216 citations · 802 across the 61 of their papers we have counts for
7 papers · 1 filter
High-Quality Visually-Guided Sound Separation from Diverse Categories
Chao Huang, Susan Liang, Yapeng Tian +2
We propose DAVIS, a Diffusion-based Audio-VIsual Separation framework that solves the audio-visual sound source separation task through generative learning. Existing methods typica…
PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data
Zheng Zhang, Zheng Ning, Chenliang Xu +2
Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging the correlation between the auditory and visual modalities. Despite their many useful downst…
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA
Ali Vosoughi, Shijian Deng, Songyang Zhang +3
To increase the generalization capability of VQA systems, many recent studies have tried to de-bias spurious language or vision associations that shortcut the question or image to…
One Forward is Enough for Neural Network Training via Likelihood Ratio Method
Jinyang Jiang, Zeliang Zhang, Chenliang Xu +2
While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the de…
Egocentric Audio-Visual Object Localization
Chao Huang, Yapeng Tian, Anurag Kumar +1
Humans naturally perceive surrounding scenes by unifying sound and sight in a first-person view. Likewise, machines are advanced to approach human intelligence by learning with mul…
AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis
Susan Liang, Chao Huang, Yapeng Tian +2
Can machines recording an audio-visual scene produce realistic, matching audio-visual experiences at novel positions and novel view directions? We answer it by studying a new task…
Improving Adversarial Transferability with Scheduled Step Size and Dual Example
Zeliang Zhang, Peihan Liu, Xiaosen Wang +1
Deep neural networks are widely known to be vulnerable to adversarial examples, especially showing significantly poor performance on adversarial examples generated under the white-…