25 citations · 82 across the 7 of their papers we have counts for
7 papers
Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning
Aixuan Li, Jing Zhang, Yunqiu Lv +4
Salient objects attract human attention and usually stand out clearly from their surroundings. In contrast, camouflaged objects share similar colors or textures with the environmen…
Weakly-supervised Contrastive Learning for Unsupervised Object Discovery
Yunqiu Lv, Jing Zhang, Nick Barnes +1
Unsupervised object discovery (UOD) refers to the task of discriminating the whole region of objects from the background within a scene without relying on labeled datasets, which b…
Contrastive Conditional Latent Diffusion for Audio-visual Segmentation
Yuxin Mao, Jing Zhang, Mochu Xiang +4
We propose a contrastive conditional latent diffusion model for audio-visual segmentation (AVS) to thoroughly investigate the impact of audio, where the correlation between audio a…
Towards Deeper Understanding of Camouflaged Object Detection
Yunqiu Lv, Jing Zhang, Yuchao Dai +3
Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to surviv…
Exploring Depth Contribution for Camouflaged Object Detection
Mochu Xiang, Jing Zhang, Yunqiu Lv +3
Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and thei…
Uncertainty-aware Joint Salient Object and Camouflaged Object Detection
Aixuan Li, Jing Zhang, Yunqiu Lv +3
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 discov…
Generative Transformer for Accurate and Reliable Salient Object Detection
Yuxin Mao, Jing Zhang, Zhexiong Wan +6
Transformer, which originates from machine translation, is particularly powerful at modeling long-range dependencies. Currently, the transformer is making revolutionary progress in…