no code implementations • 10 Jul 2023 • Aixuan Li, Jing Zhang, Yunqiu Lv, Tong Zhang, Yiran Zhong, Mingyi He, Yuchao Dai
In this case, salient objects are typically non-camouflaged, and camouflaged objects are usually not salient.
1 code implementation • 6 Jun 2023 • Aixuan Li, Yuxin Mao, Jing Zhang, Yuchao Dai
In particular, following the principle of disentangled representation learning, we introduce a mutual information upper bound with a mutual information minimization regularizer to encourage the disentangled representation of each modality for salient object detection.
1 code implementation • CVPR 2023 • Xuyang Shen, Dong Li, Jinxing Zhou, Zhen Qin, Bowen He, Xiaodong Han, Aixuan Li, Yuchao Dai, Lingpeng Kong, Meng Wang, Yu Qiao, Yiran Zhong
We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD).
1 code implementation • 23 May 2022 • Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Nick Barnes, Deng-Ping Fan
With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously localize, segment, and rank camouflaged objects, indicating the conspicuousness level of camouflage.
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
2 code implementations • 20 Apr 2021 • Yuxin Mao, Jing Zhang, Zhexiong Wan, Yuchao Dai, Aixuan Li, Yunqiu Lv, Xinyu Tian, Deng-Ping Fan, Nick Barnes
For the former, we apply transformer to a deterministic model, and explain that the effective structure modeling and global context modeling abilities lead to its superior performance compared with the CNN based frameworks.
2 code implementations • CVPR 2021 • Aixuan Li, Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, Yuchao Dai
Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding.
1 code implementation • CVPR 2021 • Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, Deng-Ping Fan
With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects.
1 code implementation • CVPR 2020 • Jing Zhang, Xin Yu, Aixuan Li, Peipei Song, Bowen Liu, Yuchao Dai
In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations.