1 code implementation • 20 Oct 2023 • Zijie Wang, Md Mosharaf Hossain, Shivam Mathur, Terry Cruz Melo, Kadir Bulut Ozler, Keun Hee Park, Jacob Quintero, MohammadHossein Rezaei, Shreya Nupur Shakya, Md Nayem Uddin, Eduardo Blanco
Experimental results demonstrate that monolingual fine-tuning is beneficial if training data can be obtained via distant supervision for the language of interest (5 languages).
Deep learning based methods have become a paradigm for cover song identification (CSI) in recent years, where the ByteCover systems have achieved state-of-the-art results on all the mainstream datasets of CSI.
We evaluate our proposed method on two text-based person retrieval datasets CUHK-PEDES and RSTPReid.
Indeed, color information is an important decision-making accordance for retrieval, but the over-reliance on color would distract the model from other key clues (e. g. texture information, structural information, etc.
Deep learning has become the most popular direction in machine learning and artificial intelligence.
Convolutional neural network (CNN)-based methods have dominated the recent research of cover song identification (CSI).
Ranked #1 on Cover song identification on SHS100K-TEST
The performance of deep neural network (DNN) based methods is highly data-dependent.
Ranked #11 on Audio Classification on ICBHI Respiratory Sound Database (using extra training data)
Many previous methods on text-based person retrieval tasks are devoted to learning a latent common space mapping, with the purpose of extracting modality-invariant features from both visual and textual modality.
Ranked #4 on Text based Person Retrieval on RSTPReid
As the cost and technical difficulty of jamming devices continue to decrease, jamming has become one of the major threats to positioning service.
However, preparation of training data is often a bottleneck in the lifecycle of deploying a deep learning model for production or research.
Frequent non-line-of-sight (NLoS) propagation and poor geometry of available anchor nodes are two significant challenges.