Learning fine-grained embeddings from coarse labels is a challenging task due to limited label granularity supervision, i. e., lacking the detailed distinctions required for fine-grained tasks.
The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata.
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks.
Ranked #3 on Long-tail Learning on CIFAR-10-LT (ρ=10)
Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images).
Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data.
Specifically, by producing attention guidance from deep activations of input images, our hard-attention is realized by keeping a few useful deep descriptors and forming them as a bag of multi-instance learning.
In this paper, we are interested in building a domain knowledge based deep learning framework to solve the chiller plants energy optimization problems.
Panoramic segmentation is a scene where image segmentation tasks is more difficult.
In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases.
Without the constraint imposed by the hand-designed heuristics, our searched networks contain more flexible and meaningful architectures that existing weight sharing based NAS approaches are not able to discover.
Face detection has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs).
Ranked #5 on Face Detection on WIDER Face (Hard)
Recently, deep neural networks have achieved impressive performance in terms of both reconstruction accuracy and efficiency for single image super-resolution (SISR).