STAM: A SpatioTemporal Attention Based Memory for Video Prediction

计算机科学 人工智能 代表(政治) 模式识别(心理学) 政治 政治学 法学
作者
Zheng Chang,Xinfeng Zhang,Shanshe Wang,Siwei Ma,Wen Gao
出处
期刊:IEEE Transactions on Multimedia [Institute of Electrical and Electronics Engineers]
卷期号:25: 2354-2367 被引量:12
标识
DOI:10.1109/tmm.2022.3146721
摘要

Video prediction has always been a very challenging problem in video representation learning due to the complexity in spatial structure and temporal variation. However, existing methods mainly predict videos by employing language-based memory structures from the traditional Long Short-Term Memories (LSTMs) or Gated Recurrent Units (GRUs), which may not be powerful enough to model the long-term dependencies in videos, consisting of much more complex spatiotemporal dynamics than sentences. In this paper, we propose a SpatioTemporal Attention based Memory (STAM), which can efficiently improve the long-term spatiotemporal memorizing capacity by incorporating the global spatiotemporal information in videos. In the temporal domain, the proposed STAM aims to observe temporal states from a wider temporal receptive field to capture accurate global motion information. In the spatial domain, the proposed STAM aims to jointly utilize both the high-level semantic spatial state and the low-level texture spatial states to model a more reliable global spatial representation for videos. In particular, the global spatiotemporal information is extracted with the help of an Efficient SpatioTemporal Attention Gate (ESTAG), which can adaptively apply different levels of attention scores to different spatiotemporal states according to their importance. Moreover, the proposed STAM are built with 3D convolutional layers due to their advantages in modeling spatiotemporal dynamics for videos. Experimental results show that the proposed STAM can achieve state-of-the-art performance on widely used datasets by leveraging the proposed spatiotemporal representations for videos.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
星辰大海应助mio采纳,获得10
1秒前
梦XING发布了新的文献求助10
1秒前
2秒前
量子星尘发布了新的文献求助10
2秒前
孙君健完成签到,获得积分10
3秒前
阳阳发布了新的文献求助10
3秒前
4秒前
yu完成签到,获得积分10
4秒前
Moshiqi发布了新的文献求助10
4秒前
orixero应助花开城北采纳,获得10
4秒前
欣欣发布了新的文献求助10
5秒前
SciGPT应助时遇采纳,获得10
5秒前
555557应助小李采纳,获得10
5秒前
小清新完成签到,获得积分10
6秒前
搜集达人应助樱悼柳雪采纳,获得10
6秒前
周周发布了新的文献求助10
6秒前
QQ完成签到,获得积分10
6秒前
碧蓝皮卡丘完成签到,获得积分10
7秒前
ylq发布了新的文献求助10
8秒前
人福药业完成签到,获得积分10
8秒前
dora完成签到,获得积分10
8秒前
Owen应助mia采纳,获得10
8秒前
天天快乐应助nkmenghan采纳,获得10
10秒前
乐乐应助CCCr采纳,获得10
10秒前
希望天下0贩的0应助wjx采纳,获得30
10秒前
上官若男应助周周采纳,获得10
11秒前
11秒前
11秒前
柔弱的马里奥完成签到,获得积分10
11秒前
爱听歌蘑菇完成签到,获得积分10
12秒前
12秒前
BL发布了新的文献求助10
14秒前
田様应助慕容松采纳,获得10
15秒前
李红玉发布了新的文献求助10
15秒前
panda发布了新的文献求助10
15秒前
金雪完成签到,获得积分10
15秒前
16秒前
CipherSage应助butter采纳,获得10
16秒前
17秒前
田様应助伶俐一曲采纳,获得10
17秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974856
求助须知:如何正确求助?哪些是违规求助? 3519400
关于积分的说明 11198085
捐赠科研通 3255563
什么是DOI,文献DOI怎么找? 1797860
邀请新用户注册赠送积分活动 877208
科研通“疑难数据库(出版商)”最低求助积分说明 806219