Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection

By Na Dong, Yongqiang Zhang, Ming Ding, Gim Hee Lee. 2021

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Hang Su
2. Jun Zhu
3. Liyuan Wang
4. Xingxing Zhang
5. Yinpeng Dong
6. Vamsi Krishna Ithapu
7. Alan L. Yuille
8. Tianyu Pang

Citations in the corpus

• A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, methodology): “The idea of knowledge distillation is then introduced to other detection frameworks, such as CenterNet [522] (SID [338]), RetinaNet [267] (RILOD [255]), GFLV1 [258] (ERD [117]) and Faster R-CNN [369] (CIFRCN [155], Faster ILOD [337], DMC [506], BNC [99], IOD-ML [229]).”
• Deep Class-Incremental Learning: A Survey (2023, unknown): “, semantic segmentation [164], [165], [166], [167], [174], object detection [175], [176], [177], [178], [179], [180], [181], video understanding [95], [133], medical surgery [182], [183], language model [184], [185], [186], [187], language-vision model [188], [189], etc.”
• Class-Incremental Learning: A Survey (2023, unknown): “…into other tasks, e.g. , semantic segmentation [193], [238], [239], [240], [241], object detection [137], [242], [243], [244], [245], [246], [247], video understanding [91], [161], medical surgery [248], [249], language model [250], [251], [252], [253], language-vision model [217],…”