Modern algorithms for RGB-IR facial recognition— a challenging problem where infrared probe images are matched with visible gallery images—leverage precise and accurate guidance from curated (i.e., labeled) data to bridge large spectral differences. However, supervised cross-spectral face recognition methods are often extremely sensitive due to over-fitting to labels, performing well in some settings but not in others. Moreover, when finetuning on data from additional settings, supervised crossspectral face recognition are prone to catastrophic forgetting. Therefore, we propose a novel unsupervised framework for RGB-IR face recognition to minimize the cost and time inefficiencies pertaining to labeling large-scale, multispectral data required to train supervised cross-spectral recognition methods and to alleviate the effect of forgetting by removing over dependence on hard labels to bridge such large spectral differences. The proposed framework integrates an efficient backbone network architecture with part-based attention models, which collectively enhances common information between visible and infrared faces. Then, the framework is optimized using pseudo-labels and a new cross-spectral memory bank loss. This framework is evaluated on the ARL-VTF and TUFTS datasets, achieving 98.55% and 43.28% true accept rate, respectively. Additionally, we analyze effects of forgetting and show that our framework is less prone to these effects.

Recent advancements like multiple contextual analysis, attention mechanisms, distance-aware optimization, and multi-task guidance have been widely used for supervised person re-identification (ReID), but the implementation and effects of such methods in unsupervised ReID frameworks are non-trivial and unclear, respectively. Moreover, with increasing size and complexity of image- and video-based ReID datasets, manual or semi-automated annotation procedures for supervised ReID are becoming labor intensive and cost prohibitive, which is undesirable especially considering the likelihood of annotation errors increase with scale/complexity of data collections. Therefore, we propose a new iterative clustering framework that is insensitive to annotation errors and over-fitting ReID annotations (i.e., labels). Our proposed unsupervised framework incorporates (a) a novel multi-context group attention architecture that learns a holistic attention map from multiple local and global contexts, (b) an unsupervised clustering loss function that down-weights easily discriminative identities, and (c) a background diversity term that helps cluster persons across different cross-camera views without leveraging any identification or camera labels. We perform extensive analysis using the DukeMTMC-VideoReID and MARS video-based ReID datasets and the MSMT17 image-based ReID dataset. Our approach is shown to provide a new state-of-the-art performance for unsupervised ReID, reducing the rank-1 performance gap between supervised and unsupervised ReID to 1.1%, 12.1%, and 21.9% from 6.1%, 17.9%, and 22.6% for DukeMTMC, MARS, and MSMT17 datasets, respectively.

Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning. However, since the data requirements-including the degree of data curations-are becoming increasingly complex and laborious, there is a critical need for unsupervised methods that are robust to large intra-class variations, such as changes in perspective, illumination, articulated motion, resolution, etc. Therefore, we propose an unsupervised framework for person re-identification which is trained in an end-to-end manner without any pre-training. Our proposed framework leverages a new attention mechanism that combines group convolutions to (1) enhance spatial attention at multiple scales and (2) reduce the number of trainable parameters by 59.6%. Additionally, our framework jointly optimizes the network with agglomerative clustering and instance learning to tackle hard samples. We perform extensive analysis using the Market1501 and DukeMTMC-reID datasets to demonstrate that our method consistently outperforms the state-of-the-art methods (with and without pre-trained weights).