亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Transformer-Based Approach Via Contrastive Learning for Zero-Shot Detection

计算机科学 人工智能 变压器 模式识别(心理学) 零(语言学) 工程类 电气工程 语言学 哲学 电压
作者
Wei Liu,Hui Chen,Yongqiang Ma,Jianji Wang,Nanning Zheng
出处
期刊:International Journal of Neural Systems [World Scientific]
卷期号:33 (07) 被引量:5
标识
DOI:10.1142/s0129065723500351
摘要

Zero-shot detection (ZSD) aims to locate and classify unseen objects in pictures or videos by semantic auxiliary information without additional training examples. Most of the existing ZSD methods are based on two-stage models, which achieve the detection of unseen classes by aligning object region proposals with semantic embeddings. However, these methods have several limitations, including poor region proposals for unseen classes, lack of consideration of semantic representations of unseen classes or their inter-class correlations, and domain bias towards seen classes, which can degrade overall performance. To address these issues, the Trans-ZSD framework is proposed, which is a transformer-based multi-scale contextual detection framework that explicitly exploits inter-class correlations between seen and unseen classes and optimizes feature distribution to learn discriminative features. Trans-ZSD is a single-stage approach that skips proposal generation and performs detection directly, allowing the encoding of long-term dependencies at multiple scales to learn contextual features while requiring fewer inductive biases. Trans-ZSD also introduces a foreground-background separation branch to alleviate the confusion of unseen classes and backgrounds, contrastive learning to learn inter-class uniqueness and reduce misclassification between similar classes, and explicit inter-class commonality learning to facilitate generalization between related classes. Trans-ZSD addresses the domain bias problem in end-to-end generalized zero-shot detection (GZSD) models by using balance loss to maximize response consistency between seen and unseen predictions, ensuring that the model does not bias towards seen classes. The Trans-ZSD framework is evaluated on the PASCAL VOC and MS COCO datasets, demonstrating significant improvements over existing ZSD models.
最长约 10秒,即可获得该文献文件

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
刚刚
1秒前
贪玩小小完成签到 ,获得积分10
4秒前
12秒前
学渣路过完成签到,获得积分0
15秒前
satisusu完成签到 ,获得积分10
18秒前
搜文献的北北完成签到,获得积分10
20秒前
好巧完成签到,获得积分10
21秒前
24秒前
糯米完成签到 ,获得积分10
26秒前
XING完成签到 ,获得积分10
31秒前
32秒前
32秒前
喂喂发布了新的文献求助10
38秒前
38秒前
一颗滚石发布了新的文献求助20
43秒前
学海行舟完成签到 ,获得积分10
48秒前
58秒前
59秒前
nicenice发布了新的文献求助10
1分钟前
咕噜噜完成签到,获得积分10
1分钟前
闫雪发布了新的文献求助10
1分钟前
1分钟前
1分钟前
1分钟前
Simpson完成签到 ,获得积分10
1分钟前
www发布了新的文献求助10
1分钟前
我是老大应助ceeray23采纳,获得20
1分钟前
李爱国应助zzzkyt采纳,获得10
1分钟前
lb001完成签到 ,获得积分10
1分钟前
zzjjyy完成签到,获得积分10
1分钟前
小刘发布了新的文献求助10
1分钟前
1分钟前
zzzkyt完成签到,获得积分10
1分钟前
万能图书馆应助www采纳,获得10
1分钟前
zzzkyt发布了新的文献求助10
1分钟前
Hyh_orz应助onlyan采纳,获得20
1分钟前
1分钟前
1分钟前
霜鸣完成签到,获得积分20
1分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3990012
求助须知:如何正确求助?哪些是违规求助? 3532047
关于积分的说明 11256141
捐赠科研通 3270918
什么是DOI,文献DOI怎么找? 1805105
邀请新用户注册赠送积分活动 882270
科研通“疑难数据库(出版商)”最低求助积分说明 809216