利用ε-greedy強化基於Transformer的物件偵 測演算法之效能;Performance Enhancement for Transformerbased Object Detection by ε-Greedy

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93128

Title:	利用ε-greedy強化基於Transformer的物件偵測演算法之效能;Performance Enhancement for Transformerbased Object Detection by ε-Greedy
Authors:	翁崇恒;Weng, Chong-Heng
Contributors:	資訊工程學系
Keywords:	深度學習;電腦視覺;物件偵測;Deep Learning;Computer Vision;Object Detection;Transformer
Date:	2023-07-19
Issue Date:	2024-09-19 16:43:51 (UTC+8)
Publisher:	國立中央大學
Abstract:	物件偵測是電腦視覺中，一項重要的基礎研究項目，而近年來，Detection Transformer(DETR)類型的模型在這項領域中脫穎而出，最終達到了state-ofthe-art 的效能水準。而這些研究在基礎的DETR 上，提出許多不同的方法，改進了原始DETR 的效能與訓練效率。然而，我們發現DETR 類型的模型在top K query selection 的環節，可能會有陷入局部最小值的狀況，造成效能無法最佳化。為了改善這個問題，我們在top K query selection 的環節加入了噪音，鼓勵模型去探索更適合預測物件的query。我們的靈感是來自於強化學習中，有ε-greedy 這樣一種方法用來對動作加入噪音。結合這一個加入噪音的方法以及先前的研究，在COCOval2017 上，運用 ResNet50 的backbone，我們改善了DINO +0.3AP 的效能。這個改善說明了ε-greedy 對於有效減輕陷入局部最小值的負面影響。;Object detection is a fundamental task in computer vision. To accomplish the object detection goal, the Detection Transformer (DETR) model has emerged as a promising approach for achieving state-of-the-art performance. Since its introduction, several variants of DETR have been proposed with the aim of improving its performance and training efficiency. However, we find that the DETR-liked model will probably be stuck in a local minimum from top-K query selections, and hence result in inferior performance. To resolve this problem, we add noise to the DETR-liked models with top-K query selections intending to encourage the model to find better queries suitable for bounding box prediction. The rationale is that we are inspired by the ε-greedy idea usually adopted in reinforcement learning which adds noise to action selection. Combining this noise-adding scheme with those successful endeavors, it can improve DINO by +0.3AP with the 4 multi-scale feature maps setting on COCOval2017 using a ResNet-50 backbone. These improvements validate that the ε-greedy is effective to reduce the negative effect of being stuck in the local minimum.
Appears in Collections:	[資訊工程研究所] 博碩士論文

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