The article introduces a new dataset called DAPlankton that is aimed at developing and benchmarking domain adaptation methods for image recognition. Different imaging instruments and local differences in plankton species…
Continue readingThe paper presents a new dataset called DAPlankton for developing and benchmarking domain adaptation methods for image recognition. The data consists of phytoplankton images captured using different imaging instruments. This…
Continue readingThe article presents a survey on transfer learning methods applied in the field of Human Activity Recognition (HAR). The authors highlight the challenges faced in sensor-based HAR due to the…
Continue readingThis article by Alexandra DeLucia et al. addresses the challenge of creating unique personas for dialogue agents, particularly in non-real-world settings such as fantasy. Existing models, mainly trained on PersonaChat,…
Continue readingThe article presents a new domain adaptation method for speech enhancement, termed as Remixed2Remixed. It uses Noise2Noise learning to adapt models trained on artificially generated noisy-clean pair data to enhance…
Continue readingThis research paper presents a novel approach to source-free object detection (SFOD), which involves using a pre-trained model on a source domain and unlabeled target domain data. The authors propose…
Continue readingThis research focuses on the creation of a new set of multimodal remote sensing benchmark datasets for cross-city semantic segmentation tasks. The dataset, called the C2Seg, includes two cross-city scenes,…
Continue readingThe study introduces a new set of multimodal remote sensing benchmark datasets (C2Seg dataset) for cross-city semantic segmentation and HighDAN, a high-resolution domain adaptation network. The AI models often face…
Continue readingThis study presents HAMLET, a novel framework aiming to overcome the challenges of real-time, online domain adaptation in semantic segmentation. It addresses the problem of high computational costs and inefficiency…
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