Fine-grained object classification (FGOC) is a challenging research topic in multimedia computing with machine learning 1–5, which aims to distinguish

Relying on massive annotated datasets, significant progress has been made on many visual recognition tasks, which is mainly due to the widespread use

https://arxiv.org/abs/2112.14478일반화 제로샷 학습을 위한 의미론적 특징 추출Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to i

Inspired by non-local means operation 69 which was mainly designed for image denoising, Wang et al. 70 proposed a differentiable non-local operation f

For a given entity in the sequence, the self-attention basically computes the dot-product of the query with all keys, which is then normalized using s

Transformer models 1 have recently demonstrated exemplary performance on a broad range of language tasks e.g., text classification, machine translatio

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision

Bilinear pooling 10 is proposed to obtain rich and orderless global representation for the last convolutional feature, which achieved the state-of-the

The problem of learning from limited data has been approached from various directions. First and foremost, there is a huge body of work in the field o

코사인 손실을 사용하여 사전 훈련 없이 작은 데이터 세트에 대한 딥 러닝Two things seem to be indisputable in the contemporary deep learning discourse: 1. The categorical cross-entro

Among the tasks of computer vision, instance segmentation is one of the most challenge ones which requires understanding and perceiving the scene in u

Attribute Prototype Network for Zero-Shot Learning Abstract From the beginning of zero-shot learning research, visual attributes have been shown to

Remote Sensing Image Change Detection With Transformers변압기를 사용한 원격 감지 이미지 변경 감지Modern change detection (CD) has achieved remarkable success by the pow

TransGeo: 횡단면 이미지 지리적 위치 파악에 필요한 것은 변압기뿐입니다.The dominant CNN-based methods for cross-view image geo-localization rely on polar transform and fail to m

Since its introduction, the Transformer 48 has had a huge impact on natural language processing (NLP) 4, 13, 39. Likewise, the advent of Vision Transf

MPViT : Multi-Path Vision Transformer for Dense PredictionMPViT : 조밀한 예측을 위한 다중 경로 비전 변압기깃허브 : https://github.com/youngwanLEE/MPViTDense computer

Illustration of different self-attention mechanisms, our CSWin is fundamentally different from two aspects. First, we split multi-heads ({h 1 , . . .

논문링크 : https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_CSWin_Transformer_A_General_Vision_Transformer_Backbone_With_Cross-Shaped_Windo

In computing self-attention, we follow 45, 1, 29, 30 by including a relative position bias B ∈ RM2 × M2 to each head in computing similarity:자기 주의를 계산

A survey on semi-supervised learningSemi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled dat