SOTAVerified

Multiple Instance Learning

Multiple Instance Learning is a type of weakly supervised learning algorithm where training data is arranged in bags, where each bag contains a set of instances $X=\{x_1,x_2, \ldots,x_M\}$, and there is one single label $Y$ per bag, $Y\in\{0, 1\}$ in the case of a binary classification problem. It is assumed that individual labels $y_1, y_2,\ldots, y_M$ exist for the instances within a bag, but they are unknown during training. In the standard Multiple Instance assumption, a bag is considered negative if all its instances are negative. On the other hand, a bag is positive, if at least one instance in the bag is positive.

Source: Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

Papers

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Showing 601–650 of 744 papers

TitleStatusHype
Pornographic Image Recognition via Weighted Multiple Instance Learning—0
Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification—0
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network—0
Multiple Instance Learning for ECG Risk Stratification—0
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices—0
A Multiclass Multiple Instance Learning Method with Exact LikelihoodCode0
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers—0
Identify, locate and separate: Audio-visual object extraction in large video collections using weak supervision—0
Learning to quantify emphysema extent: What labels do we need?—0
Weakly Supervised Object Detection in ArtworksCode1
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural NetworksCode1
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly SupervisedCode0
Deep Multiple Instance Learning for Airplane Detection in High Resolution Imagery—0
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection—0
PCL: Proposal Cluster Learning for Weakly Supervised Object DetectionCode1
Spatio-Temporal Instance Learning: Action Tubes from Class Supervision—0
Deep Multiple Instance Feature Learning via Variational Autoencoder—0
Characterizing multiple instance datasets—0
A convex method for classification of groups of examples—0
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology—0
Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos—0
W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection—0
Pointly-Supervised Action Localization—0
Training Medical Image Analysis Systems like Radiologists—0
Reliable counting of weakly labeled concepts by a single spiking neuron model—0
Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology—0
Weakly-Supervised Video Object Grounding from Text by Loss Weighting and Object Interaction—0
Learning Pretopological Spaces to Model Complex Propagation Phenomena: A Multiple Instance Learning Approach Based on a Logical Modeling—0
Adaptive pooling operators for weakly labeled sound event detectionCode0
Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events—0
Cross-Modal Retrieval with Implicit Concept Association—0
Prediction and Localization of Student Engagement in the WildCode1
Video Representation Learning Using Discriminative Pooling—0
Towards Universal Representation for Unseen Action Recognition—0
Deep Multiple Instance Learning for Zero-shot Image TaggingCode0
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing ApplicationsCode0
A bag-to-class divergence approach to multiple-instance learningCode0
Mixed Supervised Object Detection with Robust Objectness Transfer—0
Attention-based Deep Multiple Instance LearningCode2
Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach—0
Real-world Anomaly Detection in Surveillance VideosCode1
Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning—0
Grounding Referring Expressions in Images by Variational ContextCode0
Multimodal Visual Concept Learning with Weakly Supervised TechniquesCode0
Multiple Instance Learning Networks for Fine-Grained Sentiment AnalysisCode0
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection—0
Multiple-Instance, Cascaded Classification for Keyword Spotting in Narrow-Band Audio—0
pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications—0
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection—0
Progressive Representation Adaptation for Weakly Supervised Object LocalizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Snuffy (DINO Exhaustive)AUC0.99—Unverified
2Snuffy (SimCLR Exhaustive)AUC0.97—Unverified
3CAMILAUC0.96—Unverified
4CAMIL (CAMIL-L)AUC0.95—Unverified
5CAMIL (CAMIL-G)AUC0.95—Unverified
6DTFD-MIL (AFS)AUC0.95—Unverified
7DTFD-MIL (MAS)AUC0.95—Unverified
8DTFD-MIL (MaxMinS)AUC0.94—Unverified
9TransMILAUC0.93—Unverified
10DSMIL-LCAUC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DTFD-MIL (MAS)AUC0.96—Unverified
2DTFD-MIL (AFS)ACC0.95—Unverified
3Snuffy (SimCLR Exhaustive)ACC0.95—Unverified
4DSMIL-LCACC0.93—Unverified
5DSMILACC0.92—Unverified
6DTFD-MIL (MaxMinS)ACC0.89—Unverified
7TransMILACC0.88—Unverified
8DTFD-MIL (MaxS)ACC0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1SnuffyAUC0.97—Unverified
2DSMILACC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1SnuffyACC0.96—Unverified
2DSMILACC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1DSMILACC0.93—Unverified
2SnuffyACC0.79—Unverified