The Fine-Grained Image Classification task focuses on differentiating between hard-to-distinguish object classes, such as species of birds, flowers, or animals; and identifying the makes or models of vehicles.
( Image credit: Looking for the Devil in the Details )
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Our method of subspace attention is orthogonal and complementary to the existing state-of-the-arts attention mechanisms used in vision models.
We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter.
#2 best model for Fine-Grained Image Classification on Stanford Dogs
In this paper, we tried to focus on these marginal differences to extract more representative features.
At the same time, the architecture search and transfer is orders of magnitude more efficient than existing NAS methods.
It has been shown that using the first and second order statistics (e. g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance.
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
#11 best model for Fine-Grained Image Classification on Stanford Cars
The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity.
#3 best model for Metric Learning on CUB-200-2011
In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
SOTA for Image Classification on CIFAR-10
ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most authoritative academic competitions in the field of Computer Vision (CV) in recent years, but it can not achieve good result to directly migrate the champions of the annual competition, to fine-grained visual categorization (FGVC) tasks.
In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
#4 best model for Fine-Grained Image Classification on CUB-200-2011