This page shows the most relevant public items for cs.CV, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jan 8, 2026 • arXiv • Zhang et al., Chen et al.
Visual navigation in home environments often fails because textual planning cannot capture scene geometry. We propose ImagineNav++, which uses a VLM to 'imagine' future viewpoints from candidate ro...
Paper • Feb 13, 2026 • arXiv • Ruiteng Zhao, Wenshuo Wang, Marcelo H. Ang Jr., Haiyue Zhu
Current VLA models primarily rely on visual feedback, which is insufficient for contact-rich tasks like precision assembly or handling delicate objects. We introduce FD-VLA, a force-distilled frame...
Paper • Mar 20, 2017 • arXiv • Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel
Bridging the 'reality gap' between simulated environments and the physical world is a major challenge in robotics. We introduce domain randomization, a simple yet powerful technique for training ne...
Paper • Mar 10, 2020 • Distill • Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter
Neural networks are generally regarded as opaque black boxes. However, if we zoom in and carefully examine the weights and activations of convolutional neural networks, we find highly interpretable...
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 • Mar 4, 2021 • Distill • Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, Chris Olah
We investigate the internal representations of the CLIP model and discover the presence of 'multimodal neurons'. These neurons fire not only for specific visual features (like a spider) but also fo...
Paper • Oct 19, 2023 • OpenAI • James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, Wesam Manassra, Prafulla Dhariwal, Casey Chu, Yunxing Jiao, Aditya Ramesh
Current text-to-image models often struggle to faithfully follow detailed or complex prompts, frequently ignoring specific attributes or object relationships. We propose that this issue stems from ...
Paper • May 3, 2023 • arXiv • Heewoo Jun, Alex Nichol
We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of...
Paper • Dec 20, 2021 • arXiv • Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen
Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guiding technique to trade off diversity for fidelity. We explore diffusion models...
Paper • Feb 15, 2024 • OpenAI Technical Report • Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, Aditya Ramesh
We explore the large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of highly variable durations, resolutio...
Paper • Feb 18, 2021 • arXiv • Alex Nichol, Prafulla Dhariwal
Denoising diffusion probabilistic models (DDPMs) have recently demonstrated high-quality image generation, but they suffer from notoriously slow sampling times and sub-optimal log-likelihoods. We p...
Paper • Feb 24, 2021 • arXiv • Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. We describe a simple approach for this task based on a transformer that au...
Paper • Apr 13, 2022 • arXiv • Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a tw...
Paper • Feb 26, 2021 • arXiv • Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories, restricting their generality. We demonstrate that the simple pre-training task of pre...
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 • Jun 13, 2016 • arXiv • Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
Deep learning algorithms typically require vast amounts of data to achieve high performance, contrasting sharply with human ability to learn new concepts from a single example. We introduce Matchin...
Paper • Sep 28, 2018 • arXiv • Andrew Brock, Jeff Donahue, Karen Simonyan
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train ...
Paper • Mar 4, 2021 • arXiv • Andrew Jaegle, Felix Gimeno, Andrew Brock, Oriol Vinyals, Andrew Zisserman, Joao Carreira
Biological systems perceive the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, and touch. We introduce the Perceiver, an architecture tha...
Paper • Apr 28, 2022 • arXiv • Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Karen Simonyan
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family ...
Paper • Jun 17, 2020 • OpenAI • Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever
Inspired by the success of unsupervised representation learning in natural language processing with models like GPT-2, we examine whether similar models can learn useful representations for images....
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