Hexiang Hu is a Computer Science Ph.D. student in Viterbi School of Engineering at University of Southern California (USC), working with Prof. Fei Sha. Prior to this, He was a Ph.D. student in Henry Samueli School of Engineering and Applied Science at University of California, Los Angeles (UCLA). He earned his Bachelor’s degrees in Computer Science from Zhejiang University and Simon Fraser University with honor. His research interests lie in the field of Machine Learning, Computer Vision and Natural Language Processing. [ Résumé ]
We propose a novel probabilistic model for visual question answering.
We investigate the problem of cross-dataset adaptation for visual question answering.
We study three general multi-task learning (MTL) approaches on 11 sequence tagging tasks. Our extensive empirical results show that in about 50\% of cases, jointly learning all 11 tasks improves either learning tasks independently or pairwise learning of tasks. We also show that pairwise MTL can inform us what tasks can benefit others or what tasks can be benefited if they are learned jointly. We additionally identify tasks that can always benefit others as well as tasks that can always be harmed by others.
Training robust deep video representations has proven to be much more challenging than learning deep image representations and consequently hampered tasks like video action recognition. Motivated by the fact that the superfluous information can be reduced by up to two orders of magnitude with video compression techniques, in this work, we propose to train a deep network directly on the compressed video, devoid of redundancy
We show the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or the both while still doing well on the task.
We present a novel segment proposal framework, namely FastMask, which takes advantage of the hierarchical structure in deep convolutional neural network to segment multi-scale objects in one shot. Through leveraging feature pyramid and sliding-window region attention, we made instance proposal not only fast but more accurate.
We propose a generic structured model that leverages diverse label relations to improve image classification performance. It employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. The design of this framework naurally extends to leverage partial observations in the label space to inference the rest label space.