Search Results for author:
Found 14 papers, 6 papers with code
Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels
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.
Watch out Venomous Snake Species: A Solution to SnakeCLEF2023
The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata.
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition
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 via Prototype-based Classifier Learning from Single-Product Exemplars
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).
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
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.
Dual Attention Networks for Few-Shot Fine-Grained Recognition
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.
Monotonic Neural Network: combining Deep Learning with Domain Knowledge for Chiller Plants Energy Optimization
In this paper, we are interested in building a domain knowledge based deep learning framework to solve the chiller plants energy optimization problems.
Zero-Shot Instance Segmentation
We follow this motivation and propose a new task set named zero-shot instance segmentation (ZSI).
Deep Learning in the Era of Edge Computing: Challenges and Opportunities
The era of edge computing has arrived.
AinnoSeg: Panoramic Segmentation with High Perfomance
Panoramic segmentation is a scene where image segmentation tasks is more difficult.
Regression via Arbitrary Quantile Modeling
In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases.
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking
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.
Accurate Face Detection for High Performance
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)
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution
Recently, deep neural networks have achieved impressive performance in terms of both reconstruction accuracy and efficiency for single image super-resolution (SISR).
