Search Results for author: Zhengdong Zhang

Found 14 papers, 5 papers with code

Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming

no code implementations17 Dec 2023 Zhengdong Zhang, Zihan Dong, Yang Shi, Noboru Matsuda, Thomas Price, Dongkuan Xu

This study demonstrated that ChatGPT could generate Java programming assignment feedback that students perceived as formative.


Enhancing Bloodstain Analysis Through AI-Based Segmentation: Leveraging Segment Anything Model for Crime Scene Investigation

1 code implementation27 Aug 2023 Zihan Dong, Zhengdong Zhang

This paper explores the application of pre-trained SAM and fine-tuned SAM on bloodstain image segmentation with diverse image backgrounds.

Image Segmentation Segmentation +1

Conformer: Convolution-augmented Transformer for Speech Recognition

24 code implementations16 May 2020 Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, Ruoming Pang

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs).

Ranked #13 on Speech Recognition on LibriSpeech test-other (using extra training data)

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

6 code implementations7 May 2020 Wei Han, Zhengdong Zhang, Yu Zhang, Jiahui Yu, Chung-Cheng Chiu, James Qin, Anmol Gulati, Ruoming Pang, Yonghui Wu

We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2. 1%/4. 6% without external language model (LM), 1. 9%/4. 1% with LM and 2. 9%/7. 0% with only 10M parameters on the clean/noisy LibriSpeech test sets.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Streaming Object Detection for 3-D Point Clouds

no code implementations ECCV 2020 Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, Zhifeng Chen

This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to leverage camera-inspired architectures.

Action Recognition Autonomous Vehicles +4

A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images

no code implementations11 Feb 2020 Zhengdong Zhang, Shuai Li, Ziyang Wang, Yun Lu

Experimental results achieve competitive performance in detection with the AUC of 0. 9455, which outperforms other state-of-the-art methods to our best of knowledge, demonstrating the proposed framework can detect the tumor of pancreatic cancer efficiently and accurately.

Computed Tomography (CT)

Hardware for Machine Learning: Challenges and Opportunities

1 code implementation22 Dec 2016 Vivienne Sze, Yu-Hsin Chen, Joel Emer, Amr Suleiman, Zhengdong Zhang

Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day.

BIG-bench Machine Learning Self-Driving Cars

A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps

no code implementations27 Jul 2016 Amr Suleiman, Zhengdong Zhang, Vivienne Sze

This paper presents a programmable, energy-efficient and real-time object detection accelerator using deformable parts models (DPM), with 2x higher accuracy than traditional rigid body models.

Classification General Classification +3

FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos

no code implementations29 Mar 2016 Zhengdong Zhang, Vivienne Sze

State-of-the-art super-resolution (SR) algorithms require significant computational resources to achieve real-time throughput (e. g., 60Mpixels/s for HD video).


Sparkle Vision: Seeing the World through Random Specular Microfacets

no code implementations26 Dec 2014 Zhengdong Zhang, Phillip Isola, Edward H. Adelson

In this paper, we study the problem of reproducing the world lighting from a single image of an object covered with random specular microfacets on the surface.

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