5 papers · 1 filter
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era
Kanzhi Cheng, Wenpo Song, Jiaxin Fan +7
Image captioning has been a longstanding challenge in vision-language research. With the rise of LLMs, modern Vision-Language Models (VLMs) generate detailed and comprehensive imag…
Bounding and Filling: A Fast and Flexible Framework for Image Captioning
Zheng Ma, Changxin Wang, Bo Huang +2
Most image captioning models following an autoregressive manner suffer from significant inference latency. Several models adopted a non-autoregressive manner to speed up the proces…
Food-500 Cap: A Fine-Grained Food Caption Benchmark for Evaluating Vision-Language Models
Zheng Ma, Mianzhi Pan, Wenhan Wu +4
Vision-language models (VLMs) have shown impressive performance in substantial downstream multi-modal tasks. However, only comparing the fine-tuned performance on downstream tasks…
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic Corpora
Kanzhi Cheng, Zheng Ma, Shi Zong +3
Generating visually grounded image captions with specific linguistic styles using unpaired stylistic corpora is a challenging task, especially since we expect stylized captions wit…
Beyond Generic: Enhancing Image Captioning with Real-World Knowledge using Vision-Language Pre-Training Model
Kanzhi Cheng, Wenpo Song, Zheng Ma +3
Current captioning approaches tend to generate correct but "generic" descriptions that lack real-world knowledge, e.g., named entities and contextual information. Considering that…