The field of Visual Place Recognition (VPR) is witnessing significant advancements, with a growing focus on developing robust and efficient methods for long-term localization in dynamic environments. Recent research has emphasized the importance of sequence-based context correlation, temporal coherence, and rotation equivariance in achieving accurate and reliable VPR. Innovations in deep learning architectures, such as the integration of 1D convolutional encoders, differential temporal operators, and steerable Convolutional Neural Networks (CNNs), have shown promising results in addressing challenges like perceptual aliasing and rotational ambiguity. Furthermore, the development of large-scale datasets and the application of vision-language models (VLMs) have expanded the scope of VPR, enabling more effective and scalable solutions for geo-localization and search and rescue missions. Noteworthy papers in this area include OptiCorNet, which presents a novel sequence modeling framework for VPR, and VLM-Guided Visual Place Recognition, which combines the strengths of VLMs with retrieval-based VPR methods to achieve state-of-the-art performance in geo-localization benchmarks. Additionally, papers like LoopNet and DSFormer have introduced innovative approaches to loop closure detection and visual place recognition, leveraging few-shot learning and Transformer-based cross-learning modules to improve accuracy and efficiency.