computer-vision-8

Relation Network

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Learning to Compare: Relation Network for Few-Shot Learning

T. Xiang, Timothy M. Hospedales, Flood Sung, Yongxin Yang, Li Zhang, Philip H. S. Torr•Wed Nov 15 2017

A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.

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AVA-AVD: Audio-visual Speaker Diarization in the Wild

Mang Ye, Eric Z. Xu, Zeyang Song, C. Feng, Mike Zheng Shou•Sun Nov 28 2021

The AVA Audio-Visual Relation Network (AVR-Net) is designed which introduces a simple yet effective modality mask to capture discriminative information based on face visibility and shows that the method not only can outperform state-of-the-art methods but is more robust as varying the ratio of off-screen speakers.

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Temporal Relational Reasoning in Videos

Bolei Zhou, A. Torralba, A. Andonian•Tue Nov 21 2017

This paper introduces an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales.

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Temporal Relational Ranking for Stock Prediction

Xiangnan He, Tat-Seng Chua, Fuli Feng, Yiqun Liu, Xiang Wang, Cheng Luo•Mon Sep 24 2018

A new deep learning solution, named Relational Stock Ranking (RSR), named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks and outperforms state-of-the-art stock prediction solutions.

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Local Relation Networks for Image Recognition

Han Hu, Zheng Zhang, Zhenda Xie, Stephen Lin•Wed Apr 24 2019

A network built with local relation layers, called the Local Relation Network (LR-Net), is found to provide greater modeling capacity than its counterpart built with regular convolution on large-scale recognition tasks such as ImageNet classification.

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Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization

Hongsheng Li, Jing Shao, Junting Pan, Siyu Chen, Zheng Shou•Sat Jun 13 2020

An Actor-Context-Actor Relation Network (ACAR-Net) is designed which builds upon a novel High-order Relation Reasoning Operator and an Actor- Context Feature Bank to enable indirect relation reasoning for spatio-temporal action localization.

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Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering

Xiang Ren, Bill Yuchen Lin, Jun Yan, Yanlin Feng, Xinyue Chen, Peifeng Wang•Thu Apr 30 2020

A novel knowledge-aware approach that equips pre-trained language models (PTLMs) with a multi-hop relational reasoning module, namedmulti-hop graph relation network (MHGRN), which performs multi-Hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs.

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1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020

Hongsheng Li, Jing Shao, Hao Shao, Junting Pan, Guanglu Song, Siyu Chen, Ziyi Lin, Yu Liu, Manyuan Zhang•Mon Jun 15 2020

This technical report introduces the winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020, based on Actor-Context-Actor Relation Network, which outperforms other entries by a large margin.

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Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

Yanfei Zhong, Zhuo Zheng, Junjue Wang, A. Ma•Sun May 31 2020

This paper argues that the problems lie on the lack of foreground modeling and proposes a foreground-aware relation network (FarSeg) from the perspectives of relation-based and optimization-based foreground modeling, to alleviate the above two problems.

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Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing

Xiao-Dan Zhu, Luo Si, Jian Sun, Pengfei Zhu, Binyuan Hui, Binhua Li, Ruiying Geng, Fei Huang, Yongbin Li, Qiyu Ren•Mon Jan 04 2021

This paper presents a dynamic graph framework that is capable of effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds, and employs a dynamic memory decay mechanism that incorporates inductive bias to integrate enriched contextual relation representation.

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