Tracking by Natural Language Specification with Long Short-term Context Decoupling

The main challenge of Tracking by Natural Language Specification (TNL) is to predict the movement of the target object by giving two heterogeneous information, e.g., one is the static description of the main characteristics of a video contained in the textual query, i.e., long-term context; the other one is an image patch containing the object and its surroundings cropped from the current frame, i.e., the search area. Currently, most methods still struggle with the rationality of using those two information and simply fusing the two. However, the linguistic information contained in the textual query and the visual representation stored in the search area may sometimes be inconsistent, in which case the direct fusion of the two may lead to conflicts. To address this problem, we propose DecoupleTNL, introducing a video clip containing short-term context information into the framework of TNL and exploring a proper way to reduce the impact when visual representation is inconsistent with linguistic information. Concretely, we design two jointly optimized tasks, i.e., short-term context-matching and long-term context-perceiving. The context-matching task aims to gather the dynamic short-term context information in a period, while the context-perceiving task tends to extract the static long-term context information. After that, we design a long short-term modulation module to integrate both context information for accurate tracking. Extensive experiments have been conducted on three tracking benchmark datasets to demonstrate the superiority of DecoupleTNL.