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The proposed BFA-HARF Tracker effectively alleviates bounding box ambiguity and tracking drift, achieving performance comparable to state-of-the-art methods.
TCTracker is a CLIP-based RGB-T tracking algorithm driven by large language models that uses cross-modal contrastive learning to guide the backbone network in learning target representations based on these descriptions, which effectively leverages the rich semantic information in image-text pairs.
This study proposes a new RGB-T target tracking algorithm, CLRFI, which utilizes the Vision Transformer (ViT) architecture for RGB-T tracking scenarios and implements a comparative learning strategy focused on the target region, thereby optimizing the target’s representation.
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