AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models

Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models often focus on a limited set of tasks, and generalization abilities of learned representations are unclear. To this end, we propose the AV-SUPERB benchmark that enables general-purpose evaluation of unimodal audio/visual and bimodal fusion representations on 7 datasets covering 5 audio-visual tasks in speech and audio processing. We evaluate 5 recent self-supervised models and show that none of these models generalize to all tasks, emphasizing the need for future study on improving universal model performance. In addition, we show that representations may be improved with intermediate-task fine-tuning and audio event classification with AudioSet serves as a strong intermediate task. We release our benchmark with evaluation code and a model submission platform to encourage further research in audio-visual learning.Comment: Submitted to ICASSP 2024; Evaluation Code: https://github.com/roger-tseng/av-superb Submission Platform: https://av.superbbenchmark.or

The Immune Landscape in Women Cancers

In this chapter, we summarize the latest findings in the field of immuno-oncology of women cancers, particularly ovarian and breast tumors. We describe the relationship between immune parameters and clinical outcomes by evaluating the contribution of different players of the tumor microenvironment, with a particular focus on different immune cell subsets and their essential role during the development of the disease, the response to standard chemotherapy, and to emerging immunotherapeutic approaches. By reviewing the molecular and genetic features of ovarian and breast cancer subtypes, we report on the multitude of factors influencing treatment outcome, with a particular interest on the possible influence of the immune system (i.e., tumor infiltrating lymphocytes, T cells, regulatory T cells, myeloid-derived suppressor cells, dendritic cells, macrophages, B cells, tumor-associated neutrophils). Finally, we discuss emerging immune targets and novel therapeutic modalities that are likely to profoundly influence clinical outcome and prognosis of breast and ovarian cancers in the next future