189 citations · 359 across the 29 of their papers we have counts for
29 papers
High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning
Chao Xu, Junwei Zhu, Jiangning Zhang +6
Recently, emotional talking face generation has received considerable attention. However, existing methods only adopt one-hot coding, image, or audio as emotion conditions, thus la…
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator
Chao Xu, Shaoting Zhu, Junwei Zhu +4
Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from t…
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
Yue Han, Jiangning Zhang, Yabiao Wang +5
Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes with limited support examples. Existing methods based on Region Proposal Networks (RPNs) face…
E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context
Zizhang Li, Mengmeng Wang, Huaijin Pi +3
Recently, the image-wise implicit neural representation of videos, NeRV, has gained popularity for its promising results and swift speed compared to regular pixel-wise implicit rep…
EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
Jiangning Zhang, Xiangtai Li, Yabiao Wang +4
Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical evolutionary algorithm (EA) and derives that both…
Region-Aware Face Swapping
Chao Xu, Jiangning Zhang, Miao Hua +3
This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: \textbf…
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution
Jiangning Zhang, Chao Xu, Jian Li +4
In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-samp…
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network
Xianfang Zeng, Jiangning Zhang, Liang Liu +2
In spite of the success on benchmark datasets, most advanced face super-resolution models perform poorly in real scenarios since the remarkable domain gap between the real images a…
MSP : Refine Boundary Segmentation via Multiscale Superpixel
Jie Zhu, Huabin Huang, Banghuai Li +2
In this paper, we propose a simple but effective message passing method to improve the boundary quality for the semantic segmentation result. Inspired by the generated sharp edges…
MaIL: A Unified Mask-Image-Language Trimodal Network for Referring Image Segmentation
Zizhang Li, Mengmeng Wang, Jianbiao Mei +1
Referring image segmentation is a typical multi-modal task, which aims at generating a binary mask for referent described in given language expressions. Prior arts adopt a bimodal…
LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation
WeiFu Fu, CongChong Nie, Ting Sun +3
This report introduces the technical details of the team FuXi-Fresher for LVIS Challenge 2021. Our method focuses on the problem in following two aspects: the long-tail distributio…
Thoughts on the Consistency between Ricci Flow and Neural Network Behavior
Jun Chen, Tianxin Huang, Wenzhou Chen +1
The Ricci flow is a partial differential equation for evolving the metric in a Riemannian manifold to make it more regular. On the other hand, neural networks seem to have similar…
Explicitly Modeling the Discriminability for Instance-Aware Visual Object Tracking
Mengmeng Wang, Xiaoqian Yang, Yong Liu
Visual object tracking performance has been dramatically improved in recent years, but some severe challenges remain open, like distractors and occlusions. We suspect the reason is…
ActionCLIP: A New Paradigm for Video Action Recognition
Mengmeng Wang, Jiazheng Xing, Yong Liu
The canonical approach to video action recognition dictates a neural model to do a classic and standard 1-of-N majority vote task. They are trained to predict a fixed set of predef…
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
Lina Liu, Xibin Song, Mengmeng Wang +2
Remarkable results have been achieved by DCNN based self-supervised depth estimation approaches. However, most of these approaches can only handle either day-time or night-time ima…
TransVOS: Video Object Segmentation with Transformers
Jianbiao Mei, Mengmeng Wang, Yeneng Lin +2
Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS). A crucial problem in this tas…
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Jiangning Zhang, Chao Xu, Jian Li +7
Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them hav…
Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation
Jilin Tang, Yi Yuan, Tianjia Shao +3
In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the…
One-shot Face Reenactment Using Appearance Adaptive Normalization
Guangming Yao, Yi Yuan, Tianjia Shao +5
The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a dri…
HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
Xiaoyang Lyu, Liang Liu, Mengmeng Wang +5
Self-supervised learning shows great potential in monoculardepth estimation, using image sequences as the only source ofsupervision. Although people try to use the high-resolutioni…
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion
Lina Liu, Xibin Song, Xiaoyang Lyu +4
Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate depth completion as a one…
APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
Jiangning Zhang, Xianfang Zeng, Chao Xu +3
Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio. However, current methods can only reenact a special pe…
Hierarchical and Efficient Learning for Person Re-Identification
Jiangning Zhang, Liang Liu, Chao Xu +1
Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e.g. model size and latency, which are critical…
APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals
Jiangning Zhang, Liang Liu, Zhucun Xue +1
Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person. However,…