태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several possible class labels simultaneously [1]. Each of the assigned labels conveys a specific semantic relationship with the multi-label data instance [2, 3]. Multi-label learning has continued to receive a lot of research interest due to its practical application in many real-world problems such as recommender systems [4], image annotation [5], and text classification [6].
References:
Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)
Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38
Bogaert M, Lootens J, Van den Poel D, Ballings M (2019) Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 279(2):620– 634
Jing L, Shen C, Yang L, Yu J, Ng MK (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 26(10):4612–4625
Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.
The Expression in-the-Wild (ExpW) dataset is for facial expression recognition and contains 91,793 faces manually labeled with expressions. Each of the face images is annotated as one of the seven basic expression categories: “angry”, “disgust”, “fear”, “happy”, “sad”, “surprise”, or “neutral”.
The BirdSong dataset consists of audio recordings of bird songs at the H. J. Andrews (HJA) Experimental Forest, using unattended microphones. The goal of the dataset is to provide data to automatically identify the species of bird responsible for each utterance in these recordings. The dataset contains 548 10-seconds audio recordings.
Animal Kingdom is a large and diverse dataset that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors. The wild animal footage used in the dataset records different times of the day in an extensive range of environments containing variations in backgrounds, viewpoints, illumination and weather conditions. More specifically, the dataset contains 50 hours of annotated videos to localize relevant animal behavior segments in long videos for the video grounding task, 30K video sequences for the fine-grained multi-label action recognition task, and 33K frames for the pose estimation task, which correspond to a diverse range of animals with 850 species across 6 major animal classes.
The objective in extreme multi-label classification is to learn feature architectures and classifiers that can automatically tag a data point with the most relevant subset of labels from an extremely large label set. This repository provides resources that can be used for evaluating the performance of extreme multi-label algorithms including datasets, code, and metrics. For more details please visit the link http://manikvarma.org/downloads/XC/XMLRepository.html
For each dataset we provide a short description as well as some characterization metrics. It includes the number of instances (m), number of attributes (d), number of labels (q), cardinality (Card), density (Dens), diversity (Div), average Imbalance Ratio per label (avgIR), ratio of unconditionally dependent label pairs by chi-square test (rDep) and complexity, defined as m × q × d as in [Read 2010]. Cardinality measures the average number of labels associated with each instance, and density is defined as cardinality divided by the number of labels. Diversity represents the percentage of labelsets present in the dataset divided by the number of possible labelsets. The avgIR measures the average degree of imbalance of all labels, the greater avgIR, the greater the imbalance of the dataset. Finally, rDep measures the proportion of pairs of labels that are dependent at 99% confidence. A broader description of all the characterization metrics and the used partition methods are described in the MLDA documentation. We also used MLDA for the characterization and partitioning of the datasets.
This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom). There are four classes of the corn seed (Broken-B, Discolored-D, Silkcut-S, and Pure-P) 17802 images are labeled by the experts at the AdTech Corp. and 26K images were unlabeled out of which 9k images were labeled using the Active Learning (BatchBALD) We have created three different dataset: (1). Primary dataset: contains the 17802 images labeled by the experts. Top-view(8901) and Bottom-view(8901). (2). Dataset with fake images: We generated fake images using Conditional GAN (BigGAN) as follows: broken-2937, discolored-5823, pure-2937, silkcut-5823 instances and added them into the train set to balance the data set. (3). Balanced dataset: In this case of adding newly captured images labeled using the Batch Active Learning method, new 9000 labeled images are added into the primary dataset. This new dataset contains 26,802 images split into train and validation set 80: 20, respectively. Contains the 17802 images and the 9K images labeled by the Active Learning (BatchBALD).
This dataset endeavors to fill the research void by presenting a meticulously curated collection of misogynistic memes in a code-mixed language of Hindi and English. It introduces two sub-tasks: the first entails a binary classification to determine the presence of misogyny in a meme, while the second task involves categorizing the misogynistic memes into multiple labels, including Objectification, Prejudice, and Humiliation. For more Information and Citation: Singh, A., Sharma, D., & Singh, V. K. (2024). MIMIC: Misogyny Identification in Multimodal Internet Content in Hindi-English Code-Mixed Language. ACM Transactions on Asian and Low-Resource Language Information Processing. (https://doi.org/10.1145/3656169)
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