Guided Open Vocabulary Image Captioning with Constrained Beam Search
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Abstract (arXiv)
Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels.
Key facts. Cited by 105 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Dec 2016.
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Citing papers per year: 2021: 23; 2022: 18; 2023: 30; 2024: 20; 2025: 7; 2026: 7.
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- 2023Controlled Text Generation with Natural Language Instructionscited by 87
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- 2026FlashTrie: A GPU-Accelerated Constrained Beam Search for Generative Retrieval10 Jul 2026
- 2026Suffix-Constrained Greedy Search Algorithms for Causal Language Models1 Mar 2026
- 2026Online Domain-aware LLM Decoding for Continual Domain Evolution8 Feb 2026
- 2025Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition11 Jun 2025
- 2025Constrained Sampling for Language Models Should Be Easy: An MCMC Perspective6 Jun 2025
- 2025Semantic Probabilistic Control of Language Models4 May 2025
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