Search Results for author: Zichang Tan

Found 24 papers, 11 papers with code

Make Your ViT-based Multi-view 3D Detectors Faster via Token Compression

1 code implementation1 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.

Autonomous Driving

BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object Detection

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.

3D Object Detection Autonomous Driving +1

Training-Free Unsupervised Prompt for Vision-Language Models

1 code implementation25 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.

PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition

no code implementations31 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.

Multi-Label Image Recognition Representation Learning

CAMEL: CAusal Motion Enhancement Tailored for Lifting Text-driven Video Editing

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.

Video Editing

Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection

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.

Attribute Synthetic Image Detection

ProtoHPE: Prototype-guided High-frequency Patch Enhancement for Visible-Infrared Person Re-identification

no code implementations11 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.

Person Re-Identification

Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation

no code implementations18 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.

Decoder Misinformation

Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation

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.

Decoder Human Detection +1

General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stone

no code implementations19 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.

Age Estimation MORPH

NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition

1 code implementation29 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.

Nested Collaborative Learning for Long-Tailed Visual Recognition

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.

Image Classification Long-tail Learning

Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition

1 code implementation24 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)

Defending Black-box Skeleton-based Human Activity Classifiers

2 code implementations9 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.

Human Activity Recognition Time Series Analysis

LAE : Long-tailed Age Estimation

no code implementations25 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.

Age Estimation Data Augmentation +1

Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review

no code implementations23 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.

Face Anti-Spoofing Face Recognition

CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing

no code implementations11 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.

Face Anti-Spoofing Face Recognition

Static and Dynamic Fusion for Multi-modal Cross-ethnicity Face Anti-spoofing

no code implementations5 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.

Face Anti-Spoofing

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