124 citations · 367 across the 12 of their papers we have counts for
12 papers · 1 filter
FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding
Mingkun Wang, Xiaoguang Ren, Ruochun Jin +5
Most prior motion prediction endeavors in autonomous driving have inadequately encoded future scenarios, leading to predictions that may fail to accurately capture the diverse move…
GANet: Goal Area Network for Motion Forecasting
Mingkun Wang, Xinge Zhu, Changqian Yu +7
Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecas…
CORE: Consistent Representation Learning for Face Forgery Detection
Yunsheng Ni, Depu Meng, Changqian Yu +3
Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer f…
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…
An End-to-End Network for Panoptic Segmentation
Huanyu Liu, Chao Peng, Changqian Yu +4
Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic. Traditionally, the existing app…
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Changqian Yu, Jingbo Wang, Chao Peng +3
Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inf…
Learning a Discriminative Feature Network for Semantic Segmentation
Changqian Yu, Jingbo Wang, Chao Peng +3
Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, w…