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Found 6 papers, 5 papers with code
TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion
Advanced image fusion methods are devoted to generating the fusion results by aggregating the complementary information conveyed by the source images.
Generative-based Fusion Mechanism for Multi-Modal Tracking
In this context, we seek to uncover the potential of harnessing generative techniques to address the critical challenge, information fusion, in multi-modal tracking.
RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking
To demonstrate the benefits of training on a larger RGB-D data set in general, and RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT, as a baseline for future visual object tracking studies using the new dataset.
A Survey for Deep RGBT Tracking
This survey can be treated as a look-up-table for researchers who are concerned about RGBT tracking.
Temporal Aggregation for Adaptive RGBT Tracking
Specifically, different from traditional Siamese trackers, which only obtain one search image during the process of picking up template-search image pairs, an extra search sample adjacent to the original one is selected to predict the temporal transformation, resulting in improved robustness of tracking performance. As for multi-modal tracking, constrained to the limited RGBT datasets, the adaptive fusion sub-network is appended to our method at the decision level to reflect the complementary characteristics contained in two modalities.
Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking
The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation.
