Fine-Grained Visual Recognition
35 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Fine-Grained Visual Recognition
Libraries
Use these libraries to find Fine-Grained Visual Recognition models and implementations
MTL-AQA
WHOI-Plankton
WebFG-496
FeathersV1
WikiChurches
Latest papers with no code
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition
In this paper, we present to incorporate the subtle local fine-grained feature learning into global self-supervised contrastive learning through a pure self-supervised global-local fine-grained contrastive learning framework.
Democratizing Fine-grained Visual Recognition with Large Language Models
Identifying subordinate-level categories from images is a longstanding task in computer vision and is referred to as fine-grained visual recognition (FGVR).
Generalized Category Discovery with Clustering Assignment Consistency
To address the GCD without knowing the class number of unlabeled dataset, we propose a co-training-based framework that encourages clustering consistency.
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions
To address this issue, we propose a novel approach termed the detail reinforcement diffusion model~(DRDM), which leverages the rich knowledge of large models for fine-grained data augmentation and comprises two key components including discriminative semantic recombination (DSR) and spatial knowledge reference~(SKR).
M2Former: Multi-Scale Patch Selection for Fine-Grained Visual Recognition
Therefore, we propose multi-scale patch selection (MSPS) to improve the multi-scale capabilities of existing ViT-based models.
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work
We hope that this survey can shed light on future research in the computer vision field.
Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition
Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision.
ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets
Extensive experimentation shows improvements in the SoTA FGVR benchmarks of up to +1. 3% of accuracy using both CNNs and transformer-based networks.
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition
One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes.
An attention-driven hierarchical multi-scale representation for visual recognition
Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content.
