2023-05-10 更新
AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain
Authors:Jun Kataoka, Hyunsoo Yoon
This paper presents an unsupervised domain adaptation (UDA) method for predicting unlabeled target domain data, specific to complex UDA tasks where the domain gap is significant. Mainstream UDA models aim to learn from both domains and improve target discrimination by utilizing labeled source domain data. However, the performance boost may be limited when the discrepancy between the source and target domains is large or the target domain contains outliers. To explicitly address this issue, we propose the Adversarial self-superVised domain Adaptation network for the TARget domain (AVATAR) algorithm. It outperforms state-of-the-art UDA models by concurrently reducing domain discrepancy while enhancing discrimination through domain adversarial learning, self-supervised learning, and sample selection strategy for the target domain, all guided by deep clustering. Our proposed model significantly outperforms state-of-the-art methods on three UDA benchmarks, and extensive ablation studies and experiments demonstrate the effectiveness of our approach for addressing complex UDA tasks.
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Few-shot Domain-Adaptive Visually-fused Event Detection from Text
Authors:Fatemeh Shiri, Farhad Moghimifar, Van Nguyen, Reza Haffari, Yuan-Fang Li
Incorporating auxiliary modalities such as images into event detection models has attracted increasing interest over the last few years. The complexity of natural language in describing situations has motivated researchers to leverage the related visual context to improve event detection performance. However, current approaches in this area suffer from data scarcity, where a large amount of labelled text-image pairs are required for model training. Furthermore, limited access to the visual context at inference time negatively impacts the performance of such models, which makes them practically ineffective in real-world scenarios. In this paper, we present a novel domain-adaptive visually-fused event detection approach that can be trained on a few labelled image-text paired data points. Specifically, we introduce a visual imaginator method that synthesises images from text in the absence of visual context. Moreover, the imaginator can be customised to a specific domain. In doing so, our model can leverage the capabilities of pre-trained vision-language models and can be trained in a few-shot setting. This also allows for effective inference where only single-modality data (i.e. text) is available. The experimental evaluation on the benchmark M2E2 dataset shows that our model outperforms existing state-of-the-art models, by up to 11 points.
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DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
Authors:Yunze Man, Liang-Yan Gui, Yu-Xiong Wang
Closing the domain gap between training and deployment and incorporating multiple sensor modalities are two challenging yet critical topics for self-driving. Existing work only focuses on single one of the above topics, overlooking the simultaneous domain and modality shift which pervasively exists in real-world scenarios. A model trained with multi-sensor data collected in Europe may need to run in Asia with a subset of input sensors available. In this work, we propose DualCross, a cross-modality cross-domain adaptation framework to facilitate the learning of a more robust monocular bird’s-eye-view (BEV) perception model, which transfers the point cloud knowledge from a LiDAR sensor in one domain during the training phase to the camera-only testing scenario in a different domain. This work results in the first open analysis of cross-domain cross-sensor perception and adaptation for monocular 3D tasks in the wild. We benchmark our approach on large-scale datasets under a wide range of domain shifts and show state-of-the-art results against various baselines.
PDF Preprint. Project website: https://yunzeman.github.io/DualCross
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Harnessing the Power of BERT in the Turkish Clinical Domain: Pretraining Approaches for Limited Data Scenarios
Authors:Hazal Türkmen, Oğuz Dikenelli, Cenk Eraslan, Mehmet Cem Çallı, Süha Süreyya Özbek
In recent years, major advancements in natural language processing (NLP) have been driven by the emergence of large language models (LLMs), which have significantly revolutionized research and development within the field. Building upon this progress, our study delves into the effects of various pre-training methodologies on Turkish clinical language models’ performance in a multi-label classification task involving radiology reports, with a focus on addressing the challenges posed by limited language resources. Additionally, we evaluated the simultaneous pretraining approach by utilizing limited clinical task data for the first time. We developed four models, including TurkRadBERT-task v1, TurkRadBERT-task v2, TurkRadBERT-sim v1, and TurkRadBERT-sim v2. Our findings indicate that the general Turkish BERT model (BERTurk) and TurkRadBERT-task v1, both of which utilize knowledge from a substantial general-domain corpus, demonstrate the best overall performance. Although the task-adaptive pre-training approach has the potential to capture domain-specific patterns, it is constrained by the limited task-specific corpus and may be susceptible to overfitting. Furthermore, our results underscore the significance of domain-specific vocabulary during pre-training for enhancing model performance. Ultimately, we observe that the combination of general-domain knowledge and task-specific fine-tuning is essential for achieving optimal performance across a range of categories. This study offers valuable insights for developing effective Turkish clinical language models and can guide future research on pre-training techniques for other low-resource languages within the clinical domain.
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Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages
Authors:Zhong Zhou, Jan Niehues, Alex Waibel
In many humanitarian scenarios, translation into severely low resource languages often does not require a universal translation engine, but a dedicated text-specific translation engine. For example, healthcare records, hygienic procedures, government communication, emergency procedures and religious texts are all limited texts. While generic translation engines for all languages do not exist, translation of multilingually known limited texts into new, endangered languages may be possible and reduce human translation effort. We attempt to leverage translation resources from many rich resource languages to efficiently produce best possible translation quality for a well known text, which is available in multiple languages, in a new, severely low resource language. We examine two approaches: 1. best selection of seed sentences to jump start translations in a new language in view of best generalization to the remainder of a larger targeted text(s), and 2. we adapt large general multilingual translation engines from many other languages to focus on a specific text in a new, unknown language. We find that adapting large pretrained multilingual models to the domain/text first and then to the severely low resource language works best. If we also select a best set of seed sentences, we can improve average chrF performance on new test languages from a baseline of 21.9 to 50.7, while reducing the number of seed sentences to only around 1,000 in the new, unknown language.
PDF In Proceedings of the 6th Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT) of the 17th Conference of the European Chapter of the Association for Computational Linguistic in 2023
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Neural Voting Field for Camera-Space 3D Hand Pose Estimation
Authors:Lin Huang, Chung-Ching Lin, Kevin Lin, Lin Liang, Lijuan Wang, Junsong Yuan, Zicheng Liu
We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation. As opposed to recent works, most of which first adopt holistic or pixel-level dense regression to obtain relative 3D hand pose and then follow with complex second-stage operations for 3D global root or scale recovery, we propose a novel unified 3D dense regression scheme to estimate camera-space 3D hand pose via dense 3D point-wise voting in camera frustum. Through direct dense modeling in 3D domain inspired by Pixel-aligned Implicit Functions for 3D detailed reconstruction, our proposed Neural Voting Field (NVF) fully models 3D dense local evidence and hand global geometry, helping to alleviate common 2D-to-3D ambiguities. Specifically, for a 3D query point in camera frustum and its pixel-aligned image feature, NVF, represented by a Multi-Layer Perceptron, regresses: (i) its signed distance to the hand surface; (ii) a set of 4D offset vectors (1D voting weight and 3D directional vector to each hand joint). Following a vote-casting scheme, 4D offset vectors from near-surface points are selected to calculate the 3D hand joint coordinates by a weighted average. Experiments demonstrate that NVF outperforms existing state-of-the-art algorithms on FreiHAND dataset for camera-space 3D hand pose estimation. We also adapt NVF to the classic task of root-relative 3D hand pose estimation, for which NVF also obtains state-of-the-art results on HO3D dataset.
PDF Accepted to CVPR 2023
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Generalized Universal Domain Adaptation with Generative Flow Networks
Authors:Didi Zhu, Yinchuan Li, Yunfeng Shao, Jianye Hao, Fei Wu, Kun Kuang, Jun Xiao, Chao Wu
We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and label space mismatch-based variants, essentially categorizing them as a unified problem, guiding to a comprehensive framework for thoroughly solving all the variants. The key challenge of GUDA is developing and identifying novel target categories while estimating the target label distribution. To address this problem, we take advantage of the powerful exploration capability of generative flow networks and propose an active domain adaptation algorithm named GFlowDA, which selects diverse samples with probabilities proportional to a reward function. To enhance the exploration capability and effectively perceive the target label distribution, we tailor the states and rewards, and introduce an efficient solution for parent exploration and state transition. We also propose a training paradigm for GUDA called Generalized Universal Adversarial Network (GUAN), which involves collaborative optimization between GUAN and GFlowNet. Theoretical analysis highlights the importance of exploration, and extensive experiments on benchmark datasets demonstrate the superiority of GFlowDA.
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SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation
Authors:Ayan Banerjee, Sanket Biswas, Josep Lladós, Umapada Pal
Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapts a contrastive training with a mixed query selection for anchor initialization in the decoder. Later on, it performs a dot product between the obtained query embeddings and the pixel embedding map (coming from the encoder) for semantic reasoning. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ), and TableBank demonstrate that our model with SwinL backbone achieves better segmentation performance than the existing state-of-the-art approaches with the average precision of \textbf{93.72}, \textbf{54.39}, \textbf{84.65} and \textbf{98.04} respectively under one billion parameters. The code is made publicly available at: \href{https://github.com/ayanban011/SwinDocSegmenter}{github.com/ayanban011/SwinDocSegmenter}
PDF Accepted to ICDAR 2023 (San Jose, California)
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Target-driven One-Shot Unsupervised Domain Adaptation
Authors:Julio Ivan Davila Carrazco, Suvarna Kishorkumar Kadam, Pietro Morerio, Alessio Del Bue, Vittorio Murino
In this paper, we introduce a novel framework for the challenging problem of One-Shot Unsupervised Domain Adaptation (OSUDA), which aims to adapt to a target domain with only a single unlabeled target sample. Unlike existing approaches that rely on large labeled source and unlabeled target data, our Target-driven One-Shot UDA (TOS-UDA) approach employs a learnable augmentation strategy guided by the target sample’s style to align the source distribution with the target distribution. Our method consists of three modules: an augmentation module, a style alignment module, and a classifier. Unlike existing methods, our augmentation module allows for strong transformations of the source samples, and the style of the single target sample available is exploited to guide the augmentation by ensuring perceptual similarity. Furthermore, our approach integrates augmentation with style alignment, eliminating the need for separate pre-training on additional datasets. Our method outperforms or performs comparably to existing OS-UDA methods on the Digits and DomainNet benchmarks.
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Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval
Authors:Shiyin Dong, Mingrui Zhu, Nannan Wang, Heng Yang, Xinbo Gao
Zero-shot sketch-based image retrieval (ZS-SBIR) is challenging due to the cross-domain nature of sketches and photos, as well as the semantic gap between seen and unseen image distributions. Previous methods fine-tune pre-trained models with various side information and learning strategies to learn a compact feature space that (\romannumeral1) is shared between the sketch and photo domains and (\romannumeral2) bridges seen and unseen classes. However, these efforts are inadequate in adapting domains and transferring knowledge from seen to unseen classes. In this paper, we present an effective \emph{``Adapt and Align’’} approach to address the key challenges. Specifically, we insert simple and lightweight domain adapters to learn new abstract concepts of the sketch domain and improve cross-domain representation capabilities. Inspired by recent advances in image-text foundation models (\textit{e.g.}, CLIP) on zero-shot scenarios, we explicitly align the learned image embedding with a more semantic text embedding to achieve the desired knowledge transfer from seen to unseen classes. Extensive experiments on three benchmark datasets and two popular backbones demonstrate the superiority of our method in terms of retrieval accuracy and flexibility.
PDF 13 pages, 8 figures, 6 tables
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Going beyond research datasets: Novel intent discovery in the industry setting
Authors:Aleksandra Chrabrowa, Tsimur Hadeliya, Dariusz Kajtoch, Robert Mroczkowski, Piotr Rybak
Novel intent discovery automates the process of grouping similar messages (questions) to identify previously unknown intents. However, current research focuses on publicly available datasets which have only the question field and significantly differ from real-life datasets. This paper proposes methods to improve the intent discovery pipeline deployed in a large e-commerce platform. We show the benefit of pre-training language models on in-domain data: both self-supervised and with weak supervision. We also devise the best method to utilize the conversational structure (i.e., question and answer) of real-life datasets during fine-tuning for clustering tasks, which we call Conv. All our methods combined to fully utilize real-life datasets give up to 33pp performance boost over state-of-the-art Constrained Deep Adaptive Clustering (CDAC) model for question only. By comparison CDAC model for the question data only gives only up to 13pp performance boost over the naive baseline.
PDF Accepted to Findings of EACL 2023
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Fashion CUT: Unsupervised domain adaptation for visual pattern classification in clothes using synthetic data and pseudo-labels
Authors:Enric Moreu, Alex Martinelli, Martina Naughton, Philip Kelly, Noel E. O’Connor
Accurate product information is critical for e-commerce stores to allow customers to browse, filter, and search for products. Product data quality is affected by missing or incorrect information resulting in poor customer experience. While machine learning can be used to correct inaccurate or missing information, achieving high performance on fashion image classification tasks requires large amounts of annotated data, but it is expensive to generate due to labeling costs. One solution can be to generate synthetic data which requires no manual labeling. However, training a model with a dataset of solely synthetic images can lead to poor generalization when performing inference on real-world data because of the domain shift. We introduce a new unsupervised domain adaptation technique that converts images from the synthetic domain into the real-world domain. Our approach combines a generative neural network and a classifier that are jointly trained to produce realistic images while preserving the synthetic label information. We found that using real-world pseudo-labels during training helps the classifier to generalize in the real-world domain, reducing the synthetic bias. We successfully train a visual pattern classification model in the fashion domain without real-world annotations. Experiments show that our method outperforms other unsupervised domain adaptation algorithms.
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