Search Results for author:
Found 8 papers, 3 papers with code
MovePose: A High-performance Human Pose Estimation Algorithm on Mobile and Edge Devices
It aims to maintain real-time performance while improving the accuracy of human posture estimation for mobile devices.
OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation
This paper presents OmniDataComposer, an innovative approach for multimodal data fusion and unlimited data generation with an intent to refine and uncomplicate interplay among diverse data modalities.
Ranked #10 on
Zero-Shot Video Question Answer
on MSRVTT-QA
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Joint Coordinate Regression and Association For Multi-Person Pose Estimation, A Pure Neural Network Approach
We introduce a novel one-stage end-to-end multi-person 2D pose estimation algorithm, known as Joint Coordinate Regression and Association (JCRA), that produces human pose joints and associations without requiring any post-processing.
Parameter-Free Channel Attention for Image Classification and Super-Resolution
The channel attention mechanism is a useful technique widely employed in deep convolutional neural networks to boost the performance for image processing tasks, eg, image classification and image super-resolution.
Non-negative Sparse and Collaborative Representation for Pattern Classification
In this paper, we propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification.
Semi-Supervised Self-Growing Generative Adversarial Networks for Image Recognition
There are two main problems in label inference: how to measure the confidence of the unlabeled data and how to generalize the classifier.
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization.
A PID Controller Approach for Stochastic Optimization of Deep Networks
We first reveal the intrinsic connections between SGD-Momentum and PID based controller, then present the optimization algorithm which exploits the past, current, and change of gradients to update the network parameters.
