Deep Learning for Free-Hand Sketch: A Survey <s> Other Sketch-Related Multi-Modal Tasks <s> The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. Similarly, performance on multi-disciplinary tasks such as Visual Question Answering (VQA) is considered a marker for gauging progress in Computer Vision. In our work, we bring games and VQA together. Specifically, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, an elementary version of Visual Question Answering task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Notably, Sketch-QA involves asking a fixed question ("What object is being drawn?") and gathering open-ended guess-words from human guessers. We analyze the resulting dataset and present many interesting findings therein. To mimic Pictionary-style guessing, we subsequently propose a deep neural model which generates guess-words in response to temporally evolving human-drawn sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games. <s> BIB001 </s> Deep Learning for Free-Hand Sketch: A Survey <s> Other Sketch-Related Multi-Modal Tasks <s> Modelling human free-hand sketches has become topical recently, driven by practical applications such as fine-grained sketch based image retrieval (FG-SBIR). Sketches are clearly related to photo edge-maps, but a human free-hand sketch of a photo is not simply a clean rendering of that photo's edge map. Instead there is a fundamental process of abstraction and iconic rendering, where overall geometry is warped and salient details are selectively included. In this paper we study this sketching process and attempt to invert it. We model this inversion by translating iconic free-hand sketches to contours that resemble more geometrically realistic projections of object boundaries, and separately factorise out the salient added details. This factorised re-representation makes it easier to match a free-hand sketch to a photo instance of an object. Specifically, we propose a novel unsupervised image style transfer model based on enforcing a cyclic embedding consistency constraint. A deep FG-SBIR model is then formulated to accommodate complementary discriminative detail from each factorised sketch for better matching with the corresponding photo. Our method is evaluated both qualitatively and quantitatively to demonstrate its superiority over a number of state-of-the-art alternatives for style transfer and FG-SBIR. <s> BIB002 </s> Deep Learning for Free-Hand Sketch: A Survey <s> Other Sketch-Related Multi-Modal Tasks <s> Sketching is an effective communication medium that augments and enhances what can be communicated in text. We introduce Sketchforme, the first neural-network-based system that can generate complex sketches based on text descriptions specified by users. Sketchforme's key contribution is to factor complex sketch rendering into layout and rendering subtasks using neural networks. The sketches composed by Sketchforme are expressive and realistic: we show in our user study that these sketches convey descriptions better than human-generated sketches in several cases, and 36.5% of those sketches were identified as human-generated. We develop some interactive applications using these generated sketches, and show that Sketchforme can significantly improve language learning applications and support intelligent language-based sketching assistants. <s> BIB003 </s> Deep Learning for Free-Hand Sketch: A Survey <s> Other Sketch-Related Multi-Modal Tasks <s> The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. In our work, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, a guessing task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Sketch-QA involves asking a fixed question (“What object is being drawn?”) and gathering open-ended guess-words from human guessers. We analyze the resulting dataset and present many interesting findings therein. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn object sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games. <s> BIB004
Recently, based on deep learning techniques, some other interesting sketch based multi-modal tasks have also been studied, e.g., text-to-sketch generation BIB003 , sketch-based photo classifier generation  , sketch-to-text pictionary game BIB001 , BIB004 , sketch to photo contour transfer BIB002 . Similarly, the challenges of these tasks are also mainly due to the large domain gap between free-hand sketches and other modalities.