Papers Fine-Grained Image Classification
“Fine-Grained Image Classification” 태그가 달린 논문 371편 · 필터 해제
Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification
Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critical yet underutilized role of phase information in capturing structural rel…
Fine-Grained Image ClassificationPool-Select-Refine for Allocation-Aware Generative Dataset Distillation
Diffusion-based dataset distillation has recently emerged as a promising paradigm for condensing large-scale datasets into compact synthetic sets. By leveraging pretrained generative priors, these methods can produce rea…
Fine-Grained Image ClassificationToolFG: Towards Well-Grounded Fine-Grained Image Classification
Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel paradigm for solving FGIC by proposing \textbf{ToolFG}, the first tool-i…
Fine-Grained Image ClassificationKnowledge DistillationAdaptive receptive field-based spatial-frequency feature reconstruction network for few-shot fine-grained image classification
Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature-based FSFGIC methods is how to …
Fine-Grained Image ClassificationFrequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-based methods typically rely solely on spa…
Fine-Grained Image ClassificationFine-Grained Visual RecognitionComputational EfficiencyMetric LearningLive Interactive Training for Video Segmentation
Interactive video segmentation often requires many user interventions for robust performance in challenging scenarios (e.g., occlusions, object separations, camouflage, etc.). Yet, even state-of-the-art models like SAM2 …
Fine-Grained Image ClassificationVideo SegmentationSpecificity-aware reinforcement learning for fine-grained open-world classification
Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and specific. Recent reasoning Large Multimodal Models (LMMs) exhibit strong vi…
Fine-Grained Image ClassificationReinforcement LearningFine-Grained Cat Breed Recognition with Global Context Vision Transformer
Accurate identification of cat breeds from images is a challenging task due to subtle differences in fur patterns, facial structure, and color. In this paper, we present a deep learning-based approach for classifying cat…
Fine-Grained Image ClassificationData AugmentationProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification
Prototypical parts-based models offer a "this looks like that" paradigm for intrinsic interpretability, yet they typically struggle with ImageNet-scale generalization and often require computationally expensive backbone …
Fine-Grained Image ClassificationDiffusion Representations for Fine-Grained Image Classification: A Marine Plankton Case Study
Diffusion models have emerged as state-of-the-art generative methods for image synthesis, yet their potential as general-purpose feature encoders remains underexplored. Trained for denoising and generation without labels…
Fine-Grained Image ClassificationD3G: Diverse Demographic Data Generation Increases Zero-Shot Image Classification Accuracy within Multimodal Models
Image classification is a task essential for machine perception to achieve human-level image understanding. Multimodal models such as CLIP have been able to perform well on this task by learning semantic similarities acr…
Fine-Grained Image ClassificationZero-Shot Image ClassificationFormal Abductive Latent Explanations for Prototype-Based Networks
Case-based reasoning networks are machine-learning models that make predictions based on similarity between the input and prototypical parts of training samples, called prototypes. Such models are able to explain each de…
Fine-Grained Image ClassificationLearning Time in Static Classifiers
Real-world visual data rarely presents as isolated, static instances. Instead, it often evolves gradually over time through variations in pose, lighting, object state, or scene context. However, conventional classifiers …
Fine-Grained Image ClassificationVideo Anomaly DetectionZero-Shot Fine-Grained Image Classification Using Large Vision-Language Models
Large Vision-Language Models (LVLMs) have demonstrated impressive performance on vision-language reasoning tasks. However, their potential for zero-shot fine-grained image classification, a challenging task requiring pre…
Fine-Grained Image ClassificationmicroCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
Unsupervised adaptation of CLIP-based vision-language models (VLMs) for fine-grained image classification requires sensitivity to microscopic local cues. While CLIP exhibits strong zero-shot transfer, its reliance on coa…
Fine-Grained Image ClassificationCombining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification
Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under limited annotation budgets. However, in fine-grained image classificatio…
Fine-Grained Image ClassificationActive LearningBeyond Frequency: Seeing Subtle Cues Through the Lens of Spatial Decomposition for Fine-Grained Visual Classification
The crux of resolving fine-grained visual classification (FGVC) lies in capturing discriminative and class-specific cues that correspond to subtle visual characteristics. Recently, frequency decomposition/transform based…
Fine-Grained Image ClassificationVocabulary-free Fine-grained Visual Recognition via Enriched Contextually Grounded Vision-Language Model
Fine-grained image classification, the task of distinguishing between visually similar subcategories within a broader category (e.g., bird species, car models, flower types), is a challenging computer vision problem. Tra…
Fine-Grained Image ClassificationFine-Grained Visual RecognitionHierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image Classification
Few-shot fine-grained image classification (FS-FGIC) presents a significant challenge, requiring models to distinguish visually similar subclasses with limited labeled examples. Existing methods have critical limitations…
Fine-Grained Image Classificationimage-classificationImage ClassificationStructural feature enhanced transformer for fine-grained image recognition
Existing fine-grained image recognition (FGIR) models mainly rely on high-level semantic features to extract discriminative information, ignoring the potential role of the overall structural information of objects and th…
Computational EfficiencyFine-Grained Image ClassificationFine-Grained Image Recognition