64 citations · 86 across the 8 of their papers we have counts for
8 papers
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN
Weiwen Zhang, Dawei Yang, Haoxuan Che +3
For optical coherence tomography angiography (OCTA) images, a limited scanning rate leads to a trade-off between field-of-view (FOV) and imaging resolution. Although larger FOV ima…
TryOnDiffusion: A Tale of Two UNets
Luyang Zhu, Dawei Yang, Tyler Zhu +5
Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge…
Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric
Lin Niu, Jiawei Liu, Zhihang Yuan +3
Efficient inference for object detection networks is a major challenge on edge devices. Post-Training Quantization (PTQ), which transforms a full-precision model into low bit-width…
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Zhihang Yuan, Jiawei Liu, Jiaxiang Wu +6
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures. Despite its e…
PD-Quant: Post-Training Quantization based on Prediction Difference Metric
Jiawei Liu, Lin Niu, Zhihang Yuan +3
Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it…
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
Ankit Goyal, Kaiyu Yang, Dawei Yang +1
Understanding spatial relations (e.g., "laptop on table") in visual input is important for both humans and robots. Existing datasets are insufficient as they lack large-scale, high…
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Yulong Cao, Chaowei Xiao, Dawei Yang +4
Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbi…
Learning to Generate Synthetic 3D Training Data through Hybrid Gradient
Dawei Yang, Jia Deng
Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train a network to perform…