380 citations · 1.1k across the 16 of their papers we have counts for
16 papers
An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022
Zhijian Hou, Wanjun Zhong, Lei Ji +6
This technical report describes the CONE approach for Ego4D Natural Language Queries (NLQ) Challenge in ECCV 2022. We leverage our model CONE, an efficient window-centric COarse-to…
Searching for Two-Stream Models in Multivariate Space for Video Recognition
Xinyu Gong, Heng Wang, Zheng Shou +3
Conventional video models rely on a single stream to capture the complex spatial-temporal features. Recent work on two-stream video models, such as SlowFast network and AssembleNet…
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
Junting Pan, Siyu Chen, Mike Zheng Shou +3
Localizing persons and recognizing their actions from videos is a challenging task towards high-level video understanding. Recent advances have been achieved by modeling direct pai…
SF-Net: Single-Frame Supervision for Temporal Action Localization
Fan Ma, Linchao Zhu, Yi Yang +4
In this paper, we study an intermediate form of supervision, i.e., single-frame supervision, for temporal action localization (TAL). To obtain the single-frame supervision, the ann…
Towards Train-Test Consistency for Semi-supervised Temporal Action Localization
Xudong Lin, Zheng Shou, Shih-Fu Chang
Recently, Weakly-supervised Temporal Action Localization (WTAL) has been densely studied but there is still a large gap between weakly-supervised models and fully-supervised models…
CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation
Jiawei Ma, Zheng Shou, Alireza Zareian +3
Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements. For example, air quality monit…
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition
Zheng Shou, Xudong Lin, Yannis Kalantidis +4
Motion has shown to be useful for video understanding, where motion is typically represented by optical flow. However, computing flow from video frames is very time-consuming. Rece…
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Hang Gao, Zheng Shou, Alireza Zareian +2
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been rec…
AutoLoc: Weakly-supervised Temporal Action Localization
Zheng Shou, Hang Gao, Lei Zhang +2
Temporal Action Localization (TAL) in untrimmed video is important for many applications. But it is very expensive to annotate the segment-level ground truth (action class and temp…
Online Detection of Action Start in Untrimmed, Streaming Videos
Zheng Shou, Junting Pan, Jonathan Chan +5
We aim to tackle a novel task in action detection - Online Detection of Action Start (ODAS) in untrimmed, streaming videos. The goal of ODAS is to detect the start of an action ins…
Single Shot Temporal Action Detection
Tianwei Lin, Xu Zhao, Zheng Shou
Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This pro…
ConvNet Architecture Search for Spatiotemporal Feature Learning
Du Tran, Jamie Ray, Zheng Shou +2
Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation,…
Temporal Convolution Based Action Proposal: Submission to ActivityNet 2017
Tianwei Lin, Xu Zhao, Zheng Shou
In this notebook paper, we describe our approach in the submission to the temporal action proposal (task 3) and temporal action localization (task 4) of ActivityNet Challenge hoste…
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos
Zheng Shou, Jonathan Chan, Alireza Zareian +2
Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need…
EventNet Version 1.1 Technical Report
Dongang Wang, Zheng Shou, Hongyi Liu +1
EventNet is a large-scale video corpus and event ontology consisting of 500 events associated with event-specific concepts. In order to improve the quality of the current EventNet,…
Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
Zheng Shou, Dongang Wang, Shih-Fu Chang
We address temporal action localization in untrimmed long videos. This is important because videos in real applications are usually unconstrained and contain multiple action instan…