Summary of Long-tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction, by Chen-long Duan et al.

Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction

by Chen-Long Duan, Yong Li, Xiu-Shen Wei, Lin Zhao

First submitted to arxiv on: 14 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL) framework is a novel pre-training approach for object detection that addresses common limitations in current methods. By capturing both global contextual semantics and detailed local patterns through Holistic-Local Contrastive Learning, 2DRCL aligns pre-training with object detection tasks. To tackle data imbalance issues inherent in long-tailed distributions, the method employs a dynamic rebalancing strategy that adjusts sampling to better represent underrepresented tail classes. Additionally, Dual Reconstruction addresses simplicity bias by enforcing a reconstruction task aligned with the self-consistency principle. Experimental results on COCO and LVIS v1.0 datasets demonstrate the effectiveness of 2DRCL in improving mAP/AP scores for tail classes.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper creates a new way to train models for object detection that works better than usual methods when there’s a lot of different types of objects. The problem is that current methods can’t handle really rare objects very well, so they get left out. This new method tries to fix this by adjusting how it looks at the data as it trains, and also adding an extra task to make sure the model doesn’t just focus on common objects. Tests show that this works really well!