Fine-Grained Visual Classification with Efficient End-to-end Localization
The term fine-grained visual classification (FGVC) refers to classification tasks where the classes are very similar and the classification model needs to be able to find subtle differences to make the correct prediction. State-of-the-art approaches often include a localization step designed to help a classification network by localizing the relevant parts of the input images. However, this usually requires multiple iterations or passes through a full classification network or complex training schedules. In this work we present an efficient localization module that can be fused with a classification network in an end-to-end setup. On the one hand the module is trained by the gradient flowing back from the classification network. On the other hand, two self-supervised loss functions are introduced to increase the localization accuracy. We evaluate the new model on the three benchmark datasets CUB200-2011, Stanford Cars and FGVC-Aircraft and are able to achieve competitive recognition performance.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationFine-Grained Image ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization
The field of visual and audio generation is burgeoning with new state-of-the-art methods. This rapid proliferation of new techniques underscores the need for robust solutions for detecting synthetic content in videos. In…
Video ClassificationAudio GenerationWeakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning
Few-shot learning (FSL) aims to learn novel visual categories from very few samples, which is a challenging problem in real-world applications. Many methods of few-shot classification work well on general images to learn…
ClassificationFew-Shot LearningGeneral ClassificationObject Localization+1Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization
Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, …
AttributeImage Forgery DetectionRepresentation LearningPairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity. While prior work has addressed intra-class variation using localizat…
ClassificationFine-Grained Image ClassificationGeneral ClassificationELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding
The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird species or car models. State-of-the-art app…
Fine-Grained Image ClassificationGeneral Classification