[PDF] Top 20 Discriminative Feature Learning for Unsupervised Video Summarization
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Discriminative Feature Learning for Unsupervised Video Summarization
... of video content uploaded to various on- line platforms has increased dramatically in recent ...handling video have be- come increasingly ...pervised learning (Mahasseni, Lam, and Todorovic 2017; ... See full document
8
Unsupervised detector adaptation by joint dataset feature learning
... a video in an unsupervised way using joint dataset deep feature ...the video. Experiments on two challenging video datasets show that our algorithm is effective and outperforms the ... See full document
9
Cycle-SUM: Cycle-Consistent Adversarial LSTM Networks for Unsupervised Video Summarization
... Supervised video summarization approaches leverage videos with human annotation on frame importance to train ...formulate video summa- rization as a supervised subset selection problem and pro- pose ... See full document
8
Text Categorization as a Graph Classification Problem
... a feature set can be very ...the discriminative subgraphs, ...an unsupervised feature selection approach to reduce the size of the graphs and a fortiori the number of frequent ... See full document
11
Detection of Text based Cyberbullying using Semantic Enhanced Marginalized Denoising Autoencoder Learning Model
... by feature co-occurrence ...strong feature illustration below associate degree unsupervised learning framework, and this also motivates different progressive text feature ... See full document
6
Unsupervised Feature Learning for Visual Sign Language Identification
... short video samples. The method is trained on unlabelled video data (un- supervised feature learning) and using these features, it is trained to discriminate between six sign languages ... See full document
7
Discriminative Unsupervised Alignment of Natural Language Instructions with Corresponding Video Segments
... language learning algo- rithms for integrating language with vision rely on either a fully supervised (Kollar et ...or video segment, and furthermore, each word or phrase in a sentence is already mapped to ... See full document
11
Unsupervised Deep Video Hashing via Balanced Code for Large Scale Video Retrieval
... to video hashing ...the video directly. Multiple Feature Hashing (MFH) [42] and Submodular Video Hashing (Submod) [43] are the most representative ones in those ...the video ... See full document
15
Video Summarization using Convolutional Neural Network
... from video frames and unsupervised classification and also added new methodology for evaluating the video summarized called as a comparison of user summarized ...context-aware video ... See full document
7
Self Discriminative Learning for Unsupervised Document Embedding
... most discriminative sentences rather than similar ones that might not be critical to shape the em- ...essential feature to facil- itate the discriminator to push away any two doc- uments, which should ... See full document
10
Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation
... Digital recognition dataset contains five widely used benchmarks: Street View House Numbers (SVHN) (Net- zer et al. 2011), MNIST (Lecun et al. 1998), MNIST-M (Ganin et al. 2016), USPS (Hull 2002) and synthetic dig- its ... See full document
8
Unsupervised Deep Video Hashing with Balanced Rotation
... deep learning in image recognition, several recent systems have incorporated the hashing functions into the deep learning ...the discriminative and temporal information of video, assuming the ... See full document
7
Analysis of Video Summarization Techniques
... Invariant Feature Transform) algorithm is not sensitive to change in illumination, scale and ...The video summarization techniques studied were mostly based on the notion of using only the imagery ... See full document
8
Improving Update Summarization via Supervised ILP and Sentence Reranking
... In the above ILP-based summarization method, how to determine the concepts and measure their weights is the key factor impacting the system performance. Intuitively, if we can successfully identify the im- portant ... See full document
6
Unsupervised Semantic Abstractive Summarization
... ture “who is doing what to whom“ in a sentence. An AMR represents the meaning of a sentence us- ing rooted, acyclic, labeled, directed graphs. Fig- ure 2 shows the AMR graph of the sentence “I looked carefully all around ... See full document
10
Deep Unsupervised Feature Learning for Natural Language Processing
... deep learning show state-of-the-art results in part-of-speech parsing, chunking and named-entity tag- ging (Collobert, 2011), however performance in more complex NLP tasks like entity and event disambiguation and ... See full document
6
Focused Meeting Summarization via Unsupervised Relation Extraction
... that can be used as the basis of the decision abstract. We evaluate the approach (using the AMI cor- pus (Carletta et al., 2005)) under two input set- tings — in the True Clusterings setting, we assume that the DRDAs for ... See full document
10
Video Summarization and Restoration
... Video summarization using Motion Detection System can be used in surveillance and security systems in a fixed restriction ...efficient video compression, for smart tracking of moving objects, for ... See full document
9
Sparsity Based Locality –Sensitive Discriminative Dictionary Learning for Video Semantic Analysis
... on sparse coefficients to optimize the dictionary. The SLSDDL enhances the power of discrimination 460.. of sparse representation features based on the principles of Fishers for better [r] ... See full document
17
Text extraction from cricket video- Comparative study of algorithms
... Our system makes automatic changes in the website with the robust methods. Comparison of algorithms is studied practically. We can reduce time for retrieval of database using xml parser for displaying information on ... See full document
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