1 code implementation • 1 Sep 2024 • Dingyuan Zhang, Dingkang Liang, Zichang Tan, Xiaoqing Ye, Cheng Zhang, Jingdong Wang, Xiang Bai
Slow inference speed is one of the most crucial concerns for deploying multi-view 3D detectors to tasks with high real-time requirements like autonomous driving.
no code implementations • 30 Jul 2024 • Hao Tan, Zichang Tan, Jun Li, Jun Wan, Zhen Lei, Stan Z. Li
With the help of flexible prompting and gated alignments, SSPA is generalizable to specific domains.
1 code implementation • CVPR 2024 • Wenjie Wang, Yehao Lu, Guangcong Zheng, Shuigen Zhan, Xiaoqing Ye, Zichang Tan, Jingdong Wang, Gaoang Wang, Xi Li
Vision-based roadside 3D object detection has attracted rising attention in autonomous driving domain, since it encompasses inherent advantages in reducing blind spots and expanding perception range.
1 code implementation • 25 Apr 2024 • Sifan Long, Linbin Wang, Zhen Zhao, Zichang Tan, Yiming Wu, Shengsheng Wang, Jingdong Wang
In light of this, we propose Training-Free Unsupervised Prompts (TFUP), which maximally preserves the inherent representation capabilities and enhances them with a residual connection to similarity-based prediction probabilities in a training-free and labeling-free manner.
no code implementations • 31 Jan 2024 • Hao Tan, Zichang Tan, Jun Li, Jun Wan, Zhen Lei
In contrast to the unidirectional fusion in previous works, we introduce a Dual-Modal Attention (DMA) that enables bidirectional interaction between textual and visual features, yielding context-aware label representations and semantic-related visual representations, which are subsequently used to calculate similarities and generate final predictions for all labels.
no code implementations • CVPR 2024 • Guiwei Zhang, Tianyu Zhang, Guanglin Niu, Zichang Tan, Yalong Bai, Qing Yang
Second to enhance motion coherence and extend the generalization of appearance content to creative textual prompts we propose the causal motion-enhanced attention mechanism.
1 code implementation • CVPR 2024 • Huan Liu, Zichang Tan, Chuangchuang Tan, Yunchao Wei, Yao Zhao, Jingdong Wang
In this paper, we study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods, e. g., GANs and diffusion models.
no code implementations • 11 Oct 2023 • Guiwei Zhang, Yongfei Zhang, Zichang Tan
In contrast, we find that some cross-modal correlated high-frequency components contain discriminative visual patterns and are less affected by variations such as wavelength, pose, and background clutter than holistic images.
no code implementations • 18 Sep 2023 • Huan Liu, Zichang Tan, Qiang Chen, Yunchao Wei, Yao Zhao, Jingdong Wang
Moreover, to address the semantic conflicts between image and frequency domains, the forgery-aware mutual module is developed to further enable the effective interaction of disparate image and frequency features, resulting in aligned and comprehensive visual forgery representations.
2 code implementations • ICCV 2023 • Huan Liu, Qiang Chen, Zichang Tan, Jiang-Jiang Liu, Jian Wang, Xiangbo Su, Xiaolong Li, Kun Yao, Junyu Han, Errui Ding, Yao Zhao, Jingdong Wang
State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e. g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose, hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR.
no code implementations • 19 Jul 2023 • Zenghao Bao, Zichang Tan, Jun Li, Jun Wan, Xibo Ma, Zhen Lei
Driven by this, some works suggest that each class should be treated equally to improve performance in tail classes (with a minority of samples), which can be summarized as Long-tailed Age Estimation.
1 code implementation • 29 Jun 2023 • Zichang Tan, Jun Li, Jinhao Du, Jun Wan, Zhen Lei, Guodong Guo
To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed to force the consistent predictions among different experts and augmented copies, which reduces the learning uncertainties.
1 code implementation • 17 May 2023 • Jiang-Tian Zhai, Ze Feng, Jinhao Du, Yongqiang Mao, Jiang-Jiang Liu, Zichang Tan, Yifu Zhang, Xiaoqing Ye, Jingdong Wang
Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning.
Ranked #1 on Trajectory Planning on nuScenes
no code implementations • 5 May 2023 • Ajian Liu, Zichang Tan, Zitong Yu, Chenxu Zhao, Jun Wan, Yanyan Liang, Zhen Lei, Du Zhang, Stan Z. Li, Guodong Guo
The availability of handy multi-modal (i. e., RGB-D) sensors has brought about a surge of face anti-spoofing research.
no code implementations • ICCV 2023 • Sifan Long, Zhen Zhao, Junkun Yuan, Zichang Tan, JiangJiang Liu, Luping Zhou, Shengsheng Wang, Jingdong Wang
A contrastive loss is employed to align such augmented text and image representations on downstream tasks.
1 code implementation • 11 Dec 2022 • Fanglei Xue, Qiangchang Wang, Zichang Tan, Zhongsong Ma, Guodong Guo
The proposed APP is employed to select the most informative patches on CNN features, and ATP discards unimportant tokens in ViT.
Ranked #10 on Facial Expression Recognition (FER) on RAF-DB
Facial Expression Recognition Facial Expression Recognition (FER) +1
1 code implementation • CVPR 2022 • Jun Li, Zichang Tan, Jun Wan, Zhen Lei, Guodong Guo
NCL consists of two core components, namely Nested Individual Learning (NIL) and Nested Balanced Online Distillation (NBOD), which focus on the individual supervised learning for each single expert and the knowledge transferring among multiple experts, respectively.
Ranked #7 on Long-tail Learning on CIFAR-10-LT (ρ=50)
1 code implementation • 24 Mar 2022 • Fanglei Xue, Zichang Tan, Yu Zhu, Zhongsong Ma, Guodong Guo
To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment.
Facial Expression Recognition Facial Expression Recognition (FER)
2 code implementations • 9 Mar 2022 • He Wang, Yunfeng Diao, Zichang Tan, Guodong Guo
Our method is featured by full Bayesian treatments of the clean data, the adversaries and the classifier, leading to (1) a new Bayesian Energy-based formulation of robust discriminative classifiers, (2) a new adversary sampling scheme based on natural motion manifolds, and (3) a new post-train Bayesian strategy for black-box defense.
no code implementations • 25 Oct 2021 • Zenghao Bao, Zichang Tan, Yu Zhu, Jun Wan, Xibo Ma, Zhen Lei, Guodong Guo
To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on.
no code implementations • 13 Apr 2021 • Ajian Liu, Chenxu Zhao, Zitong Yu, Jun Wan, Anyang Su, Xing Liu, Zichang Tan, Sergio Escalera, Junliang Xing, Yanyan Liang, Guodong Guo, Zhen Lei, Stan Z. Li, Du Zhang
To bridge the gap to real-world applications, we introduce a largescale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask).
no code implementations • 23 Apr 2020 • Ajian Liu, Xuan Li, Jun Wan, Sergio Escalera, Hugo Jair Escalante, Meysam Madadi, Yi Jin, Zhuoyuan Wu, Xiaogang Yu, Zichang Tan, Qi Yuan, Ruikun Yang, Benjia Zhou, Guodong Guo, Stan Z. Li
Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing.
no code implementations • 11 Mar 2020 • Ajian Li, Zichang Tan, Xuan Li, Jun Wan, Sergio Escalera, Guodong Guo, Stan Z. Li
Ethnic bias has proven to negatively affect the performance of face recognition systems, and it remains an open research problem in face anti-spoofing.
no code implementations • 5 Dec 2019 • Ajian Liu, Zichang Tan, Xuan Li, Jun Wan, Sergio Escalera, Guodong Guo, Stan Z. Li
Regardless of the usage of deep learning and handcrafted methods, the dynamic information from videos and the effect of cross-ethnicity are rarely considered in face anti-spoofing.