Gaussian Harmony: Attaining Fairness in Diffusion-based Face Generation Models

Diffusion models have achieved great progress in face generation. However, these models amplify the bias in the generation process, leading to an imbalance in distribution of sensitive attributes such as age, gender and race. This paper proposes a novel solution to this problem by balancing the facial attributes of the generated images. We mitigate the bias by localizing the means of the facial attributes in the latent space of the diffusion model using Gaussian mixture models (GMM). Our motivation for choosing GMMs over other clustering frameworks comes from the flexible latent structure of diffusion model. Since each sampling step in diffusion models follows a Gaussian distribution, we show that fitting a GMM model helps us to localize the subspace responsible for generating a specific attribute. Furthermore, our method does not require retraining, we instead localize the subspace on-the-fly and mitigate the bias for generating a fair dataset. We evaluate our approach on multiple face attribute datasets to demonstrate the effectiveness of our approach. Our results demonstrate that our approach leads to a more fair data generation in terms of representational fairness while preserving the quality of generated samples.

Self-Supervised Learning of Whole and Component-Based Semantic Representations for Person Re-Identification

Interactive Segmentation Models (ISMs) like the Segment Anything Model have significantly improved various computer vision tasks, yet their application to Person Re-identification (ReID) remains limited. On the other hand, existing semantic pre-training models for ReID often have limitations like predefined parsing ranges or coarse semantics. Additionally, ReID and Clothes-Changing ReID (CC-ReID) are usually treated separately due to their different domains. This paper investigates whether utilizing precise human-centric semantic representation can boost the ReID performance and improve the generalization among various ReID tasks. We propose SemReID, a self-supervised ReID model that leverages ISMs for adaptive part-based semantic extraction, contributing to the improvement of ReID performance. SemReID additionally refines its semantic representation through techniques such as image masking and KoLeo regularization. Evaluation across three types of ReID datasets -- standard ReID, CC-ReID, and unconstrained ReID -- demonstrates superior performance compared to state-of-the-art methods. In addition, recognizing the scarcity of large person datasets with fine-grained semantics, we introduce the novel LUPerson-Part dataset to assist ReID methods in acquiring the fine-grained part semantics for robust performance.

Whole-body Detection, Recognition and Identification at Altitude and Range

In this paper, we address the challenging task of whole-body biometric detection, recognition, and identification at distances of up to 500m and large pitch angles of up to 50 degree. We propose an end-to-end system evaluated on diverse datasets, including the challenging Biometric Recognition and Identification at Range (BRIAR) dataset. Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images. After detection, we extract body images and employ a feature extractor for recognition. We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios. Our method achieves an average F1 score of 98.29% at IoU = 0.7 and demonstrates strong performance in recognition accuracy and true acceptance rate at low false acceptance rates compared to existing models. On a test set of 100 subjects with 444 distractors, our model achieves a rank-20 recognition accuracy of 75.13% and a TAR@1%FAR of 54.09%.