ArcFace: Additive Angular Margin Loss for Deep Face Recognition
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...o form an implicit decision boundary and minimizing target loss [36]. This statement exists when Mis supervised by all kinds of loss functions (softmaxcross entropy [47], triplet [44] or margin-based [9,12] losses). Our key observation is that the features embedded close to their corresponding class centroids are normally the representative examples, while features far away or closer to other centroids ...
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...orresponding landmarks for all datasets. Images are aligned to 112 112 by similarity transformation with facial landmarks. We train our base model and DDNet on the MS-Celeb-1M dataset [21] cleaned by [9]. The base model we select is modied ResNet-101 [24] released by [9]. As for the DDNet, we use a light-weight channel reduced ResNet-18 network, whose channels for 4 stages are f8, 16, 32, 48g, respe...
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...son re-identication, and action recognition. In this section, we will brie y review those related topics. Set-to-Set Face Recognition. Set-to-set face recognition aims at performing face recognition [57,27,2,29,9,69] using a set of images of a same person. To tackle set-to-set face recognition, traditional methods directly estimate the feature similarity among sets of feature vectors [1,23,5]. Other works seek to...
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...e 1:1 face verication accuracy of the given 5,000 video pairs in our experiments. As shown in Table1, our DDL achieves state-of-the-art performance on the YouTube Face benchmark [57]. It outperforms [9] by 0.16% and other set-to-set face recognition methods by impressive margins. For comparison with dierent aggregation strategies like average pooling, DDL can boost performance by 0.21%, which indic...
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....8 DeepFace [49] 91.4 FaceNet [44] 95.52 NAN [62] 95.72 DeepID2 [48] 93.20 QAN [37] 96.17 C-FAN [20] 96.50 Rao et al. [42] 96.52 Liu et al. [38] 96.21 Rao et al. [41] 94.28 CosFace [53] 97.65 ArcFace [9] 98.02 Average 97.97 Top 1 97.08 DDL 98.18 Table 2. Comparison with dierent participants and aggregation strategy on the IQIYI-VID-FACE challenge. By combining with PolyNet, DDL achieves state-of-the...
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17 citations
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Cites background from "ArcFace: Additive Angular Margin Lo..."
...In addition, they suggest the need to design better loss functions over softmax loss that can improve performance on hard examples and focus on optimiz- ing angles, e.g., (Liu et al., 2017b; Deng et al., 2019; Wang et al., 2018b;a)....
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17 citations
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