1 code implementation • CVPR 2022 • Xinyu Zhang, Dongdong Li, Zhigang Wang, Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, Jingdong Wang
Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy.
1 code implementation • 14 Jun 2022 • Jinan Zou, Haiyao Cao, Lingqiao Liu, YuHao Lin, Ehsan Abbasnejad, Javen Qinfeng Shi
In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system.
Ranked #1 on Stock Price Prediction on Astock
1 code implementation • 13 Aug 2023 • Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi
Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage resources.
3 code implementations • CVPR 2022 • Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Reza Haffari, Anton Van Den Hengel, Javen Qinfeng Shi
We identify unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations.
1 code implementation • ICCV 2021 • Zhenhua Wang, Jiajun Meng, Dongyan Guo, Jianhua Zhang, Javen Qinfeng Shi, ShengYong Chen
Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU).
1 code implementation • 30 Aug 2022 • Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan M Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi
Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization problem.
1 code implementation • 5 Dec 2022 • Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe
We recognize the adversarial learning approach for approximating the multi-modal posterior distribution of a Bayesian model can lead to mode collapse; consequently, the model's achievements in robustness and performance are sub-optimal.
1 code implementation • 29 May 2023 • Jinan Zou, Maihao Guo, Yu Tian, YuHao Lin, Haiyao Cao, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi
Identifying unexpected domain-shifted instances in natural language processing is crucial in real-world applications.
no code implementations • 7 May 2019 • Amin Parvaneh, Ehsan Abbasnejad, Qi Wu, Javen Qinfeng Shi, Anton Van Den Hengel
Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent.
no code implementations • 7 Apr 2020 • Mahdi Kazemi Moghaddam, Qi Wu, Ehsan Abbasnejad, Javen Qinfeng Shi
Through empirical studies, we show that our agent, dubbed as the optimistic agent, has a more realistic estimate of the state value during a navigation episode which leads to a higher success rate.
no code implementations • ICCV 2021 • Dong Gong, Frederic Z. Zhang, Javen Qinfeng Shi, Anton Van Den Hengel
This motivates us to propose a memory-augmented dynamic neural relational inference method, which maintains two associative memory pools: one for the interactive relations and the other for the individual entities.
no code implementations • CVPR 2022 • Dong Gong, Qingsen Yan, Yuhang Liu, Anton Van Den Hengel, Javen Qinfeng Shi
This minimizes the interference between parameters for different tasks.
Ranked #5 on Continual Learning on Tiny-ImageNet (10tasks)
no code implementations • 25 May 2022 • Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
no code implementations • 30 Aug 2022 • Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi
Within this new paradigm, we present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable to achieve more nuanced rendering of observational data.
no code implementations • 30 Aug 2022 • Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi
The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations.
no code implementations • 24 Dec 2022 • Jinan Zou, Qingying Zhao, Yang Jiao, Haiyao Cao, Yanxi Liu, Qingsen Yan, Ehsan Abbasnejad, Lingqiao Liu, Javen Qinfeng Shi
Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods.
no code implementations • 17 Mar 2023 • YuHao Lin, HaiMing Xu, Lingqiao Liu, Jinan Zou, Javen Qinfeng Shi
In this paper, we revisit the idea of using image reconstruction as the auxiliary task and incorporate it with a modern semi-supervised semantic segmentation framework.
no code implementations • NeurIPS 2020 • Zhen Zhang, Mohammed Haroon Dupty, Fan Wu, Javen Qinfeng Shi, Wee Sun Lee
In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications.
1 code implementation • 13 Aug 2023 • Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi
It motivates us to develop a technique to evaluate true loss changes without retraining, with which channels to prune can be selected more reliably and confidently.
no code implementations • 24 Oct 2023 • Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi
However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models.
no code implementations • 28 Nov 2023 • Yichao Cai, Yuhang Liu, Zhen Zhang, Javen Qinfeng Shi
To address this limitation, we adopt a causal generative perspective for multimodal data and propose contrastive learning with data augmentation to disentangle content features from the original representations.
no code implementations • 29 Nov 2023 • Hamed Damirchi, Cristian Rodríguez-Opazo, Ehsan Abbasnejad, Damien Teney, Javen Qinfeng Shi, Stephen Gould, Anton Van Den Hengel
Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box.
no code implementations • 9 Feb 2024 • Yuhang Liu, Zhen Zhang, Dong Gong, Biwei Huang, Mingming Gong, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi
Multimodal contrastive representation learning methods have proven successful across a range of domains, partly due to their ability to generate meaningful shared representations of complex phenomena.
no code implementations • 3 Mar 2024 • YuHao Lin, HaiMing Xu, Lingqiao Liu, Javen Qinfeng Shi
Class-Agnostic Counting (CAC) seeks to accurately count objects in a given image with only a few reference examples.
no code implementations • 23 Mar 2024 • Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton Van Den Hengel, Kun Zhang, Javen Qinfeng Shi
This work establishes a {sufficient} and {necessary} condition characterizing the types of distribution shifts for identifiability in the context of latent additive noise models.