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Samples of annotations in ICText dataset. Besides the common annotations + (i.e. bounding box and class), we also include the multi-label character quality attributes. + They are represented by (i) red dot for low contrast character, + (ii) green dot dot for blurry + characters, and lastly (iii) blue dot for broken characters.
+Attribute-Guided Curriculum Learning (AGCL) loss proposes to zero out the gradient of difficult characters. + It can also balances the contribution of negative samples through weighting factors and focusing parameters. + The training is split into two phases where AGCL loss is used in the first phase, and the Cross Entropy loss is used in the + second phase. +
+The figure below shows the differences between common existing loss functions used by object detectors + and our proposed AGCL on positive and negative cases during training. +
+Quantitative results of all methods on ICText's test set are shown in the table below. + Models marked with * are tested on a subset of easier images. Both inference speed and GPU memory are tested on Titan + X, and the rest of the hardware specifications can be found in our paper. ± marks the + standard deviation calculated over five runs, and ↑ shows the relative AP improvement of + AGCL-enabled methods over the baseline methods.
+Qualitative results of ABCNet, PAN++, YOLOv4 (baseline), and our proposed + YOLOv4-AGCL are shown in the figures below. Green boxes = true positives; + Red boxes = false positives; + Blue boxes = false negatives. + The character class prediction is shown in the top left corner of each box.
+We show that asking non-AGCL detectors to learn directly from flawed characters has side effects, + i.e., more false positives and false negatives. Additionally, both ABCNet and PAN++ suffer from + granularity issues. In contrast, there are significantly fewer false-positive and false-negative boxes + in our proposed method (i.e., YOLOv4-AGCL), showing that training a detector in an + easy-to-hard fashion guided by quality attributes can achieve better results.
+If you wish to cite the lastest version of the ICText dataset and AGCL:
++ + + Our paper is currently under review. We will update this section when it is published. + +
+