This paper discusses a facial expression recognition model and description generation to build descriptive sentences for images expressions of people in images. Our study shows that YOLOv5 achieves better results than traditional CNN all emotions on the KDEF dataset. In particular, accuracies models emotion are 0.853 0.938, respectively. A generating descriptions based merged architecture is pr...

Abstract Urdu is morphologically rich language and lacks the resources available in English. While several studies on image captioning task English have been published, this among pioneer generative captioning. The study makes key contributions: (i) it presents a new dataset for captioning, (ii) different attention-based architectures language. These attention mechanisms are to language, as tho...

We introduce Discriminative BLEU (∆BLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs. Reference strings are scored for quality by human raters on a scale of [−1, +1] to weight multi-reference BLEU. In tasks involving generation of conversational responses, ∆BLEU correlates reasonably with human judgments and outperforms sent...

We introduce Discriminative BLEU (∆BLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs. Reference strings are scored for quality by human raters on a scale of [−1, +1] to weight multi-reference BLEU. In tasks involving generation of conversational responses, ∆BLEU correlates reasonably with human judgments and outperforms sent...

This study aims to develop a hybridized deep learning model for generating semantically meaningful image captions in Amharic Language. Image captioning is task that combines both computer vision and natural language processing (NLP) domains. However, existing studies the English primarily focus on visual features generate captions, resulting gap between textual inadequate semantic representatio...

This paper presents an overall introduction to the CWMT2008 evaluation and focuses on its two new metrics: BLEU-SBP (Chiang et al., 2008) and linguistic check-point method (Zhou et al., 2008). BLEU-SBP is a revised BLEU with strict brevity penalty. Our experiments validated BLEU-SBP’s effectivity in resolving the nondecomposability problem of both NIST-BLEU and IBMBLEU at sentence level. Lingui...