spatial and temporal dynamics. VDC-Agent introduces an autonomous framework where the captioning...
Book • Dec 21, 2023 • Amazon • Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
Interactive learning is often the best way to master complex programming paradigms. This unique book offers an interactive experience with code and math integrated seamlessly. It covers modern conv...
Book • Feb 20, 2020 • Amazon • Joseph Howse, Joe Minichino
OpenCV remains the foundational library for performing high performance visual manipulation. This book guides developers through image processing and object tracking using Python efficiently. It br...
Book • Dec 5, 2023 • Amazon • Simon J.D. Prince
Grasping the true mechanics of deep learning requires a clear visualization of complex mathematical transformations. This highly visual textbook covers foundational neural networks and visual appli...
Book • Jan 31, 2024 • Amazon • Denis Rothman
Transformer architectures have evolved far beyond text generation to completely dominate visual tasks. This extensive guide explores how attention mechanisms are reshaping image processing and mult...
Book • Oct 13, 2020 • Amazon • Mohamed Elgendy
Understanding how a computer learns to see requires breaking down complex neural networks into intuitive concepts. This text teaches the tools necessary for building intelligent systems that identi...
Book • Mar 31, 2023 • Amazon • Lauren Mullennex, Nate Bachmeier, Jay Rao
Developing scalable visual solutions requires a robust cloud infrastructure to handle massive datasets and intensive compute loads. This book demonstrates how to build and deploy real world visual ...
Book • Aug 24, 2021 • Amazon • Valliappa Lakshmanan, Martin Görner, Ryan Gillard
Employing machine learning models to extract information from images can be daunting for software developers. This book provides intuitive explanations of visual architectures alongside practical c...
Book • Nov 27, 2020 • Amazon • V Kishore Ayyadevara, Yeshwanth Reddy
Deep learning is the driving force behind modern visual applications. This practical guide takes a code first approach to solving over fifty real world image problems using PyTorch. Readers will bu...
Book • Jan 3, 2022 • Amazon • Richard Szeliski
This foundational reference explores the vast variety of techniques used to analyze and interpret images. It takes a deeply scientific approach to formulating visual problems and solving them using...
Book • Apr 16, 2024 • Amazon • Antonio Torralba, Phillip Isola, William T. Freeman
Machine learning has revolutionized how machines perceive the world but modern methods have deep roots in classic techniques. This comprehensive textbook introduces the mathematical and algorithmic...
Paper • Jan 22, 2026 • arXiv • John Smith, Alice Chen, Wei Lin
Traditional autonomous web agents rely heavily on parsing underlying website code which often breaks during dynamic updates. We propose a purely visual framework that navigates user interfaces acro...
Paper • May 11, 2021 • arXiv • Prafulla Dhariwal, Alex Nichol
We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better archi...
Paper • May 22, 2017 • arXiv • Joao Carreira, Andrew Zisserman
Video action recognition is a crucial challenge in computer vision, but progress has been hindered by the lack of large-scale, comprehensive datasets comparable to ImageNet. We introduce the Kineti...
Paper • Nov 2, 2017 • arXiv • Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu
Learning useful representations without supervision remains a key challenge in machine learning. We propose the Vector Quantised-Variational AutoEncoder (VQ-VAE), a simple yet powerful generative m...
Paper • Feb 26, 2015 • Nature • Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Demis Hassabis
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural networ...
Paper • Feb 22, 2026 • arXiv • Tatsunori Hashimoto, Percy Liang, Xinyi Wang
Current web agents rely heavily on underlying HTML DOM structures, making them brittle to website updates and entirely incapable of navigating dynamic, canvas-based, or non-web applications. We pro...
Paper • Feb 8, 2026 • arXiv • Zhengqing Gao, Ziwen Li, Xin Wang, Tongliang Liu
To bridge the simulation-to-real gap, we introduce Mirage2Matter, a physically grounded Gaussian world model that generates high-fidelity embodied training data from multi-view videos. We reconstru...
Paper • Feb 9, 2026 • arXiv • Minhyuk Lee, Hyekyung Yoon, Myungjoo Kang
Text-to-Image (T2I) diffusion models enable high-quality synthesis, but deployment demands safeguards. We formalize the tension between safety and alignment through a total variation (TV) lens, yie...
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