6 citations · 15 across the 8 of their papers we have counts for
8 papers
A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
Ziteng Gao, Jiaqi Qu, Chaoyu Chen
Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorith…
PixNerd: Pixel Neural Field Diffusion
Shuai Wang, Ziteng Gao, Chenhui Zhu +2
The current success of diffusion transformers heavily depends on the compressed latent space shaped by the pre-trained variational autoencoder(VAE). However, this two-stage trainin…
STMixer: A One-Stage Sparse Action Detector
Tao Wu, Mengqi Cao, Ziteng Gao +2
Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract…
SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
Ziteng Gao, Zhan Tong, Limin Wang +1
Human visual recognition is a sparse process, where only a few salient visual cues are attended to rather than traversing every detail uniformly. However, most current vision netwo…
STMixer: A One-Stage Sparse Action Detector
Tao Wu, Mengqi Cao, Ziteng Gao +2
Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract…
AdaMixer: A Fast-Converging Query-Based Object Detector
Ziteng Gao, Limin Wang, Bing Han +1
Traditional object detectors employ the dense paradigm of scanning over locations and scales in an image. The recent query-based object detectors break this convention by decoding…
Mutual Supervision for Dense Object Detection
Ziteng Gao, Limin Wang, Gangshan Wu
The classification and regression head are both indispensable components to build up a dense object detector, which are usually supervised by the same training samples and thus exp…
LIP: Local Importance-based Pooling
Ziteng Gao, Limin Wang, Gangshan Wu
Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory consumption. However, for disc…