[HTML][HTML] A survey on few-shot class-incremental learning
Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …
Feature fusion vision transformer for fine-grained visual categorization
The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet
discriminative features. Most previous works achieve this by explicitly selecting the …
discriminative features. Most previous works achieve this by explicitly selecting the …
Joint discriminative representation learning for end-to-end person search
Person search simultaneously detects and retrieves a query person from uncropped scene
images. Existing methods are either two-step or end-to-end. The former employs two …
images. Existing methods are either two-step or end-to-end. The former employs two …
Mix-ViT: Mixing attentive vision transformer for ultra-fine-grained visual categorization
Ultra-fine-grained visual categorization (ultra-FGVC) moves down the taxonomy level to
classify sub-granularity categories of fine-grained objects. This inevitably poses a challenge …
classify sub-granularity categories of fine-grained objects. This inevitably poses a challenge …
Field detection of small pests through stochastic gradient descent with genetic algorithm
Pest invasion is one of the main reasons that affect crop yield and quality. Therefore,
accurate detection of pests is a key technology of smart agriculture. Pests often exist as …
accurate detection of pests is a key technology of smart agriculture. Pests often exist as …
SPARE: Self-supervised part erasing for ultra-fine-grained visual categorization
This paper presents SPARE, a self-supervised part erasing framework for ultra-fine-grained
visual categorization. The key insight of our model is to learn discriminative representations …
visual categorization. The key insight of our model is to learn discriminative representations …
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 …
[HTML][HTML] Cle-vit: Contrastive learning encoded transformer for ultra-fine-grained visual categorization
Ultra-fine-grained visual classification (ultra-FGVC) targets at classifying sub-grained
categories of fine-grained objects. This inevitably requires discriminative representation …
categories of fine-grained objects. This inevitably requires discriminative representation …
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 …