Despite the advances in the field of Face Recognition (FR), the precision of
these methods is not yet sufficient. To improve the FR performance, this paper
proposes a technique to aggregate the outputs of two state-of-the-art (SOTA)
deep FR models, namely ArcFace and AdaFace. In our approach, we leverage the
transformer attention mechanism to exploit the relationship between different
parts of two feature maps. By doing so, we aim to enhance the overall
discriminative power of the FR system. One of the challenges in feature
aggregation is the effective modeling of both local and global dependencies.
Conventional transformers are known for their ability to capture long-range
dependencies, but they often struggle with modeling local dependencies
accurately. To address this limitation, we augment the self-attention mechanism
to capture both local and global dependencies effectively. This allows our
model to take advantage of the overlapping receptive fields present in
corresponding locations of the feature maps. However, fusing two feature maps
from different FR models might introduce redundancies to the face embedding.
Since these models often share identical backbone architectures, the resulting
feature maps may contain overlapping information, which can mislead the
training process. To overcome this problem, we leverage the principle of
Information Bottleneck to obtain a maximally informative facial representation.
This ensures that the aggregated features retain the most relevant and
discriminative information while minimizing redundant or misleading details. To
evaluate the effectiveness of our proposed method, we conducted experiments on
popular benchmarks and compared our results with state-of-the-art algorithms.
The consistent improvement we observed in these benchmarks demonstrates the
efficacy of our approach in enhancing FR performance.

本文提出一种利用两种最新的深度人脸识别模型 ArcFace 和 AdaFace 的输出进行聚合的技术，通过利用 Transformer 注意机制来改善人脸识别系统的总体区分能力，并通过引入信息瓶颈原理来确保聚合特征保留最相关和区分能力最强的信息。在流行的基准测试中，我们的方法在人脸识别性能上展现出了一致的改进，证明了其有效性。

特征聚合的互信息折衷方法在人脸识别中的应用

Trading-off Mutual Information on Feature Aggregation for Face  Recognition

The design choices in the Transformer attention mechanism, including weak
inductive bias and quadratic computational complexity, have limited its
application for modeling long sequences. In this paper, we introduce Mega, a
simple, theoretically grounded, single-head gated attention mechanism equipped
with (exponential) moving average to incorporate inductive bias of
position-aware local dependencies into the position-agnostic attention
mechanism. We further propose a variant of Mega that offers linear time and
space complexity yet yields only minimal quality loss, by efficiently splitting
the whole sequence into multiple chunks with fixed length. Extensive
experiments on a wide range of sequence modeling benchmarks, including the Long
Range Arena, neural machine translation, auto-regressive language modeling, and
image and speech classification, show that Mega achieves significant
improvements over other sequence models, including variants of Transformers and
recent state space models.

本文介绍了 Mega，这是一种简单，从理论上得到支持的单头门控注意力机制，具有指数移动平均数以将位置感知的局部依赖性的归纳偏差纳入位置不可知的注意力机制中。通过将整个序列有效地分成多个具有固定长度的块以实现线性时间和空间复杂度，该文进一步提出了 Mega 的变体。在长序列建模、神经机器翻译、自回归语言建模以及图像和语音分类等广泛测试中，证明了 Mega 优于其他序列模型，包括 Transformer 的变体和最近的状态空间模型。