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Fine-Grained Image Classification
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Fine-Grained Image Classification is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.
<span style="color:grey; opacity: 0.6">( Image credit: Looking for the Devil in the Details )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image Classification
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions
DS_FusionNet: Dynamic Dual-Stream Fusion with Bidirectional Knowledge Distillation for Plant Disease Recognition
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization
Cross-Hierarchical Bidirectional Consistency Learning for Fine-Grained Visual Classification
Multi-scale Activation, Refinement, and Aggregation: Exploring Diverse Cues for Fine-Grained Bird Recognition
Adaptive Classification of Interval-Valued Time Series
Visual-RFT: Visual Reinforcement Fine-Tuning
An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual Classification
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention
Universal Fine-grained Visual Categorization by Concept Guided Learning
Enhancing Fine-grained Image Classification through Attentive Batch Training
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation
Vision Mamba Distillation for Low-resolution Fine-grained Image Classification
DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets
JPEG Inspired Deep Learning
Stochastic Subsampling With Average Pooling
SIM-OFE: Structure Information Mining and Object-aware Feature Enhancement for Fine-Grained Visual Categorization
PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning
Generating Binary Species Range Maps
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the Wild
Multi-Granularity Part Sampling Attention for Fine-Grained Visual Classification
Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning
Exploration of Class Center for Fine-Grained Visual Classification
PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers
Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification
Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes
Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
DenoiseRep: Denoising Model for Representation Learning
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classification
Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification
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