SamCap: Energy-based Controllable Image Captioning by Gradient-Based Sampling
Authors: 
Yuchen Niu, Min Zhu, Zhihua WeiAuthors Info & Claims
Yuchen Niu, Min Zhu, Zhihua WeiAuthors Info & ClaimsPages 608 - 617
Abstract
Despite remarkable advances in image captioning, existing models still lack the ability to generate controllable and diverse captions. As a solution, controllable image captioning (CIC) has recently gained attention, with the goal of generating image captions that satisfy the constraints of the given control signals. Current CIC methods have two main limitations: (1) They can only handle one specific control signal and lack the ability to handle combinations of multiple control signals. (2) They depend on costly supervised learning from task-specific data, which becomes impractical with increasing model size. To this end, we propose an energy-based sampling method for controllable image captioning, named SamCap. Specifically, by combining various constraint functions with the log likelihood of the image captioner into an energy function, we can generate captions that satisfy the specified constraints through gradient-based sampling. SamCap provides a learning-free and plug-and-play solution, that can integrate with any existing image captioner without task-specific fine-tuning. Extensive results demonstrate that SamCap not only matches the performance of SOTA signal-specific CIC models for single control signals, but also shows significant advantages in handling combinations of multiple control signals.
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Index Terms
- SamCap: Energy-based Controllable Image Captioning by Gradient-Based Sampling
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May 2024
1379 pages
ISBN:9798400706196
DOI:10.1145/3652583
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- Rachada Kongkachandra,
- Klaus Schoeffmann,
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- Luca Rossetto,
- Shin'ichi Satoh,
- Liting Zhou
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