WebFeb 2, 2024 · Abstract: Current state-of-the-art image captioning systems usually generated descriptions autoregressively, i.e., every forward step conditions on the given … WebThen, with the help of the partially deterministic prior information and image features, SAIC model non-autoregressively fills all the skipped words with one iteration. Experimental results on the MS COCO benchmark demonstrate that our SAIC model outperforms the preceding non-autoregressive image captioning models while
Partially Non-Autoregressive Image Captioning
Webthe decoding consistency of image captioning, in this paper, we propose a Non-Autoregressive Image Captioning (NA-IC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). Specifically, we con-sider NAIC as a cooperative multi-agent reinforcement learn-ing (MARL) [Bus¸oniu et al., 2010] system, … WebBased on this work, Partially Non-Autoregressive Image Captioning by Fei [7] and semi Non-Autoregressive Image Captioning by Xu et al. [8] partitioned the generated text in subgroups. Words in the hobby chimp squishies
Efficient Modeling of Future Context for Image Captioning
WebMay 18, 2024 · Non-autoregressive image captioning, on the other hand, predicts the entire sentence simultaneously and accelerates the inference process significantly. … WebJul 19, 2024 · To improve the decoding efficiency of long captions, we further propose a non-autoregressive image captioning model, LaBERT, that generates image captions in a length-irrelevant complexity. ... Acknowledgments This work was partially supported by the Key-Area Research and Development Program of Guangdong Province … Webautoregressive and non-autoregressive image caption models, both of which follows on Transformer-based encoder-decoder paradigm. After that, we conduct pilot experiments as well as empirical analy-ses on the effects of context information for caption decoding. 2.1 Model Architecture Generally, AIC and NAIC models hold the same visual encoder hsbc bank newport shropshire