Hyperspectral video contains rich spectral, spatial, and temporal conditions that are crucial
for capturing complex object variations and overcoming the inherent limitations (eg, multi …

RGBT tracking draws increasing attention due to its robustness in multi-modality warranting
(MMW) scenarios, such as nighttime and bad weather, where relying on a single sensing …

Hyperspectral (HS) video captures continuous spectral information of objects, enhancing
material identification in tracking tasks. It is expected to overcome the inherent limitations of …

Due to the limitations of visible (RGB) sensors in challenging scenarios, such as nighttime
and foggy environments, the thermal infrared (TIR) modality draws increasing attention as …

Visual object tracking often faces challenges such as invalid targets and decreased
performance in low-light conditions when relying solely on RGB image sequences. While …

Multi-modal object tracking (MMOT) is an emerging field that combines data from various
modalities,\eg vision (RGB), depth, thermal infrared, event, language and audio, to estimate …

Recent advances in RGB-D tracking have been driven by the synergistic combination of
high-performing RGB-only trackers and auxiliary depth information. However, most existing …

Multi-modal fusion has played a vital role in multi-modal scene understanding. Most existing
methods focus on cross-modal fusion involving two modalities, often overlooking more …