Multiple Instance Learning
233 papers with code • 0 benchmarks • 8 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
Libraries
Use these libraries to find Multiple Instance Learning models and implementations
UCF-Crime
AVE
SPOT
CamNuvem Dataset
Most implemented papers
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
ucas-vg/p2bnet
•
•
However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large.
Multiple Instance Dictionary Learning using Functions of Multiple Instances
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels.
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL).
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.
Adaptive pooling operators for weakly labeled sound event detection
marl/autopool
•
•
In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality.
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
stangelid/oposum
•
•
EMNLP 2018
We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e. g., in the form of product domain labels and user-provided ratings).
Weakly Supervised Object Detection in Artworks
nicaogr/Mi_max
•
•
ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018 2018
We propose a method for the weakly supervised detection of objects in paintings.
Weakly-supervised Temporal Action Localization by Uncertainty Modeling
Pilhyeon/WTAL-Uncertainty-Modeling
•
•
Experimental results show that our uncertainty modeling is effective at alleviating the interference of background frames and brings a large performance gain without bells and whistles.
Hopfield Networks is All You Need
ml-jku/hopfield-layers
•
•
ICLR 2021
The new update rule is equivalent to the attention mechanism used in transformers.
Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
nicaogr/Mi_max
•
•
Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years.
