553 citations · 3.5k across the 54 of their papers we have counts for
9 papers · 1 filter
D3D: Distilled 3D Networks for Video Action Recognition
Jonathan C. Stroud, David A. Ross, Chen Sun +2
State-of-the-art methods for video action recognition commonly use an ensemble of two networks: the spatial stream, which takes RGB frames as input, and the temporal stream, which…
DeepV2D: Video to Depth with Differentiable Structure from Motion
Zachary Teed, Jia Deng
We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric prin…
MeshAdv: Adversarial Meshes for Visual Recognition
Chaowei Xiao, Dawei Yang, Bo Li +2
Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial…
Speaker Naming in Movies
Mahmoud Azab, Mingzhe Wang, Max Smith +3
We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework. To evaluate the performance of our…
CornerNet: Detecting Objects as Paired Keypoints
Hei Law, Jia Deng
We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a sin…
Learning Single-Image Depth from Videos using Quality Assessment Networks
Weifeng Chen, Shengyi Qian, Jia Deng
Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we…