2023-11-09 更新
Learning to Learn for Few-shot Continual Active Learning
Authors:Stella Ho, Ming Liu, Shang Gao, Longxiang Gao
Continual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in CL are mostly confined to a supervised learning setting, especially in NLP domain. In this work, we consider a few-shot continual active learning (CAL) setting where labeled data is inadequate, and unlabeled data is abundant but with a limited annotation budget. We propose a simple but efficient method, called Meta-Continual Active Learning. Specifically, we employ meta-learning and experience replay to address the trade-off between stability and plasticity. As a result, it finds an optimal initialization that efficiently utilizes annotated information for fast adaptation while preventing catastrophic forgetting of past tasks. We conduct extensive experiments to validate the effectiveness of the proposed method and analyze the effect of various active learning strategies and memory sample selection methods in a few-shot CAL setup. Our experiment results demonstrate that random sampling is the best default strategy for both active learning and memory sample selection to solve few-shot CAL problems.
PDF
点此查看论文截图
PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning
Authors:Hao Liu, Jinrui Gan, Xiaoxuan Fan, Yi Zhang, Chuanxian Luo, Jing Zhang, Guangxin Jiang, Yucheng Qian, Changwei Zhao, Huan Ma, Zhenyu Guo
Self-supervised learning has been actively studied in time series domain recently, especially for masked reconstruction. Most of these methods follow the “Pre-training + Fine-tuning” paradigm in which a new decoder replaces the pre-trained decoder to fit for a specific downstream task, leading to inconsistency of upstream and downstream tasks. In this paper, we first point out that the unification of task objectives and adaptation for task difficulty are critical for bridging the gap between time series masked reconstruction and forecasting. By reserving the pre-trained mask token during fine-tuning stage, the forecasting task can be taken as a special case of masked reconstruction, where the future values are masked and reconstructed based on history values. It guarantees the consistency of task objectives but there is still a gap in task difficulty. Because masked reconstruction can utilize contextual information while forecasting can only use historical information to reconstruct. To further mitigate the existed gap, we propose a simple yet effective prompt token tuning (PT-Tuning) paradigm, in which all pre-trained parameters are frozen and only a few trainable prompt tokens are added to extended mask tokens in element-wise manner. Extensive experiments on real-world datasets demonstrate the superiority of our proposed paradigm with state-of-the-art performance compared to representation learning and end-to-end supervised forecasting methods.
PDF
点此查看论文截图
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
Authors:Cheng Cheng, Lin Song, Ruoyi Xue, Hang Wang, Hongbin Sun, Yixiao Ge, Ying Shan
The contrastive vision-language pre-training, known as CLIP, demonstrates remarkable potential in perceiving open-world visual concepts, enabling effective zero-shot image recognition. Nevertheless, few-shot learning methods based on CLIP typically require offline fine-tuning of the parameters on few-shot samples, resulting in longer inference time and the risk of over-fitting in certain domains. To tackle these challenges, we propose the Meta-Adapter, a lightweight residual-style adapter, to refine the CLIP features guided by the few-shot samples in an online manner. With a few training samples, our method can enable effective few-shot learning capabilities and generalize to unseen data or tasks without additional fine-tuning, achieving competitive performance and high efficiency. Without bells and whistles, our approach outperforms the state-of-the-art online few-shot learning method by an average of 3.6\% on eight image classification datasets with higher inference speed. Furthermore, our model is simple and flexible, serving as a plug-and-play module directly applicable to downstream tasks. Without further fine-tuning, Meta-Adapter obtains notable performance improvements in open-vocabulary object detection and segmentation tasks.
PDF Accepted by NeurIPS 2023
点此查看论文截图
A Comparative Study of Knowledge Transfer Methods for Misaligned Urban Building Labels
Authors:Bipul Neupane, Jagannath Aryal, Abbas Rajabifard
Misalignment in Earth observation (EO) images and building labels impact the training of accurate convolutional neural networks (CNNs) for semantic segmentation of building footprints. Recently, three Teacher-Student knowledge transfer methods have been introduced to address this issue: supervised domain adaptation (SDA), knowledge distillation (KD), and deep mutual learning (DML). However, these methods are merely studied for different urban buildings (low-rise, mid-rise, high-rise, and skyscrapers), where misalignment increases with building height and spatial resolution. In this study, we present a workflow for the systematic comparative study of the three methods. The workflow first identifies the best (with the highest evaluation scores) hyperparameters, lightweight CNNs for the Student (among 43 CNNs from Computer Vision), and encoder-decoder networks (EDNs) for both Teachers and Students. Secondly, three building footprint datasets are developed to train and evaluate the identified Teachers and Students in the three transfer methods. The results show that U-Net with VGG19 (U-VGG19) is the best Teacher, and U-EfficientNetv2B3 and U-EfficientNet-lite0 are among the best Students. With these Teacher-Student pairs, SDA could yield upto 0.943, 0.868, 0.912, and 0.697 F1 scores in the low-rise, mid-rise, high-rise, and skyscrapers respectively. KD and DML provide model compression of upto 82%, despite marginal loss in performance. This new comparison concludes that SDA is the most effective method to address the misalignment problem, while KD and DML can efficiently compress network size without significant loss in performance. The 158 experiments and datasets developed in this study will be valuable to minimise the misaligned labels.
PDF This work has been submitted to Elsevier for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
点此查看论文截图
Retargeting video with an end-to-end framework
Authors:Thi-Ngoc-Hanh Le, HuiGuang Huang, Yi-Ru Chen, Tong-Yee Lee
Video holds significance in computer graphics applications. Because of the heterogeneous of digital devices, retargeting videos becomes an essential function to enhance user viewing experience in such applications. In the research of video retargeting, preserving the relevant visual content in videos, avoiding flicking, and processing time are the vital challenges. Extending image retargeting techniques to the video domain is challenging due to the high running time. Prior work of video retargeting mainly utilizes time-consuming preprocessing to analyze frames. Plus, being tolerant of different video content, avoiding important objects from shrinking, and the ability to play with arbitrary ratios are the limitations that need to be resolved in these systems requiring investigation. In this paper, we present an end-to-end RETVI method to retarget videos to arbitrary aspect ratios. We eliminate the computational bottleneck in the conventional approaches by designing RETVI with two modules, content feature analyzer (CFA) and adaptive deforming estimator (ADE). The extensive experiments and evaluations show that our system outperforms previous work in quality and running time. Visit our project website for more results at $\href{http://graphics.csie.ncku.edu.tw/RETVI}{http://graphics.csie.ncku.edu.tw/RETVI}$.
PDF This paper has been accepted for publication on IEEE Transactions on Visualization and Computer Graphics, October 2023
点此查看论文截图
Robust and Communication-Efficient Federated Domain Adaptation via Random Features
Authors:Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert. C. Qiu, Zhenyu Liao
Modern machine learning (ML) models have grown to a scale where training them on a single machine becomes impractical. As a result, there is a growing trend to leverage federated learning (FL) techniques to train large ML models in a distributed and collaborative manner. These models, however, when deployed on new devices, might struggle to generalize well due to domain shifts. In this context, federated domain adaptation (FDA) emerges as a powerful approach to address this challenge. Most existing FDA approaches typically focus on aligning the distributions between source and target domains by minimizing their (e.g., MMD) distance. Such strategies, however, inevitably introduce high communication overheads and can be highly sensitive to network reliability. In this paper, we introduce RF-TCA, an enhancement to the standard Transfer Component Analysis approach that significantly accelerates computation without compromising theoretical and empirical performance. Leveraging the computational advantage of RF-TCA, we further extend it to FDA setting with FedRF-TCA. The proposed FedRF-TCA protocol boasts communication complexity that is \emph{independent} of the sample size, while maintaining performance that is either comparable to or even surpasses state-of-the-art FDA methods. We present extensive experiments to showcase the superior performance and robustness (to network condition) of FedRF-TCA.
PDF 21 pages
点此查看论文截图
Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning
Authors:Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, Mohsen Ali
Deep learning based object detectors struggle generalizing to a new target domain bearing significant variations in object and background. Most current methods align domains by using image or instance-level adversarial feature alignment. This often suffers due to unwanted background and lacks class-specific alignment. A straightforward approach to promote class-level alignment is to use high confidence predictions on unlabeled domain as pseudo-labels. These predictions are often noisy since model is poorly calibrated under domain shift. In this paper, we propose to leverage model’s predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment. We develop a technique to quantify predictive uncertainty on class assignments and bounding-box predictions. Model predictions with low uncertainty are used to generate pseudo-labels for self-training, whereas the ones with higher uncertainty are used to generate tiles for adversarial feature alignment. This synergy between tiling around uncertain object regions and generating pseudo-labels from highly certain object regions allows capturing both image and instance-level context during the model adaptation. We report thorough ablation study to reveal the impact of different components in our approach. Results on five diverse and challenging adaptation scenarios show that our approach outperforms existing state-of-the-art methods with noticeable margins.
PDF Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: 45, Issue: 12, December 2023). arXiv admin note: substantial text overlap with arXiv:2110.00249
点此查看论文截图