no code implementations • 22 Jan 2024 • Mengmeng Wang, Jiazheng Xing, Boyuan Jiang, Jun Chen, Jianbiao Mei, Xingxing Zuo, Guang Dai, Jingdong Wang, Yong liu
In this paper, we introduce a novel Multimodal, Multi-task CLIP adapting framework named \name to address these challenges, preserving both high supervised performance and robust transferability.
no code implementations • 11 Dec 2023 • Xu Peng, Junwei Zhu, Boyuan Jiang, Ying Tai, Donghao Luo, Jiangning Zhang, Wei Lin, Taisong Jin, Chengjie Wang, Rongrong Ji
Moreover, these methods often grapple with identity distortion and limited expression diversity.
1 code implementation • ICCV 2023 • Boyuan Jiang, Lei Hu, Shihong Xia
The key idea is to use a probability distribution to model the camera pose and iteratively update the distribution from 2D features instead of using camera pose.
1 code implementation • 7 Sep 2023 • Lingtong Kong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Ying Tai, Chengjie Wang, Jie Yang
Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience.
1 code implementation • 13 Jun 2023 • Lei Hu, Zihao Zhang, Chongyang Zhong, Boyuan Jiang, Shihong Xia
Moreover, we also show that our framework can generate reasonable results even for a more challenging retargeting scenario, like retargeting between bipedal and quadrupedal skeletons because of the body part retargeting strategy and PAN.
2 code implementations • CVPR 2022 • Lingtong Kong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Xiaoming Huang, Ying Tai, Chengjie Wang, Jie Yang
Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters or large delay, hindering them from diverse real-time applications.
Ranked #1 on Video Frame Interpolation on Middlebury
no code implementations • 23 Mar 2021 • Mingyu Wu, Boyuan Jiang, Donghao Luo, Junchi Yan, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Xiaokang Yang
For action recognition learning, 2D CNN-based methods are efficient but may yield redundant features due to applying the same 2D convolution kernel to each frame.
no code implementations • 24 Feb 2021 • Jingwei Yan, Boyuan Jiang, Jingjing Wang, Qiang Li, Chunmao Wang, ShiLiang Pu
In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed and graph convolution is performed to further enhance AU regional features of each level.
no code implementations • ICCV 2019 • Boyuan Jiang, Mengmeng Wang, Weihao Gan, Wei Wu, Junjie Yan
Spatiotemporal and motion features are two complementary and crucial information for video action recognition.
Ranked #1 on Action Recognition In Videos on HMDB-51
no code implementations • CVPR 2020 • Zhihong Chen, Chao Chen, Zhaowei Cheng, Boyuan Jiang, Ke Fang, Xinyu Jin
However, since the domain shift between source and target domains, only using the deep features for sample selection is defective.
Ranked #6 on Partial Domain Adaptation on Office-31
1 code implementation • 4 Sep 2018 • Chao Chen, Boyuan Jiang, Xinyu Jin
Unlike the existing parameter transfer approaches, which incorporate the source model information into the target by regularizing the di erence between the source and target domain parameters, an intuitively appealing projective-model is proposed to bridge the source and target model parameters.
1 code implementation • 28 Aug 2018 • Chao Chen, Zhihong Chen, Boyuan Jiang, Xinyu Jin
Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities.