Papers Fine-Grained Visual Categorization
“Fine-Grained Visual Categorization” 태그가 달린 논문 69편 · 필터 해제
Universal Fine-grained Visual Categorization by Concept Guided Learning
Existing fine-grained visual categorization (FGVC) methods assume that the fine-grained semantics rest in the informative parts of an image. This assumption works well on favorable front-view object-centric images, but c…
Fine-Grained Image ClassificationFine-Grained Visual CategorizationObjectobject-detection+2SIM-OFE: Structure Information Mining and Object-aware Feature Enhancement for Fine-Grained Visual Categorization
Fine-grained visual categorization (FGVC) aims to distinguish visual objects from multiple subcategories of the coarse-grained category. Subtle inter-class differences among various subcategories make the FGVC task more …
Fine-Grained Image ClassificationFine-Grained Visual CategorizationObjectEIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles
Source-free domain adaptation (SFDA) aims to transfer knowledge from a labelled source domain to an unlabelled target domain. A major challenge in SFDA is deriving accurate categorical information for the target domain, …
Domain AdaptationFine-Grained Visual CategorizationSource-Free Domain AdaptationFine-Grained Classification for Poisonous Fungi Identification with Transfer Learning
FungiCLEF 2024 addresses the fine-grained visual categorization (FGVC) of fungi species, with a focus on identifying poisonous species. This task is challenging due to the size and class imbalance of the dataset, subtle …
Fine-Grained Visual CategorizationTransfer LearningNovel Class Discovery for Ultra-Fine-Grained Visual Categorization
Ultra-fine-grained visual categorization (Ultra-FGVC) aims at distinguishing highly similar sub-categories within fine-grained objects, such as different soybean cultivars. Compared to traditional fine-grained visual cat…
Contrastive LearningFine-Grained Visual CategorizationNovel Class DiscoveryRepresentation Learning+1Data-free Knowledge Distillation for Fine-grained Visual Categorization
Data-free knowledge distillation (DFKD) is a promising approach for addressing issues related to model compression, security privacy, and transmission restrictions. Although the existing methods exploiting DFKD have achi…
Data-free Knowledge DistillationFine-Grained Visual CategorizationKnowledge DistillationModel CompressionContext-Semantic Quality Awareness Network for Fine-Grained Visual Categorization
Exploring and mining subtle yet distinctive features between sub-categories with similar appearances is crucial for fine-grained visual categorization (FGVC). However, less effort has been devoted to assessing the qualit…
Fine-Grained Image ClassificationFine-Grained Visual CategorizationFiner: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease. While such capability is largely attributed…
AttributeFine-Grained Visual CategorizationWorld KnowledgeViTree: Single-path Neural Tree for Step-wise Interpretable Fine-grained Visual Categorization
As computer vision continues to advance and finds widespread applications across various domains, the need for interpretability in deep learning models becomes paramount. Existing methods often resort to post-hoc techniq…
Decision MakingFine-Grained Visual CategorizationCross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization
High-quality annotation of fine-grained visual categories demands great expert knowledge, which is taxing and time consuming. Alternatively, learning fine-grained visual representation from enormous unlabeled images (e.g…
Fine-Grained Visual CategorizationKnowledge DistillationMultiple Instance LearningSelf-Supervised LearningFrom Coarse to Fine-Grained Open-Set Recognition
Open-set recognition (OSR) methods aim to identify whether or not a test example belongs to a category ob- served during training. Depending on how visually sim- ilar a test example is to the training categories the …
Fine-Grained Visual CategorizationOpen Set LearningLearning 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. However, this task is inheren…
Contrastive LearningFine-Grained Visual CategorizationDetail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions
The challenge in fine-grained visual categorization lies in how to explore the subtle differences between different subclasses and achieve accurate discrimination. Previous research has relied on large-scale annotated da…
Data AugmentationFine-Grained Visual CategorizationFine-Grained Visual RecognitionCoping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization
Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species. Previous studies always implicitly assume that the training and test data have the same underlyin…
Fine-Grained Visual CategorizationConcept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC). In the existing FGVC data…
Fine-Grained Visual CategorizationIncremental Generalized Category Discovery
We explore the problem of Incremental Generalized Category Discovery (IGCD). This is a challenging category incremental learning setting where the goal is to develop models that can correctly categorize images from previ…
Fine-Grained Visual CategorizationIncremental LearningCross-layer Attention Network for Fine-grained Visual Categorization
Learning discriminative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC). Compared to previous attention-based works, our work does not explicitly define …
Fine-Grained Visual CategorizationSIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization
Fine-grained visual categorization (FGVC) aims at recognizing objects from similar subordinate categories, which is challenging and practical for human's accurate automatic recognition needs. Most FGVC approaches focus o…
Contrastive LearningFine-Grained Image ClassificationFine-Grained Visual CategorizationRepresentation LearningExploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fine-grained visual categorization on image…
Fine-Grained Visual CategorizationVideo ClassificationViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder
Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image classification, can also visualize global interpretations through attention-based contributions. How- ever, the complexity of th…
Decision MakingDecoderFine-Grained Image ClassificationFine-Grained Visual Categorization+2