activity
20162021
most citedCo-training Transformer with Videos and Images Improves Action Recognition

31 citations · 57 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CV202131 cited

Co-training Transformer with Videos and Images Improves Action Recognition

Bowen Zhang, Jiahui Yu, Christopher Fifty +4

In learning action recognition, models are typically pre-trained on object recognition with images, such as ImageNet, and later fine-tuned on target action recognition with videos.…

cs.CL20211 cited

Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?

Linlu Qiu, Hexiang Hu, Bowen Zhang +2

We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding. First, we study which aspects of…

cs.CV202020 cited

A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus

Bowen Zhang, Hexiang Hu, Joonseok Lee +5

Identifying a short segment in a long video that semantically matches a text query is a challenging task that has important application potentials in language-based video search, b…

cs.CV2020

Online Action Detection in Streaming Videos with Time Buffers

Bowen Zhang, Hao Chen, Meng Wang +1

We formulate the problem of online temporal action detection in live streaming videos, acknowledging one important property of live streaming videos that there is normally a broadc…

cs.CV2020

Learning to Represent Image and Text with Denotation Graph

Bowen Zhang, Hexiang Hu, Vihan Jain +2

Learning to fuse vision and language information and representing them is an important research problem with many applications. Recent progresses have leveraged the ideas of pre-tr…

cs.CV20205 cited

Visual Storytelling via Predicting Anchor Word Embeddings in the Stories

Bowen Zhang, Hexiang Hu, Fei Sha

We propose a learning model for the task of visual storytelling. The main idea is to predict anchor word embeddings from the images and use the embeddings and the image features jo…