activity
20192022
most citedGlobal Context Enhanced Graph Neural Networks for Session-based Recommendation

554 citations · 853 across the 19 of their papers we have counts for

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7 papers · 1 filter

cs.CV2022

Unsupervised Hashing with Semantic Concept Mining

Rong-Cheng Tu, Xian-Ling Mao, Kevin Qinghong Lin +5

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is bas…

cs.CV20225 cited

Declaration-based Prompt Tuning for Visual Question Answering

Yuhang Liu, Wei Wei, Daowan Peng +1

In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a…

cs.CV202115 cited

Context-aware Biaffine Localizing Network for Temporal Sentence Grounding

Daizong Liu, Xiaoye Qu, Jianfeng Dong +5

This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence quer…

cs.CV20202 cited

Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

Daizong Liu, Shuangjie Xu, Xiao-Yang Liu +3

This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these…

cs.CV2020

Crowd Counting via Hierarchical Scale Recalibration Network

Zhikang Zou, Yifan Liu, Shuangjie Xu +3

The task of crowd counting is extremely challenging due to complicated difficulties, especially the huge variation in vision scale. Previous works tend to adopt a naive concatenati…

cs.CV2019

Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation

Wei Wei, Ling Cheng, Xianling Mao +2

Recently, automatic image caption generation has been an important focus of the work on multimodal translation task. Existing approaches can be roughly categorized into two classes…