TAVGBench: Benchmarking Text to Audible-Video Generation

1 code implementation • 22 Apr 2024 • Yuxin Mao, Xuyang Shen, Jing Zhang, Zhen Qin, Jinxing Zhou, Mochu Xiang, Yiran Zhong, Yuchao Dai

To support research in this field, we have developed a comprehensive Text to Audible-Video Generation Benchmark (TAVGBench), which contains over 1. 7 million clips with a total duration of 11. 8 thousand hours.

Multimodal Variational Auto-encoder based Audio-Visual Segmentation

1 code implementation • ICCV 2023 • Yuxin Mao, Jing Zhang, Mochu Xiang, Yiran Zhong, Yuchao Dai

To achieve this, our ECMVAE factorizes the representations of each modality with a modality-shared representation and a modality-specific representation.

Attribute Representation Learning

Contrastive Conditional Latent Diffusion for Audio-visual Segmentation

no code implementations • 31 Jul 2023 • Yuxin Mao, Jing Zhang, Mochu Xiang, Yunqiu Lv, Yiran Zhong, Yuchao Dai

We propose a latent diffusion model with contrastive learning for audio-visual segmentation (AVS) to extensively explore the contribution of audio.

Contrastive Learning Denoising +2

Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation

no code implementations • 19 Jul 2023 • Mochu Xiang, Jing Zhang, Nick Barnes, Yuchao Dai

Effectively measuring and modeling the reliability of a trained model is essential to the real-world deployment of monocular depth estimation (MDE) models.

Monocular Depth Estimation

The Second Monocular Depth Estimation Challenge

no code implementations • 14 Apr 2023 • Jaime Spencer, C. Stella Qian, Michaela Trescakova, Chris Russell, Simon Hadfield, Erich W. Graf, Wendy J. Adams, Andrew J. Schofield, James Elder, Richard Bowden, Ali Anwar, Hao Chen, Xiaozhi Chen, Kai Cheng, Yuchao Dai, Huynh Thai Hoa, Sadat Hossain, Jianmian Huang, Mohan Jing, Bo Li, Chao Li, Baojun Li, Zhiwen Liu, Stefano Mattoccia, Siegfried Mercelis, Myungwoo Nam, Matteo Poggi, Xiaohua Qi, Jiahui Ren, Yang Tang, Fabio Tosi, Linh Trinh, S. M. Nadim Uddin, Khan Muhammad Umair, Kaixuan Wang, YuFei Wang, Yixing Wang, Mochu Xiang, Guangkai Xu, Wei Yin, Jun Yu, Qi Zhang, Chaoqiang Zhao

This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).

Monocular Depth Estimation

Modeling the Distributional Uncertainty for Salient Object Detection Models

no code implementations • CVPR 2023 • Xinyu Tian, Jing Zhang, Mochu Xiang, Yuchao Dai

Most of the existing salient object detection (SOD) models focus on improving the overall model performance, without explicitly explaining the discrepancy between the training and testing distributions.

Long-tail Learning Object +3

Dense Uncertainty Estimation

1 code implementation • 13 Oct 2021 • Jing Zhang, Yuchao Dai, Mochu Xiang, Deng-Ping Fan, Peyman Moghadam, Mingyi He, Christian Walder, Kaihao Zhang, Mehrtash Harandi, Nick Barnes

Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks. The former is usually trained to achieve a mapping from input space to output space via maximum likelihood estimation for the weights, which leads to deterministic predictions during testing.

Decision Making

Exploring Depth Contribution for Camouflaged Object Detection

no code implementations • 24 Jun 2021 • Mochu Xiang, Jing Zhang, Yunqiu Lv, Aixuan Li, Yiran Zhong, Yuchao Dai

In this paper, we study the depth contribution for camouflaged object detection, where the depth maps are generated with existing monocular depth estimation (MDE) methods.

Generative Adversarial Network Monocular Depth Estimation +5