13 citations · 22 across the 5 of their papers we have counts for
10 papers
Visual Question Answering based on Local-Scene-Aware Referring Expression Generation
Jung-Jun Kim, Dong-Gyu Lee, Jialin Wu +2
Visual question answering requires a deep understanding of both images and natural language. However, most methods mainly focus on visual concept; such as the relationships between…
Improving VQA and its Explanations \\ by Comparing Competing Explanations
Jialin Wu, Liyan Chen, Raymond J. Mooney
Most recent state-of-the-art Visual Question Answering (VQA) systems are opaque black boxes that are only trained to fit the answer distribution given the question and visual conte…
Hidden State Guidance: Improving Image Captioning using An Image Conditioned Autoencoder
Jialin Wu, Raymond J. Mooney
Most RNN-based image captioning models receive supervision on the output words to mimic human captions. Therefore, the hidden states can only receive noisy gradient signals via lay…
Generating Question Relevant Captions to Aid Visual Question Answering
Jialin Wu, Zeyuan Hu, Raymond J. Mooney
Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to improve VQA performan…
Self-Critical Reasoning for Robust Visual Question Answering
Jialin Wu, Raymond J. Mooney
Visual Question Answering (VQA) deep-learning systems tend to capture superficial statistical correlations in the training data because of strong language priors and fail to genera…
Image Score: How to Select Useful Samples
Simiao Zuo, Jialin Wu
There has long been debates on how we could interpret neural networks and understand the decisions our models make. Specifically, why deep neural networks tend to be error-prone wh…