Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks. Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification. In our research, we focused on two tasks related to vegetation. The first one is plant disease detection on 25 classes of five crops (grape, cotton, wheat, cucumbers, and corn). This task is motivated because harvest losses due to diseases is a serious problem for both large farming structures and rural families. The second task is the identification of moss species (5 classes). Mosses are natural bioaccumulators of pollutants; therefore, they are used in environmental monitoring programs. The identification of moss species is an important step in the sample preprocessing. In both tasks, we used self-collected image databases. We tried several deep learning architectures and approaches. Our Siamese network architecture with a triplet loss function and MobileNetV2 as a base network showed the most impressive results in both above-mentioned tasks. The average accuracy for plant disease detection amounted to over 97.8% and 97.6% for moss species classification.
Solving Single-Shot Person Re-Identification (Re-Id) by training Deep Convolutional Neural Networks is a daunting challenge, due to the lack of training data, since only two images per person are available. This causes the overfitting of the models, leading to degenerated performance. This paper formulates the Triplet Permutation method to generate multiple training sets, from a certain re-id dataset. This is a novel strategy for feeding triplet networks, which reduces the overfitting of the Single-Shot Re-Id model. The improved performance has been demonstrated over one of the most challengin…
Huazhong University of Science and Technology（华中科技大学）；Baidu Research（百度研究院）
This paper tackles the Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning. This task has two characteristics: 1) the zero-shot setting requires a metric space with good within-class compactness and the between-class discrepancy for recognizing the novel classes and 2) the sketch query and the photo gallery are in different modalities. The metric learning viewpoint benefits ZS-SBIR from two aspects. First, it facilitates improvement through recent good practices in deep metric learning (DML). By combining two fundamental learning approa…
Shandong University（山东大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；City University of Hong Kong（香港城市大学）；Shandong Jianzhu University（山东建筑大学）
In this paper, we aim to tackle the one-shot person re-identification problem where only one image is labelled for each person, while other images are unlabelled. This task is challenging due to the lack of sufficient labelled training data. To tackle this problem, we propose to iteratively guess pseudo labels for the unlabeled image samples, which are later used to update the re-identification model together with the labelled samples. A new sampling mechanism is designed to select unlabeled samples to pseudo labelled samples based on the distance matrix, and to form a training triplet batch i…
Zero-Shot Sketch-Based Image Retrieval (ZSSBIR) is an emerging task. The pioneering work focused on the modal gap but ignored inter-class information. Although recent work has begun to consider the triplet-based or contrast-based loss to mine inter-class information, positive and negative samples need to be carefully selected, or the model is prone to lose modality-specific information. To respond to these issues, an Ontology-Aware Network (OAN) is proposed. Specifically, the smooth inter-class independence learning mechanism is put forward to maintain inter-class peculiarity. Meanwhile, disti…
Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is propose…
From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人工智能与机器人研究所，西安交通大学）；Harbin Institute of Technology（哈尔滨工业大学）；Ant Group（蚂蚁集团）
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Tencent AI Lab（腾讯人工智能实验室）
The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric le…
Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario. Previous works mostly focus on modeling the correspondence between images and sketches or synthesizing image features with sketch features. However, both of them ignore the large intra-class variance of sketches, thus resulting in unsatisfactory retrieval performance. In this paper, we propose a novel end-to-end semantic adversarial approach for ZS-SBIR. Specifically, we devise a semantic adversarial module to maximize the con…
This paper tackles face recognition in videos employing metric learning methods and similarity ranking models. The paper compares the use of the Siamese network with contrastive loss and Triplet Network with triplet loss implementing the following architectures: Google/Inception architecture, 3D Convolutional Network (C3D), and a 2-D Long short-term memory (LSTM) Recurrent Neural Network. We make use of still images and sequences from videos for training the networks and compare the performances implementing the above architectures. The dataset used was the YouTube Face Database designed for i…
OSRAM GmbH（欧司朗有限公司）；Sapienza University of Rome（罗马智慧大学）
Contrastive losses yield state-of-the-art performance for person re-identification, face verification and few shot learning. They have recently outperformed the cross-entropy loss on classification at the ImageNet scale and outperformed all self-supervision prior results by a large margin (SimCLR). Simple and effective regularization techniques such as label smoothing and self-distillation do not apply anymore, because they act on multinomial label distributions, adopted in cross-entropy losses, and not on tuple comparative terms, which characterize the contrastive losses. Here we propose a no…
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；ICube（ICube实验室）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg（斯特拉斯堡大学医院）
Fudan University（复旦大学）；Shanghai Key Laboratory of Intelligent Information Processing（上海智能信息处理重点实验室）；Academy for Engineering & Technology, Fudan University（复旦大学工程与应用技术研究院）；The University of Hong Kong（香港大学）
This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankML…
UniTriGen: Unified Triplet Generation of Aligned Visible-Infrared-Label for Few-Shot RGB-T Semantic Segmentation
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Computer Science & Technology, Xi’an University of Posts & Telecommunications（西安邮电大学计算机科学与技术学院）；MMLab, The Chinese University of Hong Kong（香港中文大学MMLab）
Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping
TU Dortmund（多特蒙德大学）；University of Bonn（波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；Fraunhofer IML（弗劳恩霍夫物流研究所）
DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval
Institute of Information & Communications Technology Planning & Evaluation (IITP)（信息通信技术规划与评估研究所（IITP））；Department of Artificial Intelligence, Korea University（韩国大学人工智能系）
Data-Efficient Generalization for Zero-shot Composed Image Retrieval
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；School of Information Science and Technology, University of Science and Technology of China, Hefei, China（中国科学技术大学信息科学与技术学院）；Sun Yat-sen University Shenzhen Campus, School of Cyber Science and Technology, Shenzhen, China（中山大学深圳校区计算机科学与技术学院）
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification
Tecnologico de Monterrey, School of Engineering and Sciences（蒙特雷理工学院工程与科学学院）；Centro de Investigación y de Estudios Avanzados（高级研究与调查中心）
KTH Royal Institute of Technology（瑞典皇家理工学院）；Sony Europe B.V.（索尼欧洲公司）
Given the semantic descriptions of classes, Zero-Shot Learning (ZSL) aims to recognize unseen classes without labeled training data by exploiting semantic information, which contains knowledge between seen and unseen classes. Existing ZSL methods mainly use vectors to represent the embeddings to the semantic space. Despite the popularity, such vector representation limits the expressivity in terms of modeling the intra-class variability for each class. We address this issue by leveraging the use of distribution embeddings. More specifically, both image embeddings and class embeddings are model…
Image modality recognition is essential for efficient imaging workflows in current clinical environments, where multiple imaging modalities are used to better comprehend complex diseases. Emerging biomarkers from novel, rare modalities are being developed to aid in such understanding, however the availability of these images is often limited. This scenario raises the necessity of recognising new imaging modalities without them being collected and annotated in large amounts. In this work, we present a few-shot learning model for limited training examples based on Deep Triplet Networks. We show …
Decoupling Endpoint and Semantic Transition Learning for Zero-Shot Composed Image Retrieval
Sun Yat-sen University（中山大学）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing（机器智能与先进计算重点实验室）
Emory University（埃默里大学）；Indiana University Purdue University-Indianapolis（印第安纳大学与普渡大学印第安纳波利斯联合分校）
Deep learning approaches applied to medical imaging have reached near-human or better-than-human performance on many diagnostic tasks. For instance, the CheXpert competition on detecting pathologies in chest x-rays has shown excellent multi-class classification performance. However, training and validating deep learning models require extensive collections of images and still produce false inferences, as identified by a human-in-the-loop. In this paper, we introduce a practical approach to improve the predictions of a pre-trained model through Few-Shot Learning (FSL). After training and valida…
Face morphing attack detection is challenging and presents a concrete and severe threat for face verification systems. Reliable detection mechanisms for such attacks, which have been tested with a robust cross-database protocol and unknown morphing tools still is a research challenge. This paper proposes a framework following the Few-Shot-Learning approach that shares image information based on the siamese network using triplet-semi-hard-loss to tackle the morphing attack detection and boost the clustering classification process. This network compares a bona fide or potentially morphed image w…
Shahjalal University of Science and Technology（沙赫贾拉勒科技大学）
Patients with the COVID-19 infection may have pneumonia-like symptoms as well as respiratory problems which may harm the lungs. From medical images, coronavirus illness may be accurately identified and predicted using a variety of machine learning methods. Most of the published machine learning methods may need extensive hyperparameter adjustment and are unsuitable for small datasets. By leveraging the data in a comparatively small dataset, few-shot learning algorithms aim to reduce the requirement of large datasets. This inspired us to develop a few-shot learning model for early detection of …
Beijing Institute of Technology（北京理工大学）；Alibaba Group（阿里巴巴集团）；Zhejiang University（浙江大学）；Australian National University（澳大利亚国立大学）
In the same vein of discriminative one-shot learning, Siamese networks allow recognizing an object from a single exemplar with the same class label. However, they do not take advantage of the underlying structure of the data and the relationship among the multitude of samples as they only rely on pairs of instances for training. In this paper, we propose a new quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation. We design four shared networks that receive multi-tuple of instances as inputs and are connected…
Korea University（高丽大学）；NAVERWEBTOON Corp.（NAVERWEBTOON公司）
Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, we present a novel memory-augmented colorization model MemoPainter that can produce high-quality colorization with limited data. In particular, our model is able to capture rare instances and successfully colorize them. We also propose a novel threshold triplet loss that enables unsupervised training of memory networks without the need of class labels. Experiments show that our …
This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled, and test images are divided into queries and a few labeled support images per object class of interest. The training and test images do not share object classes. We extend the vanilla GAN with two loss functions, both aimed at self-supervised learning. The first is a reconstruction loss that enforces the discriminator to reconstruct the probabilistically sampled latent code which has been used for generating the "fake" image. The second is a triplet loss that enforces the discriminator to ou…
Australian National University（澳大利亚国立大学）；Data61-CSIRO（Data61-联邦科学与工业研究组织）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）
Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification. However despite the increasing ubiquity of 3D sensors, the corresponding 3D point cloud classification problem has not been meaningfully explored and introduces new challenges. This paper extends, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification. To this end, a novel triplet loss is developed that takes advantage of unlab…
We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized. While the specifics of the optimization differ, in this work we collectively call this type of supervision Triplets and all methods that follow this pattern Triplet-Based…
Department of Computer Science BITS Pilani Hyderabad（比拉理工学院海得拉巴校区计算机科学系）；Department of Chemical Engineering IIT Bombay（印度理工学院孟买分校化学工程系）
The problem of segregating recyclable waste is fairly daunting for many countries. This article presents an approach for image based classification of plastic waste using one-shot learning techniques. The proposed approach exploits discriminative features generated via the siamese and triplet loss convolutional neural networks to help differentiate between 5 types of plastic waste based on their resin codes. The approach achieves an accuracy of 99.74% on the WaDaBa Database
Tongji University（同济大学）；King’s College London（伦敦国王学院）
In visual recognition tasks, few-shot learning requires the ability to learn object categories with few support examples. Its re-popularity in light of the deep learning development is mainly in image classification. This work focuses on few-shot semantic segmentation, which is still a largely unexplored field. A few recent advances are often restricted to single-class few-shot segmentation. In this paper, we first present a novel multi-way (class) encoding and decoding architecture which effectively fuses multi-scale query information and multi-class support information into one query-support…
Learning Woody Clearing With Loss Alignment for Zero-Shot Regrowth and Woody Segmentation
New South Wales Department of Climate Change, Energy, the Environment and Water（新南威尔士州气候变化、能源、环境与水利部）；Monash University（莫纳什大学）；University of New England（新英格兰大学）；University of Newcastle（纽卡斯尔大学）
