A Joint Speaker-Listener-Reinforcer Model for Referring Expressions

Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a unified framework for the tasks of referring expression comprehension and generation. Our model is composed of three modules: speaker, listener, and reinforcer. The speaker generates referring expressions, the listener comprehends referring expressions, and the reinforcer introduces a reward function to guide sampling of more discriminative expressions. The listener-speaker modules are trained jointly in an end-to-end learning framework, allowing the modules to be aware of one another during learning while also benefiting from the discriminative reinforcer's feedback. We demonstrate that this unified framework and training achieves state-of-the-art results for both comprehension and generation on three referring expression datasets. Project and demo page: https://vision.cs.unc.edu/referComment: Some typo fixed; comprehension results on refcocog updated; more human evaluation results adde

QUESTION ANSWERING, GROUNDING, AND GENERATION FOR VISION AND LANGUAGE

One ultimate goal of AI is to develop an artificial intelligent (AI) system that can communicate with people in a natural way. Such communication includes but is not limited to asking we humans questions, answering our questions, conducting dialogue with human beings, and performing some actions to better serve people. Imagine in the future where the service robot is everywhere, and we could ask our home robot to “grab me the red cup on the table.” To perform this command, the AI system needs to understand this spoken English sentence, perceive the visual world, navigate to the right place “table”, recognize the right object “the red cup”, then grab it and finally return it back to the commander. Just for this single command, it already involves many techniques, such as speech recognition, language understanding, scene understanding, embodied navigation, object recognition, pose estimation, robot manipulation, etc. Each of these techniques are not well solved yet, but we are on a rapid way toward the success. This thesis is in advancing our knowledge to explore various connections between vision, language and even beyond to push forward this ultimate goal. We study 3 popular vision and language tasks, including visual question answering, language grounding, and image-to-text language generation. Inside each, we will introduce our proposed novel task, accompanied with high-quality dataset and well-performing data-driven approaches. Specifically, we first introduce Visual Madlibs for image-based and region-based question answering. Then we introduce referring expressions, where we study both referring expression comprehension and generation, covering both language grounding and generation. Next, we study album summarization, which not only selects the key photos inside an album but also generates a natural language story describing the whole album. Last but not least, we describe multi-target embodied question answering, a task that is even closer to our ultimate goal that requires both language understanding and navigation ability from the AI system.Doctor of Philosoph

Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries

Recognising objects according to a pre-defined fixed set of class labels has been well studied in the Computer Vision. There are a great many practical applications where the subjects that may be of interest are not known beforehand, or so easily delineated, however. In many of these cases natural language dialog is a natural way to specify the subject of interest, and the task achieving this capability (a.k.a, Referring Expression Comprehension) has recently attracted attention. To this end we propose a unified framework, the ParalleL AttentioN (PLAN) network, to discover the object in an image that is being referred to in variable length natural expression descriptions, from short phrases query to long multi-round dialogs. The PLAN network has two attention mechanisms that relate parts of the expressions to both the global visual content and also directly to object candidates. Furthermore, the attention mechanisms are recurrent, making the referring process visualizable and explainable. The attended information from these dual sources are combined to reason about the referred object. These two attention mechanisms can be trained in parallel and we find the combined system outperforms the state-of-art on several benchmarked datasets with different length language input, such as RefCOCO, RefCOCO+ and GuessWhat?!.Comment: 11 page