85 citations · 201 across the 15 of their papers we have counts for
15 papers
Unified Face Attack Detection via Fine-Grained Semantic Guidance
Ning Jiang, Shijie Yu, Dingheng Zeng +4
The growing applications of facial recognition systems are accompanied by increasingly diverse security threats. Existing datasets lack detailed textual descriptions of forgery cue…
Explainable Deepfake Detection with RL Enhanced Self-Blended Images
Ning Jiang, Dingheng Zeng, Yanhong Liu +5
Most prior deepfake detection methods lack explainable outputs. With the growing interest in multimodal large language models (MLLMs), researchers have started exploring their use…
Reconstruction and Reenactment Separated Method for Realistic Gaussian Head
Zhiling Ye, Cong Zhou, Xiubao Zhang +3
In this paper, we explore a reconstruction and reenactment separated framework for 3D Gaussians head, which requires only a single portrait image as input to generate controllable…
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
Sheng Luo, Wei Chen, Wanxin Tian +12
Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems. In the cont…
Multi-View Stereo with Transformer
Jie Zhu, Bo Peng, Wanqing Li +3
This paper proposes a network, referred to as MVSTR, for Multi-View Stereo (MVS). It is built upon Transformer and is capable of extracting dense features with global context and 3…
Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation
Luanxuan Hou, Jie Cao, Yuan Zhao +3
We propose an augmented Parallel-Pyramid Net () with feature refinement by dilated bottleneck and attention module. During data preprocessing, we proposed a differentiable…
Single Image Super-Resolution via a Holistic Attention Network
Ben Niu, Weilei Wen, Wenqi Ren +6
Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features…
PropagationNet: Propagate Points to Curve to Learn Structure Information
Xiehe Huang, Weihong Deng, Haifeng Shen +2
Deep learning technique has dramatically boosted the performance of face alignment algorithms. However, due to large variability and lack of samples, the alignment problem in uncon…
Mutual Learning Network for Multi-Source Domain Adaptation
Zhenpeng Li, Zhen Zhao, Yuhong Guo +2
Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed the setting of a single source domain, where all the labeled source data come from the same distribution. How…
Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation
Luanxuan Hou, Jie Cao, Yuan Zhao +4
The target of human pose estimation is to determine body part or joint locations of each person from an image. This is a challenging problems with wide applications. To address thi…
Adaptive Object Detection with Dual Multi-Label Prediction
Zhen Zhao, Yuhong Guo, Haifeng Shen +1
In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliar…
Metric Classification Network in Actual Face Recognition Scene
Jian Li, Yan Wang, Xiubao Zhang +2
In order to make facial features more discriminative, some new models have recently been proposed. However, almost all of these models use the traditional face verification method,…
Bi-Skip: A Motion Deblurring Network Using Self-paced Learning
Yiwei Zhang, Chunbiao Zhu, Ge Li +2
A fast and effective motion deblurring method has great application values in real life. This work presents an innovative approach in which a self-paced learning is combined with G…
Virtual Class Enhanced Discriminative Embedding Learning
Binghui Chen, Weihong Deng, Haifeng Shen
Recently, learning discriminative features to improve the recognition performances gradually becomes the primary goal of deep learning, and numerous remarkable works have emerged.…
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision
Wanxin Tian, Zixuan Wang, Haifeng Shen +6
The performance of face detectors has been largely improved with the development of convolutional neural network. However, it remains challenging for face detectors to detect tiny,…