Nature Language Processing
Distributional Inclusion Vector Embedding
We propose a novel word embedding method which preserves the distributional inclusion property in the sparse-bag-of-word (SBOW) feature. The embedding can be used to predict generality of words, detect the hypernym relation, and discover the topics from the raw text simultaneously. The extensive experiments show that the embedding effectively compresses the SBOW, and achieves new state-of-the-art performances on the unsupervised hypernym detection tasks (Paper, Code, Demo, Poster). We also show that DIVE could help us to do word sense induction more efficiently (Paper, Slides).
UMASS TAC 2016 system for relation extraction
TAC-KBP is one of the most challenging text-based information retrieval tasks. We integrate research which is done in UMASS IESL in the past year, including embedding linker, multilingual Universal Schema, and LSTM sentence embedding. We perform extensive error analysis and develop some novel techniques (such as using a search engine to reduce noise in training data) to tackle the problems (Paper).
Inspired by active learning, we propose two alternatives to re-weight training samples based on lightweight estimates of sample uncertainty in stochastic gradient descent (SGD). Extensive experimental results on six datasets show that our methods reliably improve accuracy in various network architectures, including additional gains on top of other popular training techniques (Paper, Poster).
Student Modeling and Prerequisite Verification in Knowledge Tree
We extract answering logs of the exercises from Junyi Academy (http://www.junyiacademy.org/), an E-learning website similar to Khan Academy.
We use crowdsourcing and machine learning to discover relationships between exercises. Based on that, we will design a mechanism of adaptive test to improve learning experiences of Junyi academy (Paper, Presentation, Demo, Dataset).
Computer Vision and Multimedia
We use Bayesian learning to model the non-linear relationships between quality of experience (QoE) and multiple factors.
Our experiment shows that active sampling can be used to reduce the number of samples collected from crowdsourcing for building such model (Paper).
We proposed a general framework which applies classifiers with different complexity to discriminate segments in an image.
Our unsupervisedhierarchical segmentation results achieve similar or better performance in several standard benchmarks compared with the current state-of-the-art methods based on learning, and has been accepted to ACCV 2014 (Paper, Poster).
We proposed an efficient algorithm which decomposes the unsupervised Multiple Foreground Co-segmentation problem into three sub-problems: segmentation, matching and figure-ground classification.
Our method improves the accuracy of the state-of-the-art method by 13% in a standard benchmark, and has been accepted by CVIU (Paper).
We applied dictionary learning, visual and motion saliency to extract the foreground object from videos. This research has been accepted to TIP (Paper).
In a single surveillance video, we incorporate tracking data into the photo pop-up algorithm, and utilize RANSAC (random sample consensus) to reduce around 80% of tracking noise (PPT & Videos).