SPS Feed

Most current models for analyzing multimodal sequences often disregard the imbalanced contributions of individual modal representations caused by varying information densities, as well as the inherent multi-relational interactions across distinct modalities. Consequently, a biased understanding of the intricate interplay among modalities may be fostered, limiting prediction accuracy and effectiveness.

Conventional fine-tuning encounters increasing difficulties given the size of current Pre-trained Language Models, which makes parameter-efficient tuning become the focal point of frontier research. Recent advances in this field is the unified tuning methods that aim to tune the representations of both multi-head attention (MHA) and fully connected feed-forward network (FFN) simultaneously, but they rely on existing tuning methods and do not explicitly model domain knowledge for downstream tasks.

Image set compression (ISC) refers to compressing the sets of semantically similar images. Traditional ISC methods typically aim to eliminate redundancy among images at either signal or frequency domain, but often struggle to handle complex geometric deformations across different images effectively.