Multiple Instance Learning
267 papers with code • 5 benchmarks • 13 datasets
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
Benchmarks
These leaderboards are used to track progress in Multiple Instance Learning
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Libraries
Use these libraries to find Multiple Instance Learning models and implementations
CAMELYON16
UCF-Crime
AVE
SPOT
Most implemented papers
Attention-based Deep Multiple Instance Learning
AMLab-Amsterdam/AttentionDeepMIL
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ICML 2018
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances.
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.
Real-world Anomaly Detection in Surveillance Videos
To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Multiple Instance Detection Network with Online Instance Classifier Refinement
We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level supervision, i. e., without object location information.
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
ppengtang/oicr
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The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one.
Using Neural Network Formalism to Solve Multiple-Instance Problems
Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural.
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance.
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels.
Multiple Anchor Learning for Visual Object Detection
In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector.
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the positive instances, substantially improving the robustness of the MIL approach to the negative instances from abnormal videos.
