1 code implementation • 23 Aug 2024 • Qi Fan, Yutong Li, Yi Xin, Xinyu Cheng, Guanglai Gao, Miao Ma
The Multimodal Emotion Recognition challenge MER2024 focuses on recognizing emotions using audio, language, and visual signals.
1 code implementation • CVPR 2024 • Jiapeng Su, Qi Fan, Guangming Lu, Fanglin Chen, Wenjie Pei
Instead, our key idea is to adapt a small adapter for rectifying diverse target domain styles to the source domain.
no code implementations • 10 Apr 2024 • Dongdong Ren, Wenbin Li, Tianyu Ding, Lei Wang, Qi Fan, Jing Huo, Hongbing Pan, Yang Gao
However, the practical application of these algorithms across various models and platforms remains a significant challenge.
3 code implementations • 26 Mar 2024 • Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, Xiaoyi Dong, Haodong Duan, Qi Fan, Zhaoye Fei, Yang Gao, Jiaye Ge, Chenya Gu, Yuzhe Gu, Tao Gui, Aijia Guo, Qipeng Guo, Conghui He, Yingfan Hu, Ting Huang, Tao Jiang, Penglong Jiao, Zhenjiang Jin, Zhikai Lei, Jiaxing Li, Jingwen Li, Linyang Li, Shuaibin Li, Wei Li, Yining Li, Hongwei Liu, Jiangning Liu, Jiawei Hong, Kaiwen Liu, Kuikun Liu, Xiaoran Liu, Chengqi Lv, Haijun Lv, Kai Lv, Li Ma, Runyuan Ma, Zerun Ma, Wenchang Ning, Linke Ouyang, Jiantao Qiu, Yuan Qu, FuKai Shang, Yunfan Shao, Demin Song, Zifan Song, Zhihao Sui, Peng Sun, Yu Sun, Huanze Tang, Bin Wang, Guoteng Wang, Jiaqi Wang, Jiayu Wang, Rui Wang, Yudong Wang, Ziyi Wang, Xingjian Wei, Qizhen Weng, Fan Wu, Yingtong Xiong, Chao Xu, Ruiliang Xu, Hang Yan, Yirong Yan, Xiaogui Yang, Haochen Ye, Huaiyuan Ying, JIA YU, Jing Yu, Yuhang Zang, Chuyu Zhang, Li Zhang, Pan Zhang, Peng Zhang, Ruijie Zhang, Shuo Zhang, Songyang Zhang, Wenjian Zhang, Wenwei Zhang, Xingcheng Zhang, Xinyue Zhang, Hui Zhao, Qian Zhao, Xiaomeng Zhao, Fengzhe Zhou, Zaida Zhou, Jingming Zhuo, Yicheng Zou, Xipeng Qiu, Yu Qiao, Dahua Lin
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
no code implementations • 8 Dec 2023 • Haoran Fan, Qi Fan, Maurice Pagnucco, Yang song
Moreover, recognizing the variability across target domains, an Adaptive Refine Self-Matching (ARSM) method is also proposed to adjust the matching threshold and dynamically refine the prediction result with the self-matching method, enhancing accuracy.
1 code implementation • 27 Nov 2023 • Qi Fan, Xin Tao, Lei Ke, Mingqiao Ye, Yuan Zhang, Pengfei Wan, Zhongyuan Wang, Yu-Wing Tai, Chi-Keung Tang
Thus, our solution, termed Stable-SAM, offers several advantages: 1) improved SAM's segmentation stability across a wide range of prompt qualities, while 2) retaining SAM's powerful promptable segmentation efficiency and generality, with 3) minimal learnable parameters (0. 08 M) and fast adaptation (by 1 training epoch).
no code implementations • 3 Oct 2023 • Xueqing Deng, Qi Fan, Xiaojie Jin, Linjie Yang, Peng Wang
Specifically, SFA consists of external adapters and internal adapters which are sequentially operated over a transformer model.
1 code implementation • 21 Sep 2023 • Qi Fan, Haolin Zuo, Rui Liu, Zheng Lian, Guanglai Gao
This approach includes two pivotal components: firstly, a noise scheduler that adjusts the type and level of noise in the data to emulate various realistic incomplete situations.
no code implementations • 7 Jun 2023 • Yanan sun, Zihan Zhong, Qi Fan, Chi-Keung Tang, Yu-Wing Tai
Our thorough studies validate that models pre-trained as such can learn rich representations of both modalities, improving their ability to understand how images and text relate to each other.
no code implementations • 8 Nov 2022 • Qi Fan, Mattia Segu, Yu-Wing Tai, Fisher Yu, Chi-Keung Tang, Bernt Schiele, Dengxin Dai
Thus, we propose to perturb the channel statistics of source domain features to synthesize various latent styles, so that the trained deep model can perceive diverse potential domains and generalizes well even without observations of target domain data in training.
1 code implementation • 23 Jul 2022 • Qi Fan, Wenjie Pei, Yu-Wing Tai, Chi-Keung Tang
Motivated by the simple Gestalt principle that pixels belonging to the same object are more similar than those to different objects of same class, we propose a novel self-support matching strategy to alleviate this problem, which uses query prototypes to match query features, where the query prototypes are collected from high-confidence query predictions.
Ranked #13 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
3 code implementations • 30 May 2022 • Peng Zheng, Huazhu Fu, Deng-Ping Fan, Qi Fan, Jie Qin, Yu-Wing Tai, Chi-Keung Tang, Luc van Gool
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes.
Ranked #1 on
Co-Salient Object Detection
on CoCA
1 code implementation • 30 Apr 2021 • Qi Fan, Chi-Keung Tang, Yu-Wing Tai
We introduce Few-Shot Video Object Detection (FSVOD) with three contributions to real-world visual learning challenge in our highly diverse and dynamic world: 1) a large-scale video dataset FSVOD-500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Network (TPN) to generate high-quality video tube proposals for aggregating feature representation for the target video object which can be highly dynamic; 3) a strategically improved Temporal Matching Network (TMN+) for matching representative query tube features with better discriminative ability thus achieving higher diversity.
1 code implementation • CVPR 2021 • Qi Fan, Deng-Ping Fan, Huazhu Fu, Chi Keung Tang, Ling Shao, Yu-Wing Tai
We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus.
Ranked #5 on
Co-Salient Object Detection
on CoCA
1 code implementation • ECCV 2020 • Qi Fan, Lei Ke, Wenjie Pei, Chi-Keung Tang, Yu-Wing Tai
We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories.
Ranked #81 on
Instance Segmentation
on COCO test-dev
3 code implementations • CVPR 2020 • Qi Fan, Wei Zhuo, Chi-Keung Tang, Yu-Wing Tai
To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations.
Ranked #23 on
Few-Shot Object Detection
on MS-COCO (10-shot)
no code implementations • 6 Feb 2017 • Yanhao Wang, Qi Fan, Yuchen Li, Kian-Lee Tan
Influence maximization (IM), which selects a set of $k$ users (called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring.
Social and Information Networks Data Structures and Algorithms