Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Multi-Label Learning from Single Positive Labels
Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate …
image-classificationImage ClassificationMissing LabelsMulti-Label Classification+3Sum of Ranked Range Loss for Supervised Learning
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual losses of a learning model over each trai…
Multi-class ClassificationMulti-Label LearningBasic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion m…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningCCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise
Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have been made to improve the model robustness…
Multi-Label LearningA Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this prob…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Hierarchical Relationship Alignment Metric Learning
Most existing metric learning methods focus on learning a similarity or distance measure relying on similar and dissimilar relations between sample pairs. However, pairs of samples cannot be simply identified as similar …
Metric LearningMulti-Label LearningRepresentation Learning by Ranking under multiple tasks
In recent years, representation learning has become the research focus of the machine learning community. Large-scale pre-training neural networks have become the first step to realize general intelligence. The key to th…
Data AugmentationMulti-Label LearningRepresentation LearningRetrieval+1Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering
Although unsupervised person re-identification (Re-ID) has drawn increasing research attention recently, it remains challenging to learn discriminative features without annotations across disjoint camera views. In this p…
ClusteringMulti-Label LearningPerson Re-IdentificationPseudo Label+1Learning Discriminative Features using Multi-label Dual Space
Multi-label learning handles instances associated with multiple class labels. The original label space is a logical matrix with entries from the Boolean domain $\in \left \{ 0,1 \right \}$. Logical labels are not able to…
Multi-Label LearningDiscovering Multi-Label Actor-Action Association in a Weakly Supervised Setting
Since collecting and annotating data for spatio-temporal action detection is very expensive, there is a need to learn approaches with less supervision. Weakly supervised approaches do not require any bounding box annotat…
Action DetectionMulti-Label LearningImproving Tail Label Prediction for Extreme Multi-label Learning
Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set. Many previous methods treat labels uniformly such that the learned model tends to perform better on hea…
Multi-Label LearningTkML-AP: Adversarial Attacks to Top-k Multi-Label Learning
Top-k multi-label learning, which returns the top-k predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine. However, the vulnerabilities of …
Multi-Label LearningTowards Coarse and Fine-grained Multi-Graph Multi-Label Learning
Multi-graph multi-label learning (\textsc{Mgml}) is a supervised learning framework, which aims to learn a multi-label classifier from a set of labeled bags each containing a number of graphs. Prior techniques on the \te…
Multi-Label LearningMulti-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) hav…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Characterizing the Evasion Attackability of Multi-label Classifiers
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic. Characterizing the crucial factors determining the attackability of the multi-label adversarial thre…
Computational EfficiencyMulti-Label LearningA Study on the Autoregressive and non-Autoregressive Multi-label Learning
Extreme classification tasks are multi-label tasks with an extremely large number of labels (tags). These tasks are hard because the label space is usually (i) very large, e.g. thousands or millions of labels, (ii) very …
Multi-Label LearningThe Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classificati…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning
Emotion-cause pair extraction (ECPE) is a new task that aims to extract the potential pairs of emotions and their corresponding causes in a document. The existing methods first perform emotion extraction and cause extrac…
Emotion-Cause Pair ExtractionMulti-Label LearningMulti-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis
Aspect-category sentiment analysis (ACSA) aims to predict sentiment polarities of sentences with respect to given aspect categories. To detect the sentiment toward a particular aspect category in a sentence, most previou…
Aspect Category Sentiment AnalysisMulti-Label LearningSentenceSentiment AnalysisMulti-typed Objects Multi-view Multi-instance Multi-label Learning
Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annota…
Multi-Label Learning