no code implementations • 2 Nov 2022 • Yixuan Pei, Zhiwu Qing, Jun Cen, Xiang Wang, Shiwei Zhang, Yaxiong Wang, Mingqian Tang, Nong Sang, Xueming Qian
The former is to reduce the memory cost by preserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensing stage.
no code implementations • 9 Oct 2022 • Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu, Xibin Zhao, Hai Wan, Mingqian Tang, Rong Jin, Yue Gao
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories.
3 code implementations • 5 Sep 2022 • Hangjie Yuan, Jianwen Jiang, Samuel Albanie, Tao Feng, Ziyuan Huang, Dong Ni, Mingqian Tang
The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications.
Ranked #16 on Human-Object Interaction Detection on HICO-DET
1 code implementation • 4 Jul 2022 • Jun Cen, Peng Yun, Shiwei Zhang, Junhao Cai, Di Luan, Michael Yu Wang, Ming Liu, Mingqian Tang
Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e. g., autonomous driving, since it is closed-set and static.
1 code implementation • CVPR 2022 • Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, Nong Sang
To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric.
no code implementations • CVPR 2022 • Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang
In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos.
2 code implementations • ICLR 2022 • Ziyuan Huang, Shiwei Zhang, Liang Pan, Zhiwu Qing, Mingqian Tang, Ziwei Liu, Marcelo H. Ang Jr
This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modelling complex temporal dynamics in videos.
Ranked #67 on Action Recognition on Something-Something V2 (using extra training data)
no code implementations • ICLR 2022 • Yutong Feng, Jianwen Jiang, Mingqian Tang, Rong Jin, Yue Gao
Though for most cases, the pre-training stage is conducted based on supervised methods, recent works on self-supervised pre-training have shown powerful transferability and even outperform supervised pre-training on multiple downstream tasks.
no code implementations • ICCV 2021 • Zhi-Fan Wu, Tong Wei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, Yu-Feng Li
The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance.
Ranked #18 on Image Classification on mini WebVision 1.0
no code implementations • ICCV 2021 • Xinpeng Ding, Nannan Wang, Shiwei Zhang, De Cheng, Xiaomeng Li, Ziyuan Huang, Mingqian Tang, Xinbo Gao
The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts.
1 code implementation • 24 Aug 2021 • Zhiwu Qing, Ziyuan Huang, Shiwei Zhang, Mingqian Tang, Changxin Gao, Marcelo H. Ang Jr, Rong Jin, Nong Sang
The visualizations show that ParamCrop adaptively controls the center distance and the IoU between two augmented views, and the learned change in the disparity along the training process is beneficial to learning a strong representation.
no code implementations • 4 Aug 2021 • Zhen Han, Xiangteng He, Mingqian Tang, Yiliang Lv
To address the above issues, we propose the Video Similarity and Alignment Learning (VSAL) approach, which jointly models spatial similarity, temporal similarity and partial alignment.
1 code implementation • 26 Jul 2021 • Peng Wu, Xiangteng He, Mingqian Tang, Yiliang Lv, Jing Liu
Based on these, we naturally construct hierarchical representations in the individual-local-global manner, where the individual level focuses on the alignment between frame and word, local level focuses on the alignment between video clip and textual context, and global level focuses on the alignment between the whole video and text.
no code implementations • 24 Jun 2021 • Zhiwu Qing, Xiang Wang, Ziyuan Huang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Changxin Gao, Nong Sang
Temporal action localization aims to localize starting and ending time with action category.
1 code implementation • 20 Jun 2021 • Xiang Wang, Zhiwu Qing, Ziyuan Huang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Changxin Gao, Nong Sang
We calculate the detection results by assigning the proposals with corresponding classification results.
Ranked #2 on Temporal Action Localization on ActivityNet-1.3 (using extra training data)
no code implementations • 20 Jun 2021 • Xiang Wang, Zhiwu Qing, Ziyuan Huang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Yuanjie Shao, Nong Sang
Then our proposed Local-Global Background Modeling Network (LGBM-Net) is trained to localize instances by using only video-level labels based on Multi-Instance Learning (MIL).
Weakly-supervised Learning Weakly-supervised Temporal Action Localization +1
no code implementations • 15 Jun 2021 • Yutong Feng, Jianwen Jiang, Ziyuan Huang, Zhiwu Qing, Xiang Wang, Shiwei Zhang, Mingqian Tang, Yue Gao
This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021.
Ranked #4 on Spatio-Temporal Action Localization on AVA-Kinetics (using extra training data)
1 code implementation • 13 Jun 2021 • Zhiwu Qing, Ziyuan Huang, Xiang Wang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Changxin Gao, Marcelo H. Ang Jr, Nong Sang
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop.
1 code implementation • 9 Jun 2021 • Ziyuan Huang, Zhiwu Qing, Xiang Wang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Zhurong Xia, Mingqian Tang, Nong Sang, Marcelo H. Ang Jr
In this paper, we present empirical results for training a stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition dataset.
no code implementations • 16 Apr 2021 • Xiangteng He, Yulin Pan, Mingqian Tang, Yiliang Lv
In addition, most retrieval systems are based on frame-level features for video similarity searching, making it expensive both storage wise and search wise.
1 code implementation • CVPR 2021 • Ziyuan Huang, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Rong Jin, Marcelo Ang
We furthermore introduce a static mask in pseudo motions to create local motion patterns, which forces the model to additionally locate notable motion areas for the correct classification. We demonstrate that MoSI can discover regions with large motion even without fine-tuning on the downstream datasets.
no code implementations • 1 Jan 2021 • Yuqi Huo, Mingyu Ding, Haoyu Lu, Zhiwu Lu, Tao Xiang, Ji-Rong Wen, Ziyuan Huang, Jianwen Jiang, Shiwei Zhang, Mingqian Tang, Songfang Huang, Ping Luo
With the constrained jigsaw puzzles, instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable but meanwhile still ensure that the learned representation is sensitive to spatiotemporal continuity at both the local and global levels.
no code implementations • 10 Dec 2020 • Liang Han, Zhaozheng Yin, Zhurong Xia, Mingqian Tang, Rong Jin
The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items with the images and text descriptions uploaded to the online platforms.
no code implementations • 10 Dec 2020 • Liang Han, Zhaozheng Yin, Zhurong Xia, Li Guo, Mingqian Tang, Rong Jin
Then, we design a vision-based price suggestion module which takes the extracted visual features along with some statistical item features from the shopping platform as the inputs to determine whether an uploaded item image is qualified for price suggestion by a binary classification model, and provide price suggestions for items with qualified images by a regression model.