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20182022
most citedContext-Aware Zero-Shot Recognition

3 citations · 6 across the 4 of their papers we have counts for

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8 papers · 1 filter

cs.CV2022

Prior-enhanced Temporal Action Localization using Subject-aware Spatial Attention

Yifan Liu, Youbao Tang, Ning Zhang +2

Temporal action localization (TAL) aims to detect the boundary and identify the class of each action instance in a long untrimmed video. Current approaches treat video frames homog…

cs.CV20222 cited

FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning

Suvir Mirchandani, Licheng Yu, Mengjiao Wang +4

Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems - e.g., retrieving a fashion item gi…

cs.CV20221 cited

Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment

Mingyang Zhou, Licheng Yu, Amanpreet Singh +3

Vision-and-Language (V+L) pre-training models have achieved tremendous success in recent years on various multi-modal benchmarks. However, the majority of existing models require p…

cs.CV2021

Connecting What to Say With Where to Look by Modeling Human Attention Traces

Zihang Meng, Licheng Yu, Ning Zhang +4

We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework [30], w…

cs.CV2019

Dynamic Kernel Distillation for Efficient Pose Estimation in Videos

Xuecheng Nie, Yuncheng Li, Linjie Luo +2

Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and…

cs.CV2019

Weakly Supervised Body Part Segmentation with Pose based Part Priors

Zhengyuan Yang, Yuncheng Li, Linjie Yang +2

Human body part segmentation refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part segmentation methods achieve good perfor…