8 citations · 12 across the 6 of their papers we have counts for
6 papers
SnapCap: Efficient Snapshot Compressive Video Captioning
Jianqiao Sun, Yudi Su, Hao Zhang +5
Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. For machines, the traditiona…
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
Miaoge Li, Dongsheng Wang, Xinyang Liu +4
Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent spac…
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing
Zequn Zeng, Hao Zhang, Zhengjue Wang +3
Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing o…
Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing
Ruiying Lu, Ziheng Cheng, Bo Chen +1
Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture sequential video frames and compress them into a single measurement. Various reconstruction methods have…
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network
Ruiying Lu, Bo Chen, Guanliang Liu +3
Dual-view snapshot compressive imaging (SCI) aims to capture videos from two field-of-views (FoVs) using a 2D sensor (detector) in a single snapshot, achieving joint FoV and tempor…
Memory-Efficient Network for Large-scale Video Compressive Sensing
Ziheng Cheng, Bo Chen, Guanliang Liu +4
Video snapshot compressive imaging (SCI) captures a sequence of video frames in a single shot using a 2D detector. The underlying principle is that during one exposure time, differ…