158 citations · 646 across the 34 of their papers we have counts for
8 papers · 1 filter
Improving Deep Metric Learning by Divide and Conquer
Artsiom Sanakoyeu, Pingchuan Ma, Vadim Tschernezki +1
Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically simi…
Sim2Real for Soft Robotic Fish via Differentiable Simulation
John Z. Zhang, Yu Zhang, Pingchuan Ma +5
Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots. We address this gap with our differentiable simulation tool by learning th…
LiRA: Learning Visual Speech Representations from Audio through Self-supervision
Pingchuan Ma, Rodrigo Mira, Stavros Petridis +2
The large amount of audiovisual content being shared online today has drawn substantial attention to the prospect of audiovisual self-supervised learning. Recent works have focused…
DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation
Pingchuan Ma, Tao Du, John Z. Zhang +4
The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipelin…
End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks
Rodrigo Mira, Konstantinos Vougioukas, Pingchuan Ma +3
Video-to-speech is the process of reconstructing the audio speech from a video of a spoken utterance. Previous approaches to this task have relied on a two-step process where an in…
End-to-end Audio-visual Speech Recognition with Conformers
Pingchuan Ma, Stavros Petridis, Maja Pantic
In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and Convolution-augmented transformer (Conformer), that can be trained in an end-to-end manner. In partic…