Ssfe-net: Self-supervised feature enhancement for ultra-fine-grained few-shot class incremental learning
Abstract Ultra-Fine-Grained Visual Categorization (ultra-FGVC) has become a popular
problem due to its great real-world potential for classifying the same or closely related …
problem due to its great real-world potential for classifying the same or closely related …
Learning Contrastive Self-Distillation for Ultra-Fine-Grained Visual Categorization Targeting Limited Samples
In the field of intelligent multimedia analysis, ultra-fine-grained visual categorization (Ultra-
FGVC) plays a vital role in distinguishing intricate subcategories within broader categories …
FGVC) plays a vital role in distinguishing intricate subcategories within broader categories …
Integrating foreground–background feature distillation and contrastive feature learning for ultra-fine-grained visual classification
Q Chen, L Jiao, F Wang, J Du, H Liu, X Wang… - Pattern Recognition, 2024 - Elsevier
In pattern recognition, ultra-fine-grained visual classification (ultra-FGVC) has emerged as a
paramount challenge, focusing on sub-category distinction within fine-grained objects. The …
paramount challenge, focusing on sub-category distinction within fine-grained objects. The …
Diving into Continual Ultra-fine-grained Visual Categorization
Recent advance in ultra-fine-grained visual categorization (ultra-FGVC) has significantly
boosted the capability of deep neural networks for ultra-FGVC tasks. However, building …
boosted the capability of deep neural networks for ultra-FGVC tasks. However, building …
What EXACTLY are We Looking at?: Investigating for Discriminance in Ultra-Fine-Grained Visual Categorization Tasks
Comparing discriminative features at different levels of granularity is an inherent part of the
human object recognition process and AI-based models mimic such behaviour with …
human object recognition process and AI-based models mimic such behaviour with …