James Hong

Learning Subject-Aware Cropping by Outpainting Professional Photos

James Hong, Lu Yuan, Michaël Gharbi, Matthew Fisher, and Kayvon Fatahalian

AAAI Conference on Artificial Intelligence (AAAI) 2024

Project website / Paper / Code

TL;DR: We turn a stock photo database into a weakly-labeled dataset for learning what makes an aesthetically pleasing composition. Despite being only weakly-supervsied, our system outperforms supervised methods trained on large, crowd-annotated datasets.

Learning to Place Objects into Scenes by Hallucinating Scenes around Objects

Lu Yuan, James Hong, Vishnu Sarukkai, and Kayvon Fatahalian

SyntheticData4ML Workshop @ NeurIPS 2023

Project website / Paper

TL;DR: We synthesize weakly-labeled scenes around objects using foundation models for image generation. By synthesizing a large number of scenes, we train a model that can better understand where objects can and cannot go.

Spotting Temporally Precise, Fine-Grained Events in Video

James Hong, Haotian Zhang, Michaël Gharbi, Matthew Fisher, and Kayvon Fatahalian

European Conference on Computer Vision (ECCV) 2022

Project website / Paper / Code

TL;DR: We propose an efficient neural network for processing every frame of a video to detect very temporally fine-grained events at the granularity of a single frame.

Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition

James Hong, Matthew Fisher, Michaël Gharbi, and Kayvon Fatahalian

International Conference on Computer Vision (ICCV) 2021

Project website / Paper / Code

TL;DR: We generate large amounts of dense weak-supervision on unlabeled and untrimmed sports video in order to learn more robust features for recognizing fine-grained actions.

Analyzing the Faces in a Decade of US Cable TV News

James Hong, Will Crichton, Haotian Zhang, Daniel Y. Fu, Jacob Ritchie, Jeremy Barenholtz, Ben Hannel, Xinwei Yao, Michaela Murray, Geraldine Moriba, Maneesh Agrawala, and Kayvon Fatahalian

Conference on Knowledge Discovery and Data Mining (KDD) 2021

Project website / Paper / Code

TL;DR: We conduct an analysis of the visual content in 300,000 hours of US cable TV news. I built infrastructure to process the video and the TV news analyzer, a public interface and query engine for interactive search and visualization of all of the data.

Learning in situ: A Randomized Experiment in Video Streaming

Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein

Symposium on Networked Systems Design and Implementation (NSDI) 2020

Project website / Paper / Code

Awards: USENIX NSDI Community Award, IRTF Applied Networking Research Prize
TL;DR: A public research platform for conducting video streaming experimentation. I designed the JS/HTML web client, which streams video and audio chunks over a websocket connection.

Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels

Dan Fu, Will Crichton, James Hong, Xinwei Yao, Haotian Zhang, Anh Truong, Avanika Narayan, Maneesh Agrawala, Christopher Ré, and Kayvon Fatahalian

AI Systems Workshop @ SOSP 2019

Project website / Code

Securing the Internet of Things With Default-Off Networking

James Hong, Amit Levy, Laurynas Riliskis, and Philip Levis

Conference on Internet of Things Design and Implementation (IoTDI) 2018

Paper

Tethys: Collecting Sensor Data Without Infrastructure or Trust

Holly Chiang, James Hong, Kevin Kiningham, Laurynas Riliskis, Philip Levis, and Mark Horowitz

Conference on Internet of Things Design and Implementation (IoTDI) 2018

Paper

Beetle: Flexible Communication for Bluetooth Low Energy

Amit Levy, James Hong, Laurynas Riliskis, Philip Levis, and Keith Winstein

Conference on Mobile Systems, Applications, and Services (MobiSys) 2016

Paper

Ravel: Programming IoT Applications as Distributed Models, Views, and Controllers

Laurynas Riliskis, James Hong, and Philip Levis

IoT-App Workshop @ Sensys 2015

Paper