21 citations · 56 across the 11 of their papers we have counts for
11 papers
Chop & Learn: Recognizing and Generating Object-State Compositions
Nirat Saini, Hanyu Wang, Archana Swaminathan +4
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions. In this paper, we study the task of cutting o…
Towards Scalable Neural Representation for Diverse Videos
Bo He, Xitong Yang, Hanyu Wang +6
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e.g., NeRV, E-NeRV). W…
Align and Attend: Multimodal Summarization with Dual Contrastive Losses
Bo He, Jun Wang, Jielin Qiu +3
The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Unlike the unimodal summarization, the multimo…
CNeRV: Content-adaptive Neural Representation for Visual Data
Hao Chen, Matt Gwilliam, Bo He +2
Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have u…
Learning Semantic Correspondence with Sparse Annotations
Shuaiyi Huang, Luyu Yang, Bo He +3
Finding dense semantic correspondence is a fundamental problem in computer vision, which remains challenging in complex scenes due to background clutter, extreme intra-class variat…
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization
Bo He, Xitong Yang, Le Kang +3
Weakly-supervised temporal action localization aims to recognize and localize action segments in untrimmed videos given only video-level action labels for training. Without the bou…
NeRV: Neural Representations for Videos
Hao Chen, Bo He, Hanyu Wang +3
We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we rep…
Probabilistic fine-tuning of pruning masks and PAC-Bayes self-bounded learning
Soufiane Hayou, Bobby He, Gintare Karolina Dziugaite
We study an approach to learning pruning masks by optimizing the expected loss of stochastic pruning masks, i.e., masks which zero out each weight independently with some weight-sp…
Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection
Xin Zhou, Le Kang, Zhiyu Cheng +2
With rapidly evolving internet technologies and emerging tools, sports related videos generated online are increasing at an unprecedentedly fast pace. To automate sports video edit…
GTA: Global Temporal Attention for Video Action Understanding
Bo He, Xitong Yang, Zuxuan Wu +3
Self-attention learns pairwise interactions to model long-range dependencies, yielding great improvements for video action recognition. In this paper, we seek a deeper understandin…
Stable ResNet
Soufiane Hayou, Eugenio Clerico, Bobby He +3
Deep ResNet architectures have achieved state of the art performance on many tasks. While they solve the problem of gradient vanishing, they might suffer from gradient exploding as…