CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning
<|reference_start|>CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning: In many applications, robots can benefit from object-level understanding of their environments, including the ability to distinguish object instances and re-identify previously seen instances. Object re-identification is challenging across different viewpoints and in scenes with significant appearance variation arising from weather or lighting changes. Most works on object re-identification focus on specific classes; approaches that address general object re-identification require foreground segmentation and have limited consideration of challenges such as occlusions, outdoor scenes, and illumination changes. To address this problem, we introduce CODa Re-ID: an in-the-wild object re-identification dataset containing 1,037,814 observations of 557 objects of 8 classes under diverse lighting conditions and viewpoints. Further, we propose CLOVER, a representation learning method for object observations that can distinguish between static object instances. Our results show that CLOVER achieves superior performance in static object re-identification under varying lighting conditions and viewpoint changes, and can generalize to unseen instances and classes.<|reference_end|>
@article{lee2024clover:,
    title={CLOVER: Context-aware Long-term Object Viewpoint- and Environment-
  Invariant Representation Learning},
    author={Dongmyeong Lee, Amanda Adkins, Joydeep Biswas},
    journal={arXiv preprint arXiv:2407.09718},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09718},
    primaryClass={cs.CV cs.RO}
}