activity
20142026
most citedYOLOv3: An Incremental Improvement

5.8k citations · 6.6k across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV2026

OlmoEarth v1.2: A more efficient family of OlmoEarth models

Gabriel Tseng, Yawen Zhang, Favyen Bastani +8

We present a set of improvements to the OlmoEarth family. These improvements allow us to cut compute costs during training ( reduction in GPU hours required to train ou…

cs.CV2025★ 1 cited

OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation

Henry Herzog, Favyen Bastani, Yawen Zhang +23

Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temp…

cs.LG2025

High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data

Patrick Alan Johnson, Gabriel Tseng, Yawen Zhang +5

Wildfires are increasing in intensity and severity at an alarming rate. Recent advances in AI and publicly available satellite data enable monitoring critical wildfire risk factors…

cs.CV2018★ 5.8k cited

YOLOv3: An Incremental Improvement

Joseph Redmon, Ali Farhadi

We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last ti…

cs.CV2018

Who Let The Dogs Out? Modeling Dog Behavior From Visual Data

Kiana Ehsani, Hessam Bagherinezhad, Joseph Redmon +2

We introduce the task of directly modeling a visually intelligent agent. Computer vision typically focuses on solving various subtasks related to visual intelligence. We depart fro…

cs.CV2017

IQA: Visual Question Answering in Interactive Environments

Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari +3

We introduce Interactive Question Answering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual environment. IQA presents the…

cs.CV2016★ 438 cited

YOLO9000: Better, Faster, Stronger

Joseph Redmon, Ali Farhadi

We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detectio…

cs.CV2016★ 279 cited

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Mohammad Rastegari, Vicente Ordonez, Joseph Redmon +1

We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated…

cs.CV2015

You Only Look Once: Unified, Real-Time Object Detection

Joseph Redmon, Santosh Divvala, Ross Girshick +1

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression…

cs.RO2014★ 8 cited

Real-Time Grasp Detection Using Convolutional Neural Networks

Joseph Redmon, Anelia Angelova

We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding box…