The field of vision-language tracking and detection is moving towards more robust and accurate methods, particularly in complex and dynamic scenarios. Researchers are exploring new approaches to effectively integrate visual and textual cues, such as aligning target-context cues with dynamic target states and utilizing semantic context. Additionally, there is a growing interest in self-supervised learning methods that can eliminate the need for extensive manual annotations. Noteworthy papers in this area include: ATCTrack, which achieves robust tracking through comprehensive target-context feature modeling. Towards Universal Modal Tracking with Online Dense Temporal Token Learning, which proposes a universal video-level modality-awareness tracking model. SAViL-Det, which introduces a novel semantic-aware vision-language model for multi-script text detection. Decoupled Spatio-Temporal Consistency Learning for Self-Supervised Tracking, which presents a novel self-supervised tracking framework that eliminates the need for box annotations.