activity
20142023
most citedAssociative Embedding: End-to-End Learning for Joint Detection and Grouping

553 citations · 3.5k across the 54 of their papers we have counts for

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Showing 2019Show all

11 papers · 1 filter

cs.CV2019★ 230 cited

Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy

Kaiyu Yang, Klint Qinami, Li Fei-Fei +2

Computer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive predictio…

cs.CV2019★ 2 cited

Compositional Temporal Visual Grounding of Natural Language Event Descriptions

Jonathan C. Stroud, Ryan McCaffrey, Rada Mihalcea +2

Temporal grounding entails establishing a correspondence between natural language event descriptions and their visual depictions. Compositional modeling becomes central: we first g…

cs.CV2019

Generative Modeling for Small-Data Object Detection

Lanlan Liu, Michael Muelly, Jia Deng +2

This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This…

cs.CV2019★ 4 cited

SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition

Kaiyu Yang, Olga Russakovsky, Jia Deng

Understanding the spatial relations between objects in images is a surprisingly challenging task. A chair may be "behind" a person even if it appears to the left of the person in t…

cs.LG2019★ 11 cited

Feature Partitioning for Efficient Multi-Task Architectures

Alejandro Newell, Lu Jiang, Chong Wang +2

Multi-task learning holds the promise of less data, parameters, and time than training of separate models. We propose a method to automatically search over multi-task architectures…

cs.CV2019★ 7 cited

Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control

Yu-Wei Chao, Jimei Yang, Weifeng Chen +1

Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement lear…