Authors - Rajesh Kapoor, Vishal Goyal, Aasheesh Shukla Abstract - This paper presents a systematic review of visual sarcasm detection research with a focus on learning-based approaches. The review examines input representations, feature extraction methods, model architectures, datasets, and evaluation practices reported in the literature. Studies are analyzed with respect to the use of visual information, including images and image–text pairs, along with associated deep learning frameworks such as convolutional, transformer-based, and hybrid models. A structured search strategy, defined inclusion criteria, and an analytical framework are employed to ensure consistency and reproducibility of the review process. The findings are synthesized to identify prevailing research patterns, methodological limitations, and gaps related to visual feature representation, model design, and experimental consistency. By organizing and comparing existing approaches, this systematic review provides a consolidated reference and supports future research in visual sarcasm detection.
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