Search Results for author: Javen Qinfeng Shi

Found 27 papers, 11 papers with code

MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

no code implementations16 Jul 2024 Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi

As Large Language Models (LLMs) grow dramatically in size, there is an increasing trend in compressing and speeding up these models.

Multiple-choice

Augmented Commonsense Knowledge for Remote Object Grounding

1 code implementation3 Jun 2024 Bahram Mohammadi, Yicong Hong, Yuankai Qi, Qi Wu, Shirui Pan, Javen Qinfeng Shi

To address enhancing representation, we propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as a spatio-temporal knowledge graph for improving agent navigation.

Decision Making Object +1

Identifiable Latent Neural Causal Models

no code implementations23 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.

Representation Learning

A Simple-but-effective Baseline for Training-free Class-Agnostic Counting

no code implementations3 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.

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models

no code implementations9 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.

Representation Learning

Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines

no code implementations29 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.

Retrieval

CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts

1 code implementation28 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.

Contrastive Learning Image Augmentation +1

Identifiable Latent Polynomial Causal Models Through the Lens of Change

no code implementations24 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.

Representation Learning

A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations

1 code implementation13 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.

Adversarial Robustness Network Pruning +1

Influence Function Based Second-Order Channel Pruning-Evaluating True Loss Changes For Pruning Is Possible Without Retraining

1 code implementation13 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.

Factor Graph Neural Networks

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.

Graph Neural Network Representation Learning

Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation

no code implementations17 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.

Image Reconstruction Representation Learning +2

Stock Market Prediction via Deep Learning Techniques: A Survey

no code implementations24 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.

Stock Market Prediction

Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense

1 code implementation5 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.

Adversarial Defense

Identifying Weight-Variant Latent Causal Models

no code implementations30 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.

Representation Learning

Truncated Matrix Power Iteration for Differentiable DAG Learning

1 code implementation30 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.

Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift

no code implementations30 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.

Domain Adaptation

Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model

1 code implementation14 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.

Decision Making News Classification +5

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 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).

Image Restoration Vocal Bursts Intensity Prediction

Implicit Sample Extension for Unsupervised Person Re-Identification

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.

Clustering Unsupervised Person Re-Identification

Active Learning by Feature Mixing

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.

Active Learning

Memory-Augmented Dynamic Neural Relational Inference

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.

Trajectory Prediction

Consistency-Aware Graph Network for Human Interaction Understanding

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).

3D human pose and shape estimation

Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation

no code implementations7 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.

Reinforcement Learning (RL) Visual Navigation

Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead

no code implementations7 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.

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