Top-down and bottom-up attentional multiple instance learning for still image action recognition

Author:

Bas CagdasORCID,Ikizler-Cinbis Nazli

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference62 articles.

1. Observing human-object interactions: Using spatial and functional compatibility for recognition;Gupta;IEEE Trans. Pattern Anal. Mach. Intell.,2009

2. Modeling mutual context of object and human pose in human-object interaction activities;Yao,2010

3. Discriminative models for static human-object interactions;Desai,2010

4. Learning person-object interactions for action recognition in still images;Delaitre,2011

5. On recognizing actions in still images via multiple features;Sener,2012

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. LCIF-Net: Local criss-cross attention based optical flow method using multi-scale image features and feature pyramid;Signal Processing: Image Communication;2023-03

2. Attention Transfer in Self-Regulated Networks for Recognizing Human Actions from Still Images;2022 12th International Conference on Computer and Knowledge Engineering (ICCKE);2022-11-17

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