2022-09-14 更新
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition
Authors:Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, Yue Zhang
Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model’s generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of cross-domain NER tasks compared to previous state-of-the-art methods, including the data augmentation and prompt-tuning methods. Our codes are available at https://github.com/lifan-yuan/FactMix.
PDF Accepted by COLING 2022, oral paper
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Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification
Authors:Tianyi Lei, Honghui Hu, Qiaoyang Luo, Dezhong Peng, Xu Wang
Few-shot text classification aims to classify the text under the few-shot scenario. Most of the previous methods adopt optimization-based meta learning to obtain task distribution. However, due to the neglect of matching between the few amount of samples and complicated models, as well as the distinction between useful and useless task features, these methods suffer from the overfitting issue. To address this issue, we propose a novel Adaptive Meta-learner via Gradient Similarity (AMGS) method to improve the model generalization ability to a new task. Specifically, the proposed AMGS alleviates the overfitting based on two aspects: (i) acquiring the potential semantic representation of samples and improving model generalization through the self-supervised auxiliary task in the inner loop, (ii) leveraging the adaptive meta-learner via gradient similarity to add constraints on the gradient obtained by base-learner in the outer loop. Moreover, we make a systematic analysis of the influence of regularization on the entire framework. Experimental results on several benchmarks demonstrate that the proposed AMGS consistently improves few-shot text classification performance compared with the state-of-the-art optimization-based meta-learning approaches.
PDF COLING 2022
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Few-shot Object Counting with Similarity-Aware Feature Enhancement
Authors:Zhiyuan You, Kai Yang, Wenhan Luo, Xin Lu, Lei Cui, Xinyi Le
This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query image. The major challenge lies in that the target objects can be densely packed in the query image, making it hard to recognize every single one. To tackle the obstacle, we propose a novel learning block, equipped with a similarity comparison module and a feature enhancement module. Concretely, given a support image and a query image, we first derive a score map by comparing their projected features at every spatial position. The score maps regarding all support images are collected together and normalized across both the exemplar dimension and the spatial dimensions, producing a reliable similarity map. We then enhance the query feature with the support features by employing the developed point-wise similarities as the weighting coefficients. Such a design encourages the model to inspect the query image by focusing more on the regions akin to the support images, leading to much clearer boundaries between different objects. Extensive experiments on various benchmarks and training setups suggest that we surpass the state-of-the-art methods by a sufficiently large margin. For instance, on a recent large-scale FSC-147 dataset, we surpass the state-of-the-art method by improving the mean absolute error from 22.08 to 14.32 (35%$\uparrow$). Code has been released in https://github.com/zhiyuanyou/SAFECount.
PDF Accepted by WACV 2023
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OmDet: Language-Aware Object Detection with Large-scale Vision-Language Multi-dataset Pre-training
Authors:Tiancheng Zhao, Peng Liu, Xiaopeng Lu, Kyusong Lee
Advancing object detection to open-vocabulary and few-shot transfer has long been a challenge for computer vision research. This work explores a continual learning approach that enables a detector to expand its zero/few-shot capabilities via multi-dataset vision-language pre-training. Using natural language as knowledge representation, we explore methods to accumulate “visual vocabulary” from different training datasets and unify the task as a language-conditioned detection framework. Specifically, we propose a novel language-aware detector OmDet and a novel training mechanism. The proposed multimodal detection network can resolve the technical challenges in multi-dataset joint training and it can generalize to arbitrary number of training datasets without the requirements for manual label taxonomy merging. Experiment results on COCO, Pascal VOC, and Wider Face/Pedestrian confirmed the efficacy by achieving on par or higher scores in joint training compared to training separately. Moreover, we pre-train on more than 20 million images with 4 million unique object vocabulary, and the resulting model is evaluated on 35 downstream tasks of ODinW. Results show that OmDet is able to achieve the state-of-the-art fine-tuned performance on ODinW. And analysis shows that by scaling up the proposed pre-training method, OmDet continues to improve its zero/few-shot tuning performance, suggesting a promising way for further scaling.
PDF
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FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing
Authors:Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, Jing Liao
We propose a Few-shot Dynamic Neural Radiance Field (FDNeRF), the first NeRF-based method capable of reconstruction and expression editing of 3D faces based on a small number of dynamic images. Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs. Compared to state-of-the-art few-shot NeRFs designed for modeling static scenes, the proposed FDNeRF accepts view-inconsistent dynamic inputs and supports arbitrary facial expression editing, i.e., producing faces with novel expressions beyond the input ones. To handle the inconsistencies between dynamic inputs, we introduce a well-designed conditional feature warping (CFW) module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. As a result, features of different expressions are transformed into the target ones. We then construct a radiance field based on these view-consistent features and use volumetric rendering to synthesize novel views of the modeled faces. Extensive experiments with quantitative and qualitative evaluation demonstrate that our method outperforms existing dynamic and few-shot NeRFs on both 3D face reconstruction and expression editing tasks. Code is available at https://github.com/FDNeRF/FDNeRF.
PDF Accepted at SIGGRAPH Asia 2022. Project page: https://fdnerf.github.io
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Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
Authors:Han Wu, Bala Rajaratnam, Jie Yin, Jianyuan Guo
Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been proposed as a solution to alleviate incompleteness and expand the coverage of KGs. It aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have mostly focused on designing local neighbor aggregators to learn entity-level information and/or imposing sequential dependency assumption at the triplet level to learn meta relation information. However, valuable pairwise triplet-level interactions and context-level relational information have been largely overlooked for learning meta representations of few-shot relations. In this paper, we propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize very well to new unseen relations. Extensive experiments on two benchmark datasets validate the superiority of HiRe against other state-of-the-art methods.
PDF 10 pages, 5 figures
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