115 citations · 144 across the 6 of their papers we have counts for
9 papers
Efficient Video Segmentation Models with Per-frame Inference
Yifan Liu, Chunhua Shen, Changqian Yu +1
Most existing real-time deep models trained with each frame independently may produce inconsistent results across the temporal axis when tested on a video sequence. A few methods t…
Representative Graph Neural Network
Changqian Yu, Yifan Liu, Changxin Gao +2
Non-local operation is widely explored to model the long-range dependencies. However, the redundant computation in this operation leads to a prohibitive complexity. In this paper,…
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
Changqian Yu, Changxin Gao, Jingbo Wang +3
The low-level details and high-level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sacr…
Context Prior for Scene Segmentation
Changqian Yu, Jingbo Wang, Changxin Gao +3
Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. However, most approaches rarely distinguish different types of contextu…
Efficient Semantic Video Segmentation with Per-frame Inference
Yifan Liu, Chunhua Shen, Changqian Yu +1
For semantic segmentation, most existing real-time deep models trained with each frame independently may produce inconsistent results for a video sequence. Advanced methods take in…
GTNet: Generative Transfer Network for Zero-Shot Object Detection
Shizhen Zhao, Changxin Gao, Yuanjie Shao +4
We propose a Generative Transfer Network (GTNet) for zero shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Dete…