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Found 6 papers, 4 papers with code
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion Similarity
Furthermore, in order to compute the motion similarity from these datasets, we propose a deep learning model that produces motion embeddings suitable for measuring the similarity between different motions of each human body part.
HintPose
Most of the top-down pose estimation models assume that there exists only one person in a bounding box.
ChoiceNet: Robust Learning by Revealing Output Correlations
To this end, we present a novel framework referred to here as ChoiceNet that can robustly infer the target distribution in the presence of inconsistent data.
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks
In this paper, we focus on weakly supervised learning with noisy training data for both classification and regression problems. We assume that the training outputs are collected from a mixture of a target and correlated noise distributions. Our proposed method simultaneously estimates the target distribution and the quality of each data which is defined as the correlation between the target and data generating distributions. The cornerstone of the proposed method is a Cholesky Block that enables modeling dependencies among mixture distributions in a differentiable manner where we maintain the distribution over the network weights. We first provide illustrative examples in both regression and classification tasks to show the effectiveness of the proposed method. Then, the proposed method is extensively evaluated in a number of experiments where we show that it constantly shows comparable or superior performances compared to existing baseline methods in the handling of noisy data.
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
In object detection, reducing computational cost is as important as improving accuracy for most practical usages.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recent technical innovations.
