Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection

Unsupervised domain adaptation (UDA) plays a crucial role in object detection when adapting a source-trained detector to a target domain without annotated data. In this paper, we propose a novel and effective four-step UDA approach that leverages self-supervision and trains source and target data concurrently. We harness self-supervised learning to mitigate the lack of ground truth in the target domain. Our method consists of the following steps: (1) identify the region with the highest-confidence set of detections in each target image, which serve as our pseudo-labels; (2) crop the identified region and generate a collection of its augmented versions; (3) combine these latter into a composite image; (4) adapt the network to the target domain using the composed image. Through extensive experiments under cross-camera, cross-weather, and synthetic-to-real scenarios, our approach achieves state-of-the-art performance, improving upon the nearest competitor by more than 2% in terms of mean Average Precision (mAP). The code is available at https://github.com/MohamedTEV/DACA.

A Sparse Representation of Complete Local Binary Pattern Histogram for Human Face Recognition

Human face recognition has been a long standing problem in computer vision and pattern recognition. Facial analysis can be viewed as a two-fold problem, namely (i) facial representation, and (ii) classification. So far, many face representations have been proposed, a well-known method is the Local Binary Pattern (LBP), which has witnessed a growing interest. In this respect, we treat in this paper the issues of face representation as well as classification in a novel manner. On the one hand, we use a variant to LBP, so-called Complete Local Binary Pattern (CLBP), which differs from the basic LBP by coding a given local region using a given central pixel and Sing_ Magnitude difference. Subsequently, most of LBPbased descriptors use a fixed grid to code a given facial image, which technique is, in most cases, not robust to pose variation and misalignment. To cope with such issue, a representative Multi-Resolution Histogram (MH) decomposition is adopted in our work. On the other hand, having the histograms of the considered images extracted, we exploit their sparsity to construct a so-called Sparse Representation Classifier (SRC) for further face classification. Experimental results have been conducted on ORL face database, and pointed out the superiority of our scheme over other popular state-of-the-art techniques.