Image modality is not perfect as it often fails in certain conditions, eg, night and fast motion.
This significantly limits the robustness and versatility of existing multi-modal (ie, Image+ X) …

We present UniBind a flexible and efficient approach that learns a unified representation
space for seven diverse modalities--images text audio point cloud thermal video and event …

Event-based semantic segmentation (ESS) is a fundamental yet challenging task for event
camera sensing. The difficulties in interpreting and annotating event data limit its scalability …

This paper introduces a self-supervised learning framework designed for pre-training neural
networks tailored to dense prediction tasks using event camera data. Our approach utilizes …

We present CEIA, an effective framework for open-world event-based understanding.
Currently training a large event-text model still poses a huge challenge due to the shortage …

Event cameras have recently been shown beneficial for practical vision tasks such as action
recognition thanks to their high temporal resolution power efficiency and reduced privacy …

The multifaceted nature of human perception and comprehension indicates that, when we
think, our body can naturally take any combination of senses, aka, modalities and form a …

This paper studies zero-shot object recognition using event camera data. Guided by CLIP,
which is pre-trained on RGB images, existing approaches achieve zero-shot object …

Research on multi-modal learning dominantly aligns the modalities in a unified space at
training, and only a single one is taken for prediction at inference. However, for a real …

Recently, electroencephalography (EEG) signals have been actively incorporated to decode
brain activity to visual or textual stimuli and achieve object recognition in multi-modal AI …