TY - GEN
T1 - Research Progress of Fine-grained Visual Classification
T2 - 3rd International Academic Exchange Conference on Science and Technology Innovation, IAECST 2021
AU - Ma, Congying
AU - Pu, Yuhan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2022/2/7
Y1 - 2022/2/7
N2 - With the progress of multimedia technology, fine-grained visual classification (FGVC) has gradually attracted extensive attention in the field of computer vision. Thanks to the success of deep neural networks, both the accuracy and speed have achieved an unprecedented breakthrough. Hundreds of fine-grained classification techniques have been developed from the early, fully supervised methods to the current weakly ones. To this end, in this article, we provide a comprehensive survey of the recent achievements of FGVC. Specifically, we describe the formulation and setting of the FGVC, including the background, basic framework, and challenges. Then, we introduce the widely used datasets and compare the performance of different models. Lastly, we discuss the potential future directions of FGVC.
AB - With the progress of multimedia technology, fine-grained visual classification (FGVC) has gradually attracted extensive attention in the field of computer vision. Thanks to the success of deep neural networks, both the accuracy and speed have achieved an unprecedented breakthrough. Hundreds of fine-grained classification techniques have been developed from the early, fully supervised methods to the current weakly ones. To this end, in this article, we provide a comprehensive survey of the recent achievements of FGVC. Specifically, we describe the formulation and setting of the FGVC, including the background, basic framework, and challenges. Then, we introduce the widely used datasets and compare the performance of different models. Lastly, we discuss the potential future directions of FGVC.
KW - deep learning
KW - Fine-grained visual classification
KW - weakly supervised learning
KW - zero-shot learning
UR - http://www.scopus.com/inward/record.url?scp=85126962953&partnerID=8YFLogxK
U2 - 10.1109/IAECST54258.2021.9695701
DO - 10.1109/IAECST54258.2021.9695701
M3 - Chapter in a published conference proceeding
AN - SCOPUS:85126962953
VL - 2021
T3 - 2021 3rd International Academic Exchange Conference on Science and Technology Innovation, IAECST 2021
SP - 413
EP - 419
BT - 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST)
PB - IEEE
CY - U. S. A.
Y2 - 10 December 2021 through 12 December 2021
ER -