73 citations · 78 across the 7 of their papers we have counts for
7 papers
Low-Resource Vision Challenges for Foundation Models
Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale. However, low-resource problems are…
Learning Unseen Modality Interaction
Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. In this paper, we challenge this modality-comp…
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight
Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
This paper strives to recognize activities in the dark, as well as in the day. We first establish that state-of-the-art activity recognizers are effective during the day, but not t…
TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection
Chang Liu, Gang Yang, Shuo Wang +3
Existing RGB-D SOD methods mainly rely on a symmetric two-stream CNN-based network to extract RGB and depth channel features separately. However, there are two problems with the sy…
Audio-Adaptive Activity Recognition Across Video Domains
Yunhua Zhang, Hazel Doughty, Ling Shao +1
This paper strives for activity recognition under domain shift, for example caused by change of scenery or camera viewpoint. The leading approaches reduce the shift in activity app…
High-Performance Long-Term Tracking with Meta-Updater
Kenan Dai, Yunhua Zhang, Dong Wang +3
Long-term visual tracking has drawn increasing attention because it is much closer to practical applications than short-term tracking. Most top-ranked long-term trackers adopt the…
Learning regression and verification networks for long-term visual tracking
Yunhua Zhang, Dong Wang, Lijun Wang +2
Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if presen…