Trading-off Mutual Information on Feature Aggregation for Face Recognition

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.Comment: Accepted to 22$^{nd}$ IEEE International Conference on Machine Learning and Applications 2023 (ICMLA

Frequency Disentangled Features in Neural Image Compression

The design of a neural image compression network is governed by how well the entropy model matches the true distribution of the latent code. Apart from the model capacity, this ability is indirectly under the effect of how close the relaxed quantization is to the actual hard quantization. Optimizing the parameters of a rate-distortion variational autoencoder (R-D VAE) is ruled by this approximated quantization scheme. In this paper, we propose a feature-level frequency disentanglement to help the relaxed scalar quantization achieve lower bit rates by guiding the high entropy latent features to include most of the low-frequency texture of the image. In addition, to strengthen the de-correlating power of the transformer-based analysis/synthesis transform, an augmented self-attention score calculation based on the Hadamard product is utilized during both encoding and decoding. Channel-wise autoregressive entropy modeling takes advantage of the proposed frequency separation as it inherently directs high-informational low-frequency channels to the first chunks and conditions the future chunks on it. The proposed network not only outperforms hand-engineered codecs, but also neural network-based codecs built on computation-heavy spatially autoregressive entropy models.Comment: Accepted to 30$^{th}$ IEEE International Conference on Image Processing (ICIP 2023

Multi-Context Dual Hyper-Prior Neural Image Compression

Transform and entropy models are the two core components in deep image compression neural networks. Most existing learning-based image compression methods utilize convolutional-based transform, which lacks the ability to model long-range dependencies, primarily due to the limited receptive field of the convolution operation. To address this limitation, we propose a Transformer-based nonlinear transform. This transform has the remarkable ability to efficiently capture both local and global information from the input image, leading to a more decorrelated latent representation. In addition, we introduce a novel entropy model that incorporates two different hyperpriors to model cross-channel and spatial dependencies of the latent representation. To further improve the entropy model, we add a global context that leverages distant relationships to predict the current latent more accurately. This global context employs a causal attention mechanism to extract long-range information in a content-dependent manner. Our experiments show that our proposed framework performs better than the state-of-the-art methods in terms of rate-distortion performance.Comment: Accepted to IEEE 22$^nd$ International Conference on Machine Learning and Applications 2023 (ICMLA) - Selected for Oral Presentatio

AAFACE: Attribute-aware Attentional Network for Face Recognition

In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task. Our proposed network named AAFace utilizes SB attributes to enhance the discriminative ability of FR representation. To achieve this goal, we propose an attribute-aware attentional integration (AAI) module to perform weighted integration of FR with SB feature maps. Our proposed AAI module is not only fully context-aware but also capable of learning complex relationships between input features by means of the sequential multi-scale channel and spatial sub-modules. Experimental results verify the superiority of our proposed network compared with the state-of-the-art (SoTA) SB prediction and FR methods.Comment: Accepted to $30^{th}$ IEEE International Conference on Image Processing (ICIP 2023) as an oral presentatio

The exact synchronization timing between the cleavage embryo stage and duration of progesterone therapy-improved pregnancy rates in frozen embryo transfer cycles: A cross-sectional study

Background: Synchronization between the embryonic stage and the uterine endometrial lining is important in the outcomes of the vitrified-warmed embryo transfer (ET) cycles. Objective: The aim was to investigate the effect of the exact synchronization between the cleavage stage of embryos and the duration of progesterone administration on the improvement of clinical outcomes in frozen embryo transfer (FET) cycles. Materials and Methods: 312 FET cycles were categorized into two groups: (A) day- 3 ET after three days of progesterone administration (n = 177) and (B) day-2 or -4 ET after three days of progesterone administration (n = 135). Group B was further divided into two subgroups: B1: day-2 ET cycles, that the stage of embryos were less than the administrated progesterone and B2: day-4 ET cycles, that the stage of embryos were more than the administrated progesterone. The clinical outcome measures were compared between the groups. Results: The pregnancy outcomes between groups A and B showed a significant differences in the chemical (40.1% vs 27.4%; p = 0.010) and clinical pregnancies (32.8% vs 22.2%; p = 0.040), respectively. The rate of miscarriage tended to be higher and live birth rate tended to be lower in group B than in group A. Also, significantly higher rates were noted in chemical pregnancy, clinical pregnancy, and live birth in group A when compared with subgroup B2. Conclusion: Higher rates of pregnancy and live birth were achieved in day-3 ET after three days of progesterone administration in FET cycles. Key words: Endometrium, Embryo transfer, Pregnancy, Live birth, Progesterone

The Exact Synchronization Timing Between the Cleavage Embryo Stage and Duration of Progesterone Therapy-improved Pregnancy Rates in Frozen Embryo Transfer Cycles: A Cross-sectional Study

Background: Synchronization between the embryonic stage and the uterine endometrial lining is important in the outcomes of the vitrified-warmed embryo transfer (ET) cycles. Objective: The aim was to investigate the effect of the exact synchronization between the cleavage stage of embryos and the duration of progesterone administration on the improvement of clinical outcomes in frozen embryo transfer (FET) cycles. Materials and Methods: 312 FET cycles were categorized into two groups: (A) day- 3 ET after three days of progesterone administration (n = 177) and (B) day-2 or -4 ET after three days of progesterone administration (n = 135). Group B was further divided into two subgroups: B1: day-2 ET cycles, that the stage of embryos were less than the administrated progesterone and B2: day-4 ET cycles, that the stage of embryos were more than the administrated progesterone. The clinical outcome measures were compared between the groups. Results: The pregnancy outcomes between groups A and B showed a significant differences in the chemical (40.1% vs 27.4%; p = 0.010) and clinical pregnancies (32.8% vs 22.2%; p = 0.040), respectively. The rate of miscarriage tended to be higher and live birth rate tended to be lower in group B than in group A. Also, significantly higher rates were noted in chemical pregnancy, clinical pregnancy, and live birth in group A when compared with subgroup B2. Conclusion: Higher rates of pregnancy and live birth were achieved in day-3 ET after three days of progesterone administration in FET cycles. Key words: Endometrium, Embryo transfer, Pregnancy, Live birth, Progesterone