Joint Visual Grounding and Tracking with Natural Language Specification (2303.12027v1)

Abstract: Tracking by natural language specification aims to locate the referred target in a sequence based on the natural language description. Existing algorithms solve this issue in two steps, visual grounding and tracking, and accordingly deploy the separated grounding model and tracking model to implement these two steps, respectively. Such a separated framework overlooks the link between visual grounding and tracking, which is that the natural language descriptions provide global semantic cues for localizing the target for both two steps. Besides, the separated framework can hardly be trained end-to-end. To handle these issues, we propose a joint visual grounding and tracking framework, which reformulates grounding and tracking as a unified task: localizing the referred target based on the given visual-language references. Specifically, we propose a multi-source relation modeling module to effectively build the relation between the visual-language references and the test image. In addition, we design a temporal modeling module to provide a temporal clue with the guidance of the global semantic information for our model, which effectively improves the adaptability to the appearance variations of the target. Extensive experimental results on TNL2K, LaSOT, OTB99, and RefCOCOg demonstrate that our method performs favorably against state-of-the-art algorithms for both tracking and grounding. Code is available at https://github.com/lizhou-cs/JointNLT.

• The paper presents a unified model that concurrently handles visual grounding and tracking using natural language guidance.
• It leverages a multi-source relation module and semantics-guided temporal modeling to effectively integrate visual and linguistic features.
• Experimental results demonstrate improved tracking precision and recall on benchmarks like TNL2K, LaSOT, and OTB99 compared to separated models.

Joint Visual Grounding and Tracking with Natural Language Specification

The paper "Joint Visual Grounding and Tracking with Natural Language Specification" introduces a novel approach for enhancing the task of tracking objects in video sequences based on natural language descriptions. Tracking by natural language has the potential to create more advanced human-machine interactions by allowing trackers to utilize linguistic information as guidance. This paper addresses significant limitations of prior work, which generally separates visual grounding and tracking into distinct modules. Such a bifurcated approach often overlooks the inherent connections between visual grounding and tracking, making it challenging to train models in an end-to-end fashion.

The proposed framework in this paper reformulates grounding and tracking as a unified task, leveraging a single model to perform both tasks. It achieves this by utilizing a multi-source relation modeling module that effectively builds relationships between visual-language references and the visual content of a current frame. A temporal modeling module is included to improve the model's responsiveness to variations in the appearance of the target by incorporating global semantic information.

This joint model significantly improves upon traditional separated frameworks by allowing for end-to-end learning and simplifying the need for independent grounding and tracking steps. The approach provides notable performance improvements on several standard benchmarks, such as TNL2K, LaSOT, OTB99, and RefCOCOg, surpassing many state-of-the-art algorithms in both tracking precision and recall metrics.

Key Components and Contributions

1. Unified Task Approach: The authors propose a method that treats visual grounding and tracking as a single, cohesive task, which allows for the model to be trained end-to-end efficiently. This consolidation helps in capturing the intricate relationships between language and visual data in a single framework.
2. Multi-Source Relation Modeling: A transformer-based module constructs comprehensive correlations between input references (such as language and historical visual data) and visual frames. This approach effectively harnesses both modality and temporal relationships.
3. Semantics-Guided Temporal Modeling: The paper introduces a temporal modeling module that makes use of previously predicted target states, guided by semantic information from the natural language input. This module enhances the model's ability to remain adaptive to significant target appearance changes across frames.

The experimental results demonstrate substantial improvements over existing separated models, showcasing both a reduction in computational requirements and an increase in accuracy. For instance, extending the application to both visual grounding and natural language-based tracking tasks, the model achieves strong performance metrics, indicating that unifying the two tasks effectively harnesses cross-modal information.

Implications and Future Directions

The implications of this research extend beyond merely improving tracking performance. This work sheds light on the potential for more immersive cognitive systems that think more holistically across modalities. Practically, these developments could enhance computer vision applications that demand nuanced understanding, such as surveillance, autonomous driving, and advanced user interfaces.

Looking towards the future, one could anticipate further exploration into refining the joint modeling of visual and linguistic features, potentially integrating additional sensory data streams, like audio. Moreover, the approach could benefit from exploring the scalability to larger datasets and more complex tracking scenarios, such as those involving multiple interacting objects and more abstract language constructs.

Overall, the paper provides a substantial step forward in natural language-aided visual perception, setting a foundation for further advancements in multisensory integration for artificial intelligence systems.