iCap: Interactive Image Captioning with Predictive Text
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Abstract (arXiv)
In this paper we study a brand new topic of interactive image captioning with human in the loop. Different from automated image captioning where a given test image is the sole input in the inference stage, we have access to both the test image and a sequence of (incomplete) user-input sentences in the interactive scenario. We formulate the problem as Visually Conditioned Sentence Completion (VCSC). For VCSC, we propose asynchronous bidirectional decoding for image caption completion (ABD-Cap). With ABD-Cap as the core module, we build iCap, a web-based interactive image captioning system capable of predicting new text with respect to live input from a user. A number of experiments covering both automated evaluations and real user studies show the viability of our proposals.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2020.
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.
- 2023Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memorycited by 1
- 2023Embedded Heterogeneous Attention Transformer for Cross-lingual Image Captioningcited by 1
- 2022Interactive Attention AI to translate low light photos to captions for night scene understanding in women safetycited by 0
- 2023Embedded Heterogeneous Attention Transformer for Cross-lingual Image Captioning19 Jul 2023
- 2023Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory6 Jun 2023
- 2022Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety4 Jan 2022