Browse SoTA > Methodology > Multiple Instance Learning

Multiple Instance Learning

43 papers with code · Methodology

Leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

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

Real-world Anomaly Detection in Surveillance Videos

CVPR 2018 WaqasSultani/AnomalyDetectionCVPR2018

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.

ACTIVITY RECOGNITION ANOMALY DETECTION IN SURVEILLANCE VIDEOS 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

Weakly labelled audioset tagging with attention neural networks

IEEE/ACM Transactions on Audio, Speech, and Language Processing 2019 qiuqiangkong/audioset_classification

We bridge the connection between attention neural networks and multiple instance learning (MIL) methods, and propose decision-level and feature-level attention neural networks for audio tagging.

AUDIO TAGGING 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

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

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).

ASPECT EXTRACTION MULTIPLE INSTANCE LEARNING

Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection

24 May 2019Kikyo-16/Sound_event_detection

In this paper, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed.

MULTI-LABEL CLASSIFICATION MULTIPLE INSTANCE LEARNING SOUND EVENT DETECTION