In this paper, we present a new sequence-to-sequence learning framework for visual
tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem …

Visible-modal object tracking gives rise to a series of downstream multi-modal tracking
tributaries. To inherit the powerful representations of the foundation model, a natural modus …

In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when
missing-modality occurs either during training or testing in real-world situations; and 2) when …

With the emergence of large pretrained vison-language models such as CLIP, transferable
representations can be adapted to a wide range of downstream tasks via prompt tuning …

In the realm of video object tracking auxiliary modalities such as depth thermal or event data
have emerged as valuable assets to complement the RGB trackers. In practice most existing …

Visual object tracking aims to localize the target object of each frame based on its initial
appearance in the first frame. Depending on the input modility tracking tasks can be divided …

Due to the rapid development of computer vision, single-modal (RGB) object tracking has
made significant progress in recent years. Considering the limitation of single imaging …

Abstract Multimodal Visual Object Tracking (VOT) has recently gained significant attention
due to its robustness. Early research focused on fully fine-tuning RGB-based trackers which …

Multimodal (eg, RGB-Depth/RGB-Thermal) fusion has shown great potential for improving
semantic segmentation in complex scenes (eg, indoor/low-light conditions). Existing …

In recent years, visual object tracking, as a prominent research area in computer vision, has
garnered significant attention. To bolster the robustness of trackers across a spectrum of …