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Guoqiang Zhong

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6 papers

1 author row

AAAI Conference 2024 Conference Paper

Self-Prompt Mechanism for Few-Shot Image Recognition

Mingchen Song
Huiqiang Wang
Guoqiang Zhong

Few-shot learning poses a formidable challenge as it necessitates effective recognition of novel classes based on a limited set of examples. Recent studies have sought to address the challenge of rare samples by tuning visual features through the utilization of external text prompts. However, the performance of these methods is constrained due to the inherent modality gap between the prompt text and image features. Instead of naively utilizing the external semantic information generated from text to guide the training of the image encoder, we propose a novel self-prompt mechanism (SPM) to adaptively adjust the neural network according to unseen data. Specifically, SPM involves a systematic selection of intrinsic semantic features generated by the image encoder across spatial and channel dimensions, thereby engendering self-prompt information. Subsequently, upon backpropagation of this self-prompt information to the deeper layers of the neural network, it effectively steers the network toward the learning and adaptation of new samples. Meanwhile, we propose a novel parameter-efficient tuning method that exclusively fine-tunes the parameters relevant to self-prompt (prompts are no more than 2% of the total parameters), and the incorporation of additional learnable parameters as self-prompt ensures the retention of prior knowledge through frozen encoder weights. Therefore, our method is highly suited for few-shot recognition tasks that require both information retention and adaptive adjustment of network parameters with limited labeling data constraints. Extensive experiments demonstrate the effectiveness of the proposed SPM in both 5-way 1-shot and 5-way 5-shot settings for standard single-domain and cross-domain few-shot recognition datasets, respectively. Our code is available at https://github.com/codeshop715/SPM.

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AAAI Conference 2024 Conference Paper

TextGT: A Double-View Graph Transformer on Text for Aspect-Based Sentiment Analysis

Shuo Yin
Guoqiang Zhong

Aspect-based sentiment analysis (ABSA) is aimed at predicting the sentiment polarities of the aspects included in a sentence instead of the whole sentence itself, and is a fine-grained learning task compared to the conventional text classification. In recent years, on account of the ability to model the connectivity relationships between the words in one sentence, graph neural networks have been more and more popular to handle the natural language processing tasks, and meanwhile many works emerge for the ABSA task. However, most of the works utilizing graph convolution easily incur the over-smoothing problem, while graph Transformer for ABSA has not been explored yet. In addition, although some previous works are dedicated to using both GNN and Transformer to handle text, the methods of tightly combining graph view and sequence view of text is open to research. To address the above issues, we propose a double-view graph Transformer on text (TextGT) for ABSA. In TextGT, the procedure in graph view of text is handled by GNN layers, while Transformer layers deal with the sequence view, and these two processes are tightly coupled, alleviating the over-smoothing problem. Moreover, we propose an algorithm for implementing a kind of densely message passing graph convolution called TextGINConv, to employ edge features in graphs. Extensive experiments demonstrate the effectiveness of our TextGT over the state-of-the-art approaches, and validate the TextGINConv module. The source code is available at https://github.com/shuoyinn/TextGT.

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IJCAI Conference 2023 Conference Paper

LGI-GT: Graph Transformers with Local and Global Operators Interleaving

Shuo Yin
Guoqiang Zhong

Since Transformers can alleviate some critical and fundamental problems of graph neural networks (GNNs), such as over-smoothing, over-squashing and limited expressiveness, they have been successfully applied to graph representation learning and achieved impressive results. However, although there are many works dedicated to make graph Transformers (GTs) aware of the structure and edge information by specifically tailored attention forms or graph-related positional and structural encodings, few works address the problem of how to construct high-performing GTs with modules of GNNs and Transformers. In this paper, we propose a novel graph Transformer with local and global operators interleaving (LGI-GT), in which we further design a new method propagating embeddings of the [CLS] token for global information representation. Additionally, we propose an effective message passing module called edge enhanced local attention (EELA), which makes LGI-GT a full-attention GT. Extensive experiments demonstrate that LGI-GT performs consistently better than previous state-of-the-art GNNs and GTs, while ablation studies show the effectiveness of the proposed LGI scheme and EELA. The source code of LGI-GT is available at https: //github. com/shuoyinn/LGI-GT.

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AAAI Conference 2014 Conference Paper

Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery

Chengcheng Jia
Guoqiang Zhong
Yun Fu

Tensor completion is an important topic in the area of image processing and computer vision research, which is generally built on extraction of the intrinsic structure of the tensor data. Drawing on this fact, action classiﬁcation, relying heavily on the extracted features of high-dimensional tensors, may indeed beneﬁt from tensor completion techniques. In this paper, we propose a low-rank tensor completion method for action classiﬁcation, as well as image recovery. Since there may exist distortion and corruption in the tensor representations of video sequences, we project the tensors into a subspace, which contains the invariant structure of the tensors. In order to integrate useful supervisory information for classiﬁcation, we adopt a discriminant analysis criterion to learn the projection matrices. The resulting multi-variate optimization problem can be effectively solved using the augmented Lagrange multiplier (ALM) algorithm. Experiments demonstrate that our method results with better accuracy compared with some other state-of-the-art low-rank tensor representation learning approaches on the MSR Hand Gesture 3D database and the MSR Action 3D database. By denoising the Multi-PIE face database, our experimental setup testiﬁes the proposed method can also be employed to recover images.

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IJCAI Conference 2013 Conference Paper

Adaptive Error-Correcting Output Codes

Guoqiang Zhong
Mohamed Cheriet

Error-correcting output codes (ECOC) are a successful technique to combine a set of binary classiﬁers for multi-class learning problems. However, in traditional ECOC framework, all the base classiﬁers are trained independently according to the deﬁned ECOC matrix. In this paper, we reformulate the ECOC models from the perspective of multi-task learning, where the binary classiﬁers are learned in a common subspace of data. This novel model can be considered as an adaptive generalization of the traditional ECOC framework. It simultaneously optimizes the representation of data as well as the binary classiﬁers. More importantly, it builds a bridge between the ECOC framework and multitask learning for multi-class learning problems. To deal with complex data, we also present the kernel extension of the proposed model. Extensive empirical study on 14 data sets from UCI machine learning repository and the USPS handwritten digits recognition application demonstrates the effectiveness and efﬁciency of our model.

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AAAI Conference 2010 Conference Paper

Gaussian Process Latent Random Field

Guoqiang Zhong
Wu-Jun Li
Dit-Yan Yeung
Xinwen Hou
Cheng-Lin Liu

The Gaussian process latent variable model (GPLVM) is an unsupervised probabilistic model for nonlinear dimensionality reduction. A supervised extension, called discriminative GPLVM (DGPLVM), incorporates supervisory information into GPLVM to enhance the classification performance. However, its limitation of the latent space dimensionality to at most C − 1 (C is the number of classes) leads to unsatisfactorily performance when the intrinsic dimensionality of the application is higher than C − 1. In this paper, we propose a novel supervised extension of GPLVM, called Gaussian process latent random field (GPLRF), by enforcing the latent variables to be a Gaussian Markov random field with respect to a graph constructed from the supervisory information. In GPLRF, the dimensionality of the latent space is no longer restricted to at most C − 1. This makes GPLRF much more flexible than DGPLVM in applications. Experiments conducted on both synthetic and real-world data sets demonstrate that GPLRF performs comparably with DGPLVM and other state-ofthe-art methods on data sets with intrinsic dimensionality at most C − 1, and dramatically outperforms DG- PLVM on data sets when the intrinsic dimensionality exceeds C − 1.

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