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) …

Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of
semantic segmentation yet remains less explored to date. Recent endeavors regard RGB …

Multi-modal learning has achieved remarkable success by integrating information from
various modalities, achieving superior performance in tasks like recognition and retrieval …

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 …

The development of Large Language Models (LLMs) has significantly advanced various AI
applications in commercial and scientific research fields, such as scientific literature …

In this paper, we address the challenging modality-agnostic semantic segmentation (MaSS),
aiming at centering the value of every modality at every feature granularity. Training with all …

Multi-modal large language models (MLLMs) have shown remarkable abilities in various
visual understanding tasks. However, MLLMs still struggle with fine-grained visual …

Multimodal learning plays a crucial role in enabling machine learning models to fuse and
utilize diverse data sources, such as text, images, and audio, to support a variety of …