Search Results for author:
Found 19 papers, 2 papers with code
Speech Synthesis as Augmentation for Low-Resource ASR
Meanwhile, speech synthesis techniques have been rapidly getting closer to the goal of achieving human-like speech.
The 2019 BBN Cross-lingual Information Retrieval System
In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English.
Learning from Noisy Labels with Noise Modeling Network
Multi-label image classification has generated significant interest in recent years and the performance of such systems often suffers from the not so infrequent occurrence of incorrect or missing labels in the training data.
Combining Word Embeddings and N-grams for Unsupervised Document Summarization
An improved sentence similarity graph is built and used in a submodular objective function for extractive summarization, which consists of a weighted coverage term and a diversity term.
Cross-lingual Information Retrieval with BERT
Multiple neural language models have been developed recently, e. g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking.
Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval
We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages.
Towards a New Understanding of the Training of Neural Networks with Mislabeled Training Data
This includes results about the ability of the noisy model to make the same decisions as the clean model and the effects of noise on model performance.
Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks
The learnt representation is discriminative and transferable between languages.
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks
Experimental results demonstrate that our approach can achieve state-of-the-art average precision (AP) performances on the InfAR dataset: (1) the proposed two-stream 3D CNN achieves the best reported 77. 5% AP, and (2) our 3D CNN model applied to the optical flow fields achieves the best reported single stream 75. 42% AP.
Generating Discriminative Object Proposals via Submodular Ranking
Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation.
Learning Discriminative Features via Label Consistent Neural Network
Deep Convolutional Neural Networks (CNN) enforces supervised information only at the output layer, and hidden layers are trained by back propagating the prediction error from the output layer without explicit supervision.
Submodular Attribute Selection for Action Recognition in Video
In real-world action recognition problems, low-level features cannot adequately characterize the rich spatial-temporal structures in action videos.
Submodular Object Recognition
We present a novel object recognition framework based on multiple figure-ground hypotheses with a large object spatial support, generated by bottom-up processes and mid-level cues in an unsupervised manner.
Collaborative Receptive Field Learning
However, measuring pairwise distance of RF's for building the similarity graph is a nontrivial problem.
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification
We now know that mid-level features can greatly enhance the performance of image learning, but how to automatically learn the image features efficiently and in an unsupervised manner is still an open question.
Sparse Dictionary-based Attributes for Action Recognition and Summarization
We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes.
Tag Taxonomy Aware Dictionary Learning for Region Tagging
Tags of image regions are often arranged in a hierarchical taxonomy based on their semantic meanings.
Learning Structured Low-Rank Representations for Image Classification
An approach to learn a structured low-rank representation for image classification is presented.
Submodular Salient Region Detection
The problem of salient region detection is formulated as the well-studied facility location problem from operations research.
