Deep LAC: Deep Localization, Alignment and Classification for Fine-Grained Recognition
We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function(VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system. The VLF can adaptively compromise the errors of classification and alignment when training the LAC model. It in turn helps update localization. The performance on fine-grained object data bears out the effectiveness of our LAC system.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Oriented Feature Alignment for Fine-grained Object Recognition in High-Resolution Satellite Imagery
Oriented object detection in remote sensing images has made great progress in recent years. However, most of the current methods only focus on detecting targets, and cannot distinguish fine-grained objects well in comple…
Objectobject-detectionObject DetectionObject Localization+2Fine-grained visual recognition with salient feature detection
Computer vision based fine-grained recognition has received great attention in recent years. Existing works focus on discriminative part localization and feature learning. In this paper, to improve the performance of fin…
ClassificationFine-Grained Visual RecognitionGeneral ClassificationObjectExploring Localization for Self-supervised Fine-grained Contrastive Learning
Self-supervised contrastive learning has demonstrated great potential in learning visual representations. Despite their success in various downstream tasks such as image classification and object detection, self-supervis…
Contrastive LearningFine-Grained Image ClassificationFine-Grained Image Recognitionimage-classification+2Weakly-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+1Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning
The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision. Inspired by this learning process, …
General ClassificationMeta-Learning