Pictionary-Style Word Guessing on Hand-Drawn Object Sketches: Dataset, Analysis and Deep Network Models

Sarvadevabhatla, Ravi Kiran; Surya, Shiv; Mittal, Trisha; Babu, R. Venkatesh

doi:10.1109/tpami.2018.2877996

Cited by 7 publications

(8 citation statements)

References 36 publications

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“…Sketch also provides some unique opportunities compared to photos: (i) As a counterpoint to the sparsity challenge, sketch often lacks distracting background clutter compared to photos, which can benefit automated analysis [3]. (ii) If captured appropriately, the sequential nature of sketch generation can further be exploited to benefit analysis compared to static images [54]. (iii) The sparse and sequential nature of sketch also provides opportunities for high quality sketch generation, where image generation is hampered by the need to fill in pixel detail [5], [55], [56].…”

Section: Unique Challenges and Opportunitiesmentioning

confidence: 99%

“…(iii) The sparse and sequential nature of sketch also provides opportunities for high quality sketch generation, where image generation is hampered by the need to fill in pixel detail [5], [55], [56]. (iv) Sketches can serve as a computer-interaction modality in a way that photos cannot [4], [54], due to the intuitive way humans can generate them without training. For example, people without professional painting training can do casual sketch-based design via sketch-to-photo generation techniques, e.g., scene photo generation [17].…”

Section: Unique Challenges and Opportunitiesmentioning

confidence: 99%

“…Sketch recognition [6] aims to predict the class label of a given sketch, which is one of the most fundamental tasks in computer vision. It has a variety of practical applications including: interactive drawing systems that provide feedback to users [83], sketch-based science education [84], games [5], [54], etc. Both object [5], [6] and scene [85], [86] categories have been studied from a recognition perspective.…”

Section: Recognitionmentioning

confidence: 99%

“…Recently some other interesting sketch based multimodal tasks have emerged, e.g., text-to-sketch generation [317] (e.g., text instruction based conversational authoring of sketches [318]), sketch-based photo classifier generation [20], sketch-based segmentation model generation [319], sketch-based pictionary games [54], [131], [320], sketch to photo contour transfer [321], and sketch-guided object localization in natural photos [322].…”

Section: Other Sketch-related Multi-modal Tasksmentioning

confidence: 99%

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Deep Learning for Free-Hand Sketch: A Survey

Xu¹,

Hospedales²,

Yin³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.

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Section: Unique Challenges and Opportunitiesmentioning

confidence: 99%

Section: Unique Challenges and Opportunitiesmentioning

confidence: 99%

Section: Recognitionmentioning

confidence: 99%

Section: Other Sketch-related Multi-modal Tasksmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Free-Hand Sketch: A Survey

Xu¹,

Hospedales²,

Yin³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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show abstract

“…Recently, based on deep learning techniques, some other interesting sketch based multi-modal tasks have also been studied, e.g., text-to-sketch generation [231], sketch-based photo classifier generation [17], sketch-to-text pictionary game [232], [233], sketch to photo contour transfer [234]. Similarly, the challenges of these tasks are also mainly due to the large domain gap between free-hand sketches and other modalities.…”

Section: Other Sketch-related Multi-modal Tasksmentioning

confidence: 99%

Deep Learning for Free-Hand Sketch: A Survey

Xu¹

2020

Preprint

View full text Add to dashboard Cite

Free-hand sketches are highly hieroglyphic and illustrative, which have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly more popular. The prosperity of deep learning has also immensely promoted the research for the free-hand sketch. This paper presents a comprehensive survey of the free-hand sketch oriented deep learning techniques. The main contents of this survey include: (i) The intrinsic traits and domain-unique challenges of the free-hand sketch are discussed, to clarify the essential differences between free-hand sketch and other data modalities, e.g., natural photo. (ii) The development of the free-hand sketch community in the deep learning era is reviewed, by surveying the existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy. (iii) Moreover, the bottlenecks, open problems, and potential research directions of this community have also been discussed to promote the future works.

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