2022-09-24 更新
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
Authors:Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
Inspired by humans’ remarkable ability to master arithmetic and generalize to unseen problems, we present a new dataset, HINT, to study machines’ capability of learning generalizable concepts at three levels: perception, syntax, and semantics. Learning agents are tasked to reckon how concepts are perceived from raw signals such as images (i.e., perception), how multiple concepts are structurally combined to form a valid expression (i.e., syntax), and how concepts are realized to afford various reasoning tasks (i.e., semantics), all in a weakly supervised manner. With a focus on systematic generalization, we carefully design a five-fold test set to evaluate both the interpolation and the extrapolation of learned concepts w.r.t. the three levels. We further design a few-shot learning split to test whether models could quickly learn new concepts and generalize them to more complex scenarios. To understand existing models’ limitations, we conduct extensive experiments with various sequence-to-sequence models, including RNNs, Transformers, and GPT-3 (with the chain of thought prompting). The results suggest that current models still struggle in extrapolation to long-range syntactic dependency and semantics. Models show a significant gap toward human-level generalization when tested with new concepts in a few-shot setting. Moreover, we find that it is infeasible to solve HINT by simply scaling up the dataset and the model size; this strategy barely helps the extrapolation over syntax and semantics. Finally, in zero-shot GPT-3 experiments, the chain of thought prompting shows impressive results and significantly boosts the test accuracy. We believe the proposed dataset together with the experimental findings is of great interest to the community on systematic generalization.
PDF website: https://liqing-ustc.github.io/HINT
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ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Authors:Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun
Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention. Recent studies on CD-FSL generally focus on transfer learning based approaches, where a neural network is pre-trained on popular labeled source domain datasets and then transferred to target domain data. Although the labeled datasets may provide suitable initial parameters for the target data, the domain difference between the source and target might hinder fine-tuning on the target domain. This paper proposes a simple yet powerful method that re-randomizes the parameters fitted on the source domain before adapting to the target data. The re-randomization resets source-specific parameters of the source pre-trained model and thus facilitates fine-tuning on the target domain, improving few-shot performance.
PDF CIKM 2022 Short; 5 pages, 3 figures, 4 tables
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A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts
Authors:Giuseppe De Gregorio, Sanket Biswas, Mohamed Ali Souibgui, Asma Bensalah, Josep Lladós, Alicia Fornés, Angelo Marcelli
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction.
PDF Accepted in ICFHR 2022
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AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection
Authors:Yipeng Gao, Lingxiao Yang, Yunmu Huang, Song Xie, Shiyong Li, Wei-shi Zheng
Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data. There exists two significant challenges: (1) Highly insufficient target domain data; (2) Potential over-adaptation and misleading caused by inappropriately amplified target samples without any restriction. To address these challenges, we propose an adaptive method consisting of two parts. First, we propose an adaptive optimization strategy to select augmented data similar to target samples rather than blindly increasing the amount. Specifically, we filter the augmented candidates which significantly deviate from the target feature distribution in the very beginning. Second, to further relieve the data limitation, we propose the multi-level domain-aware data augmentation to increase the diversity and rationality of augmented data, which exploits the cross-image foreground-background mixture. Experiments show that the proposed method achieves state-of-the-art performance on multiple benchmarks.
PDF Accepted in ECCV 2022
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A Versatile Agent for Fast Learning from Human Instructors
Authors:Yiwen Chen, Zedong Zhang, Haofeng Liu, Jiayi Tan, Chee-Meng Chew, Marcelo Ang
In recent years, a myriad of superlative works on intelligent robotics policies have been done, thanks to advances in machine learning. However, inefficiency and lack of transfer ability hindered algorithms from pragmatic applications, especially in human-robot collaboration, when few-shot fast learning and high flexibility become a wherewithal. To surmount this obstacle, we refer to a “Policy Pool”, containing pre-trained skills that can be easily accessed and reused. An agent is employed to govern the “Policy Pool” by unfolding requisite skills in a flexible sequence, contingent on task specific predilection. This predilection can be automatically interpreted from one or few human expert demonstrations. Under this hierarchical setting, our algorithm is able to pick up a sparse-reward, multi-stage knack with only one demonstration in a Mini-Grid environment, showing the potential for instantly mastering complex robotics skills from human instructors. Additionally, the innate quality of our algorithm also allows for lifelong learning, making it a versatile agent.
PDF
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Query-Guided Networks for Few-shot Fine-grained Classification and Person Search
Authors:Bharti Munjal, Alessandro Flaborea, Sikandar Amin, Federico Tombari, Fabio Galasso
Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated them separately. But a closer look unveils important similarities: both tasks target categories that can only be discriminated by specific object details; and the relevant models should generalize to new categories, not seen during training. We propose a novel unified Query-Guided Network (QGN) applicable to both tasks. QGN consists of a Query-guided Siamese-Squeeze-and-Excitation subnetwork which re-weights both the query and gallery features across all network layers, a Query-guided Region Proposal subnetwork for query-specific localisation, and a Query-guided Similarity subnetwork for metric learning. QGN improves on a few recent few-shot fine-grained datasets, outperforming other techniques on CUB by a large margin. QGN also performs competitively on the person search CUHK-SYSU and PRW datasets, where we perform in-depth analysis.
PDF Accepted at Pattern Recognition Journal 2022
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Few-Shot Object Detection in Unseen Domains
Authors:Karim Guirguis, George Eskandar, Matthias Kayser, Bin Yang, Juergen Beyerer
Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data by transferring knowledge gained on abundant base classes. FSOD approaches commonly assume that both the scarcely provided examples of novel classes and test-time data belong to the same domain. However, this assumption does not hold in various industrial and robotics applications, where a model can learn novel classes from a source domain while inferring on classes from a target domain. In this work, we address the task of zero-shot domain adaptation, also known as domain generalization, for FSOD. Specifically, we assume that neither images nor labels of the novel classes in the target domain are available during training. Our approach for solving the domain gap is two-fold. First, we leverage a meta-training paradigm, where we learn the domain shift on the base classes, then transfer the domain knowledge to the novel classes. Second, we propose various data augmentations techniques on the few shots of novel classes to account for all possible domain-specific information. To constraint the network into encoding domain-agnostic class-specific representations only, a contrastive loss is proposed to maximize the mutual information between foreground proposals and class embeddings and reduce the network’s bias to the background information from target domain. Our experiments on the T-LESS, PASCAL-VOC, and ExDark datasets show that the proposed approach succeeds in alleviating the domain gap considerably without utilizing labels or images of novel categories from the target domain.
PDF
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AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization
Authors:Dazhuo Wang, Jianfei Yang, Wei Cui, Lihua Xie, Sumei Sun
WiFi-based smart human sensing technology enabled by Channel State Information (CSI) has received great attention in recent years. However, CSI-based sensing systems suffer from performance degradation when deployed in different environments. Existing works solve this problem by domain adaptation using massive unlabeled high-quality data from the new environment, which is usually unavailable in practice. In this paper, we propose a novel augmented environment-invariant robust WiFi gesture recognition system named AirFi that deals with the issue of environment dependency from a new perspective. The AirFi is a novel domain generalization framework that learns the critical part of CSI regardless of different environments and generalizes the model to unseen scenarios, which does not require collecting any data for adaptation to the new environment. AirFi extracts the common features from several training environment settings and minimizes the distribution differences among them. The feature is further augmented to be more robust to environments. Moreover, the system can be further improved by few-shot learning techniques. Compared to state-of-the-art methods, AirFi is able to work in different environment settings without acquiring any CSI data from the new environment. The experimental results demonstrate that our system remains robust in the new environment and outperforms the compared systems.
PDF 11 pages, 3 figures, paper under review
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A Few-shot Approach to Resume Information Extraction via Prompts
Authors:Chengguang Gan, Tatsunori Mori
Prompt learning has been shown to achieve near-Fine-tune performance in most text classification tasks with very few training examples. It is advantageous for NLP tasks where samples are scarce. In this paper, we attempt to apply it to a practical scenario, i.e resume information extraction, and to enhance the existing method to make it more applicable to the resume information extraction task. In particular, we created multiple sets of manual templates and verbalizers based on the textual characteristics of resumes. In addition, we compared the performance of Masked Language Model (MLM) pre-training language models (PLMs) and Seq2Seq PLMs on this task. Furthermore, we improve the design method of verbalizer for Knowledgeable Prompt-tuning in order to provide a example for the design of Prompt templates and verbalizer for other application-based NLP tasks. In this case, we propose the concept of Manual Knowledgeable Verbalizer(MKV). A rule for constructing the Knowledgeable Verbalizer corresponding to the application scenario. Experiments demonstrate that templates and verbalizers designed based on our rules are more effective and robust than existing manual templates and automatically generated prompt methods. It is established that the currently available automatic prompt methods cannot compete with manually designed prompt templates for some realistic task scenarios. The results of the final confusion matrix indicate that our proposed MKV significantly resolved the sample imbalance issue.
PDF
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SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task
Authors:Jihyun Lee, Gary Geunbae Lee
Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data. In this paper, we introduce an ontology-free few-shot DST with self-feeding belief state input. The self-feeding belief state input increases the accuracy in multi-turn dialogue by summarizing previous dialogue. Also, we newly developed a slot-gate auxiliary task. This new auxiliary task helps classify whether a slot is mentioned in the dialogue. Our model achieved the best score in a few-shot setting for four domains on multiWOZ 2.0.
PDF Accepted in INTERSPEECH 2022
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