mlsanityData to 7 Oct 2026

Rich Image Captioning in the Wild

arXiv 1603.09016 ↗First posted 30 Mar 2016cs.CVcited by 11

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Abstract (arXiv)

We present an image caption system that addresses new challenges of automatically describing images in the wild. The challenges include high quality caption quality with respect to human judgments, out-of-domain data handling, and low latency required in many applications. Built on top of a state-of-the-art framework, we developed a deep vision model that detects a broad range of visual concepts, an entity recognition model that identifies celebrities and landmarks, and a confidence model for the caption output. Experimental results show that our caption engine outperforms previous state-of-the-art systems significantly on both in-domain dataset (i.e. MS COCO) and out of-domain datasets.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2016.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 3; 2024: 0; 2025: 1; 2026: 1.

11 papers.