###### Abstract
Notable for their broad-spectrum activity and reduced propensity for resistance, AMPs exhibit substantial potential in medical, agricultural, and food industry applications [4; 5]. In the medical realm, AMPs hold promise for treating drug-resistant infections, preventing surgical complications, and fostering novel therapeutic approaches. In agriculture, they can serve as safer feed additives to reduce antibiotic dependence [6], whereas in the food sector, their bactericidal properties can extend product shelf-life [7]. In the field of AMP generation, researchers have widely adopted various powerful deep learning architectures that were developed for computer vision and video processing, such as CNN, diffusion, transformer, VAE, and GAN [21; 22; 23; 24; 25; 26; 27]. These architectures have proven to be highly effective in capturing complex patterns and generating high-quality outputs. By leveraging the strengths of these video-driven AI architectures, researchers are able to establish robust deep generative networks for AMP generation [28; 29; 30; 31; 32; 33]. This approach allows them to explore the vast search space of peptide sequences and identify promising candidates with antimicrobial properties. Crucially, ongoing research integrates state-of-the-art pattern recognition methods--including video-based analysis--to accelerate AMP screening and functional verification, aligning directly with the objectives of harnessing advanced video processing models to address real-world challenges. By leveraging machine learning, computer vision, and other AI-driven techniques, researchers can more accurately characterise the dynamic interactions between AMPs and target pathogens, thereby expediting both fundamental discoveries and translational applications. **Consequently, we systematically evaluate video-based computational strategies--particularly advanced computer vision architectures--for their capacity to advance AMP generative modelling, elucidate hidden sequence-function relationships, and enhance the robustness of generative power in silico frameworks. With the abuse of antibiotics and the non-specific killing of traditional antibiotics, many bacteria, viruses, parasites, and tumours have developed multidrug resistance, which seriously threatens people's life and health safety [34]. In addition, frequent epidemics have also led to an increase in antibiotic resistance. For example, with the large-scale outbreak of the new coronavirus since 2019, it is estimated that the number of excess deaths caused by the new coronavirus worldwide has exceeded 5.42 million [35]. Among them, China (including mainland China and Hong Kong, Macao, and Taiwan) has a total of 18.22 million confirmed cases and more than 30,000 deaths [36]. During this period, the use of antibiotics was inevitable, resulting in an increase in antibiotic resistance during the new coronavirus (COVID-19) pandemic [37]. Today, the problem of antibiotic resistance has become a public health issue worldwide [38]. AMPs generally refer to amino acid sequences with a length of less than 200, such as \"SDKEVDEVDAALSDLEITLE\", where each letter represents an amino acid [39]. AMPs have broad-spectrum antimicrobial activity and can quickly kill various targets, including bacteria, viruses, fungi, parasites, and tumour cells [40]. Many of them are pure natural peptides, making them potentially promising therapeutic drugs [41]. Traditional biological experimental methods for screening new antimicrobial peptides from many candidate peptide chains are expensive, time-consuming, and labour-intensive [42]. Therefore, in silico methods are necessary to screen a few more likely candidate peptide chains through machine learning or deep learning methods and then perform computer and biological experimental verification to narrow the range of candidate peptide chains and improve the efficiency of screening new drugs.