activity
20142023
most citedInside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

63 citations · 130 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2023★ 4 cited

Learning and Verification of Task Structure in Instructional Videos

Medhini Narasimhan, Licheng Yu, Sean Bell +2

Given the enormous number of instructional videos available online, learning a diverse array of multi-step task models from videos is an appealing goal. We introduce a new pre-trai…

cs.LG2023★ 2 cited

RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data

Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma +4

Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised represen…

cs.CV2022

Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation

Tsu-Jui Fu, Licheng Yu, Ning Zhang +4

Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to…

cs.CV2017

Shading Annotations in the Wild

Balazs Kovacs, Sean Bell, Noah Snavely +1

Understanding shading effects in images is critical for a variety of vision and graphics problems, including intrinsic image decomposition, shadow removal, image relighting, and in…

cs.CV2015★ 63 cited

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Sean Bell, C. Lawrence Zitnick, Kavita Bala +1

It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object det…

cs.CV2015★ 41 cited

Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences

Andreas Veit, Balazs Kovacs, Sean Bell +3

With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to an…

cs.CV2014★ 20 cited

Material Recognition in the Wild with the Materials in Context Database

Sean Bell, Paul Upchurch, Noah Snavely +1

Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make…