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20152021
most citedLearning Fashion Compatibility with Bidirectional LSTMs

320 citations · 848 across the 30 of their papers we have counts for

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Showing 2020Show all

11 papers · 1 filter

cs.CV2020

2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition

Hengduo Li, Zuxuan Wu, Abhinav Shrivastava +1

3D convolutional networks are prevalent for video recognition. While achieving excellent recognition performance on standard benchmarks, they operate on a sequence of frames with 3…

cs.CV2020

The Lottery Ticket Hypothesis for Object Recognition

Sharath Girish, Shishira R. Maiya, Kamal Gupta +3

Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep network…

cs.CV202020 cited

All About Knowledge Graphs for Actions

Pallabi Ghosh, Nirat Saini, Larry S. Davis +1

Current action recognition systems require large amounts of training data for recognizing an action. Recent works have explored the paradigm of zero-shot and few-shot learning to l…

cs.CV20203 cited

ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors

Rohun Tripathi, Vasu Singla, Mahyar Najibi +3

The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines. Unfortunately, for the region proposal stage of…

cs.CV2020

A Generic Visualization Approach for Convolutional Neural Networks

Ahmed Taha, Xitong Yang, Abhinav Shrivastava +1

Retrieval networks are essential for searching and indexing. Compared to classification networks, attention visualization for retrieval networks is hardly studied. We formulate att…

cs.CV20202 cited

InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling

Jun Wang, Shiyi Lan, Mingfei Gao +1

Real-time 3D object detection is crucial for autonomous cars. Achieving promising performance with high efficiency, voxel-based approaches have received considerable attention. How…