In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis.
This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years.
Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.
University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；University of California, Berkeley（加州大学伯克利分校）；Boston University（波士顿大学）；Lisbon University（里斯本大学）；Northwest Nazarene University（西北拿撒勒大学）；University of Oxford（牛津大学）
The accurate characterization of the severity of the wildfire event strongly contributes to the characterization of the fuel conditions in fire-prone areas, and provides valuable information for disaster response. The aim of this study is to develop an autonomous system built on top of high-resolution multispectral satellite imagery, with an advanced deep learning method for detecting burned area change. This work proposes an initial exploration of using an unsupervised model for feature extraction in wildfire scenarios. It is based on the contrastive learning technique SimCLR, which is traine…
Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese concept is topology-agnostic and can use any neural network as its backbone. The two most popular loss functions for training these networks are the triplet and contrastive loss functions. In this paper, we propose two novel loss functions, named Fisher Discriminant Triplet (FDT) and Fisher Discriminant Contrastive (FDC). The former uses anchor-neighbor-distant triplets while the latter utilizes pairs of anchor-neighbor …
Animal pose estimation has recently come into the limelight due to its application in biology, zoology, and aquaculture. Deep learning methods have effectively been applied to human pose estimation. However, the major bottleneck to the application of these methods to animal pose estimation is the unavailability of sufficient quantities of labeled data. Though there are ample quantities of unlabelled data publicly available, it is economically impractical to label large quantities of data for each animal. In addition, due to the wide variety of body shapes in the animal kingdom, the transfer of…
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the Wild
School of Computer and Information Engineering, Henan University（河南大学计算机与信息工程学院）；UPV/EHU（巴斯克大学）；IKERBASQUE, Basque Foundation for Science（巴斯克科学基金会（IKERBASQUE））
The prevalence of abusive language on different online platforms has been a major concern that raises the need for automated cross-platform abusive language detection. However, prior works focus on concatenating data from multiple platforms, inherently adopting Empirical Risk Minimization (ERM) method. In this work, we address this challenge from the perspective of domain generalization objective. We design SCL-Fish, a supervised contrastive learning integrated meta-learning algorithm to detect abusive language on unseen platforms. Our experimental analysis shows that SCL-Fish achieves better …
In this work, we introduce panoramic panoptic segmentation as the most holistic scene understanding both in terms of field of view and image level understanding for standard camera based input. A complete surrounding understanding provides a maximum of information to the agent, which is essential for any intelligent vehicle in order to make informed decisions in a safety-critical dynamic environment such as real-world traffic. In order to overcome the lack of annotated panoramic images, we propose a framework which allows model training on standard pinhole images and transfers the learned feat…
Universitat de Barcelona（巴塞罗那大学）；Universidade Federal de Santa Catarina（圣卡塔琳娜联邦大学）；Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Universidade Federal do Paraná（巴拉那联邦大学）；Witwatersrand University（威特沃特斯兰德大学）；Oregon State University（俄勒冈州立大学）；University of Zurich（苏黎世大学）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）
Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the …
Machine learning models deployed in the wild naturally encounter unlabeled samples from both known and novel classes. Challenges arise in learning from both the labeled and unlabeled data, in an open-world semi-supervised manner. In this paper, we introduce a new learning framework, open-world contrastive learning (OpenCon). OpenCon tackles the challenges of learning compact representations for both known and novel classes and facilitates novelty discovery along the way. We demonstrate the effectiveness of OpenCon on challenging benchmark datasets and establish competitive performance. On the …
Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere
Athinoula A. Martinos Center（阿西努拉·A.马蒂诺斯中心）；MGH, HMS, Harvard University（哈佛医学院、麻省总医院、哈佛大学）；Imperial College London（伦敦帝国学院）；Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）
Recent advances in self-supervised learning (SSL) using large models to learn visual representations from natural images are rapidly closing the gap between the results produced by fully supervised learning and those produced by SSL on downstream vision tasks. Inspired by this advancement and primarily motivated by the emergence of tabular and structured document image applications, we investigate which self-supervised pretraining objectives, architectures, and fine-tuning strategies are most effective. To address these questions, we introduce RegCLR, a new self-supervised framework that combi…
University of Pennsylvania（宾夕法尼亚大学）；Baidu Research, Baidu Inc.（百度公司百度研究院）；Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）
Training images with data transformations have been suggested as contrastive examples to complement the testing set for generalization performance evaluation of deep neural networks (DNNs). In this work, we propose a practical framework ContRE (The word "contre" means "against" or "versus" in French.) that uses Contrastive examples for DNN geneRalization performance Estimation. Specifically, ContRE follows the assumption in contrastive learning that robust DNN models with good generalization performance are capable of extracting a consistent set of features and making consistent predictions fr…
Learning Contrastive Feature Representations for Facial Action Unit Detection
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.（新型软件技术国家重点实验室，南京大学，中国南京）；School of Intelligence Science and Technology, Nanjing University, Suzhou, China.（智能科学与技术学院，南京大学，中国苏州）；School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China.（计算与人工智能学院，西南交通大学，中国成都）
Samsung Advanced Institute of Technology (SAIT)（三星先进技术研究院）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
As the expressive depth of an emotional face differs with individuals or expressions, recognizing an expression using a single facial image at a moment is difficult. A relative expression of a query face compared to a reference face might alleviate this difficulty. In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose. The contrastive representation is calculated at the embedding layer of deep networks by comparing a given (query) image with the reference image. We attempt to utilize a generative reference image…
Seoul National University of Science and Technology（首尔科学技术大学）
Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly. This benefits especially deep neural networks which generally require a huge number of labeled samples to achieve high performance. Most existing active learning methods have been evaluated in an ideal setting where only samples relevant to the target task, i.e., in-distribution samples, exist in an unlabeled data pool. A data pool gathered from the wild, however, is likely to include samples that are irrelevant to the target task at all and/or too …
University of Science and Technology of China（中国科学技术大学）
Facial expression recognition (FER) is an essential task for understanding human behaviors. As one of the most informative behaviors of humans, facial expressions are often compound and variable, which is manifested by the fact that different people may express the same expression in very different ways. However, most FER methods still use one-hot or soft labels as the supervision, which lack sufficient semantic descriptions of facial expressions and are less interpretable. Recently, contrastive vision-language pre-training (VLP) models (e.g., CLIP) use text as supervision and have injected ne…
Salesforce Research（Salesforce研究中心）；UC San Diego（加州大学圣地亚哥分校）
Users' interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.).However, users' underlying intents are often unobserved/latent, making it challenging to leverage such latent intents forSequentialrecommendation(SR). To investigate the benefits of latent intents and leverage them effectively for recommendation, we proposeIntentContrastiveLearning(ICL), a general learning paradigm that leverages a latent intent variable into SR. The core idea is to learn users' intent distribution functions from unlabeled user behavior sequenc…
Contrastive Attraction and Contrastive Repulsion for Representation Learning
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Shanghai Jiao Tong University（上海交通大学）；Alibaba Group（阿里巴巴集团）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Microsoft Research（微软研究院）；Xidian University（西安电子科技大学）
Online continual learning in the wild is a very difficult task in machine learning. Non-stationarity in online continual learning potentially brings about catastrophic forgetting in neural networks. Specifically, online continual learning for autonomous driving with SODA10M dataset exhibits extra problems on extremely long-tailed distribution with continuous distribution shift. To address these problems, we propose multiple deep metric representation learning via both contrastive and supervised contrastive learning alongside soft labels distillation to improve model generalization. Moreover, w…
University of Science and Technology of China（科学技术大学）；Hefei University of Technology（合肥工业大学）；IAI, Hefei Comprehensive National Science Center（IAI合肥国家科学中心）
This paper describes our approach for the Disguised Faces in the Wild (DFW) 2018 challenge. The task here is to verify the identity of a person among disguised and impostors images. Given the importance of the task of face verification it is essential to compare methods across a common platform. Our approach is based on VGG-face architecture paired with Contrastive loss based on cosine distance metric. For augmenting the data set, we source more data from the internet. The experiments show the effectiveness of the approach on the DFW data. We show that adding extra data to the DFW dataset with…
University of Science and Technology of China（中国科学技术大学）
Most modern face completion approaches adopt an autoencoder or its variants to restore missing regions in face images. Encoders are often utilized to learn powerful representations that play an important role in meeting the challenges of sophisticated learning tasks. Specifically, various kinds of masks are often presented in face images in the wild, forming complex patterns, especially in this hard period of COVID-19. It's difficult for encoders to capture such powerful representations under this complex situation. To address this challenge, we propose a self-supervised Siamese inference netw…
FishAI 2.0: Marine Fish Image Classification with Multi-modal Few-shot Learning
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（中国教育部系统控制与信息处理重点实验室）；State Key Laboratory of Submarine Geoscience, Second Institute of Oceanography, Ministry of Natural Resources（自然资源部第二海洋研究所海底科学国家重点实验室）；Key Laboratory of Marine Ecosystem Dynamics, Ministry of Natural Resources（自然资源部海洋生态系统动力学重点实验室）；Second Institute of Oceanography, Ministry of Natural Resources（自然资源部第二海洋研究所）
MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition
University of Bonn（波恩大学）；University of Cambridge（剑桥大学）；University of Siegen（锡根大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习和人工智能研究所）
Ecole des Ponts（巴黎高科路桥学校）；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）；CNRS（法国国家科学研究中心）
Motivated by the need for estimating the 3D pose of arbitrary objects, we consider the challenging problem of class-agnostic object viewpoint estimation from images only, without CAD model knowledge. The idea is to leverage features learned on seen classes to estimate the pose for classes that are unseen, yet that share similar geometries and canonical frames with seen classes. We train a direct pose estimator in a class-agnostic way by sharing weights across all object classes, and we introduce a contrastive learning method that has three main ingredients: (i) the use of pre-trained, self-sup…
University of Helsinki（赫尔辛基大学）；Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）
Self-supervised learning is an increasingly popular approach to unsupervised learning, achieving state-of-the-art results. A prevalent approach consists in contrasting data points and noise points within a classification task: this requires a good noise distribution which is notoriously hard to specify. While a comprehensive theory is missing, it is widely assumed that the optimal noise distribution should in practice be made equal to the data distribution, as in Generative Adversarial Networks (GANs). We here empirically and theoretically challenge this assumption. We turn to Noise-Contrastiv…
LaCoVL-FER: Landmark-Guided Contrastive Learning Network with Vision-Language Enhancement for Facial Expression Recognition
School of Airspace and Engineering, Shandong University（山东大学航空航天与工程学院）；School of Computer Science and Technology, Shandong University of Finance and Economics（山东财经大学计算机科学与技术学院）；School of Psychology and Neuroscience, University of Glasgow（格拉斯哥大学心理学与神经科学学院）；Faculty of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）
The University of Tokyo（东京大学）；Sony CSL Kyoto（索尼计算机科学实验室京都所）
Silent speech interface is a promising technology that enables private communications in natural language. However, previous approaches only support a small and inflexible vocabulary, which leads to limited expressiveness. We leverage contrastive learning to learn efficient lipreading representations, enabling few-shot command customization with minimal user effort. Our model exhibits high robustness to different lighting, posture, and gesture conditions on an in-the-wild dataset. For 25-command classification, an F1-score of 0.8947 is achievable only using one shot, and its performance can be…
In this work, we introduce panoramic panoptic segmentation, as the most holistic scene understanding, both in terms of Field of View (FoV) and image-level understanding for standard camera-based input. A complete surrounding understanding provides a maximum of information to a mobile agent. This is essential information for any intelligent vehicle to make informed decisions in a safety-critical dynamic environment such as real-world traffic. In order to overcome the lack of annotated panoramic images, we propose a framework which allows model training on standard pinhole images and transfers t…
