This is the results of the ICME2022 paper "MIRNet: A Robust RGBT Tracking Jointly with Multi-Modal Interaction and Refinement".

RGBT tracking attempts to design a robust all-weather tracker by integrating the complementary features of visible and thermal spectrums. To explore the latent interdependencies across modalities, we propose a novel real-time tracker named MIRNet, which contains a multi-modal interaction module (MIM) and a refinement mechanism (RM), thereby adaptively merging multi-modal features and achieving precise scale estimation. Specifically, to enhance instance representation in lowquality modality, the MIM reinforces discriminative features from one modality to another in a bidirectional way. Considering the negative effects of unreliable modality, we further introduce a gate function in MIM to filter redundancy. To address the problem of random drifting and estimate the precise scale in the online tracking, we present a well-designed RM that combines optical flow and refinement network. Comprehensive experiments on two public RGBT benchmarks validate that our tracker outperforms the state-of-the-art methods.

You can download the raw result MIR-rgbt234.zip and MIR-gtot.zip and mir-lasher.zip

GTOT PR:0.909 SR:0.744 RGBT234 PR:0.816 SR:0.589 lasher PR:0.462 NPR:0.412 SR:0.354

Please cite this paper in your publications if it helps your research:

@inproceedings{hou2022mirnet,
  title={MIRNet: A Robust RGBT Tracking Jointly with Multi-Modal Interaction and Refinement},
  author={Hou, Ruichao and Ren, Tongwei and Wu, Gangshan},
  booktitle={2022 IEEE International Conference on Multimedia and Expo (ICME)},
  pages={1--6},
  year={2022},
  organization={IEEE}
}

@inproceedings{hou2023mtnet,
  title={MTNet: Learning Modality-aware Representation with Transformer for RGBT Tracking},
  author={Hou, Ruichao and Xu, Boyue and Ren, Tongwei and Wu, Gangshan},
  booktitle={2023 IEEE International Conference on Multimedia and Expo (ICME)},
  pages={1--6},
  year={2023},
  organization={IEEE}
}