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Multiple Instance Learning

26 papers with code · Methodology

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Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices

NeurIPS 2018 Microsoft/EdgeML

We propose a method, EMI-RNN, that exploits these observations by using a multiple instance learning formulation along with an early prediction technique to learn a model that achieves better accuracy compared to baseline models, while simultaneously reducing computation by a large fraction.

MULTIPLE INSTANCE LEARNING TIME SERIES TIME SERIES CLASSIFICATION

Attention-based Deep Multiple Instance Learning

ICML 2018 AMLab-Amsterdam/AttentionDeepMIL

Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances.

MULTIPLE INSTANCE LEARNING

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

9 Jul 2018ppengtang/oicr

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.

MULTIPLE INSTANCE LEARNING OBJECT RECOGNITION WEAKLY SUPERVISED OBJECT DETECTION

Multiple Instance Detection Network with Online Instance Classifier Refinement

CVPR 2017 ppengtang/oicr

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.

MULTIPLE INSTANCE LEARNING OBJECT RECOGNITION WEAKLY SUPERVISED OBJECT DETECTION

Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised

EMNLP 2018 stangelid/oposum

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).

MULTIPLE INSTANCE LEARNING

C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection

CVPR 2019 Winfrand/C-MIL

Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.

MULTIPLE INSTANCE LEARNING WEAKLY SUPERVISED OBJECT DETECTION WEAKLY-SUPERVISED OBJECT LOCALIZATION

Adaptive pooling operators for weakly labeled sound event detection

26 Apr 2018marl/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.

MULTIPLE INSTANCE LEARNING SOUND EVENT DETECTION TIME SERIES

Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis

TACL 2018 stangelid/milnet-sent

We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL).

MULTIPLE INSTANCE LEARNING SENTIMENT ANALYSIS

From Captions to Visual Concepts and Back

CVPR 2015 Epiphqny/Multiple-instance-learning

The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.

IMAGE CAPTIONING LANGUAGE MODELLING MULTIPLE INSTANCE LEARNING

Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval

CVPR 2019 yalesong/pvse

In this work, we introduce Polysemous Instance Embedding Networks (PIE-Nets) that compute multiple and diverse representations of an instance by combining global context with locally-guided features via multi-head self-attention and residual learning.

CROSS-MODAL RETRIEVAL MULTIPLE INSTANCE LEARNING