In this paper, we introduce a new task - interactive image editing via conversational language, where users can guide an agent to edit images via multi-turn natural language dialogue. In each dialogue turn, the agent takes a source image and a natural language description from the user as the input and generates a new image following the textual description. Two new datasets are introduced for this task, Zap-Seq, and DeepFashion-Seq. We propose a novel Sequential Attention Generative Adversarial Network (SeqAttnGAN) framework, which applies a neural state tracker to encode both the source image and the textual description in each dialogue turn and generates high-quality new image consistent with both the preceding images and the dialogue context. To achieve better region-specific text-to-image generation, we also introduce an attention mechanism into the model. Experiments on the two new datasets show that the proposed SeqAttnGAN model outperforms state-of-the-art (SOTA) approaches on the dialogue-based image editing task. Detailed quantitative evaluation and user study also demonstrate that our model is more effective than SOTA baselines on image generation, in terms of both visual quality and text-to-image consistency.
Most approaches have focused on end-to-end neural models based on the encoder-decoder architectures and sequence-to-sequence learning @cite_36 @cite_1 @cite_12 @cite_13 . Das @cite_16 proposed the task of visual dialogue, where the agent can answer questions about images in an interactive dialogue. De Vries @cite_15 introduced the GuessWhat?! game, where a series of questions is asked to pinpoint a speciﬁc object in an image, with yes no NA answers. However, these dialogue settings are purely text-based, where visual feature only plays a supportive role. DeVault @cite_34 investigated building dialogue systems that can help users efficiently explore data through visualizations. Guo @cite_35 introduced an agent presenting candidate images to the user and retrieving new images based on user's feedback. Another piece of related work is @cite_25 for interactive image generation by encoding history information. Different from them, text information is used to guide the image generation editing in our work.
@cite_35 @cite_36 @cite_1 @cite_34 @cite_15 @cite_16 @cite_13 @cite_25 @cite_12
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Existing methods for interactive image retrieval have demonstrated the merit of integrating user feedback, improving retrieval results. However, most current systems rely on restricted forms of user feedback, such as binary relevance responses, or feedback based on a fixed set of relative attributes, which limits their impact. In this paper, we introduce a new approach to interactive image search that enables users to provide feedback via natural language, allowing for more natural and effective interaction. We formulate the task of dialog-based interactive image retrieval as a reinforcement learning problem, and reward the dialog system for improving the rank of the target image during each dialog turn. To avoid the cumbersome and costly process of collecting human-machine conversations as the dialog system learns, we train our system with a user simulator, which is itself trained to describe the differences between target and candidate images. The efficacy of our approach is demonstrated in a footwear retrieval application. Extensive experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface. The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings. We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks. We introduce the first goal-driven training for visual question answering and dialog agents. Specifically, we pose a cooperative 'image guessing' game between two agents -- Qbot and Abot -- who communicate in natural language dialog so that Qbot can select an unseen image from a lineup of images. We use deep reinforcement learning (RL) to learn the policies of these agents end-to-end -- from pixels to multi-agent multi-round dialog to game reward. We demonstrate two experimental results. First, as a 'sanity check' demonstration of pure RL (from scratch), we show results on a synthetic world, where the agents communicate in ungrounded vocabulary, i.e., symbols with no pre-specified meanings (X, Y, Z). We find that two bots invent their own communication protocol and start using certain symbols to ask answer about certain visual attributes (shape color style). Thus, we demonstrate the emergence of grounded language and communication among 'visual' dialog agents with no human supervision. Second, we conduct large-scale real-image experiments on the VisDial dataset, where we pretrain with supervised dialog data and show that the RL 'fine-tuned' agents significantly outperform SL agents. Interestingly, the RL Qbot learns to ask questions that Abot is good at, ultimately resulting in more informative dialog and a better team. In this work we combine two research threads from Vision Graphics and Natural Language Processing to formulate an image generation task conditioned on attributes in a multi-turn setting. By multiturn, we mean the image is generated in a series of steps of user-specified conditioning information. Our proposed approach is practically useful and offers insights into neural interpretability. We introduce a framework that includes a novel training algorithm as well as model improvements built for the multi-turn setting. We demonstrate that this framework generates a sequence of images that match the given conditioning information and that this task is useful for more detailed benchmarking and analysis of conditional image generation methods. Traditional dialog systems used in goal-oriented applications require a lot of domain-specific handcrafting, which hinders scaling up to new domains. End-to-end dialog systems, in which all components are trained from the dialogs themselves, escape this limitation. But the encouraging success recently obtained in chit-chat dialog may not carry over to goal-oriented settings. This paper proposes a testbed to break down the strengths and shortcomings of end-to-end dialog systems in goal-oriented applications. Set in the context of restaurant reservation, our tasks require manipulating sentences and symbols, so as to properly conduct conversations, issue API calls and use the outputs of such calls. We show that an end-to-end dialog system based on Memory Networks can reach promising, yet imperfect, performance and learn to perform non-trivial operations. We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge (, 2014a). We show similar result patterns on data extracted from an online concierge service.