The recent advancements in audio-visual processing have shown a significant shift towards end-to-end solutions that integrate multiple modalities to enhance performance and robustness. Researchers are increasingly focusing on developing joint audio-visual models that can handle complex scenarios such as overlapping speech, noise, and varying speaker numbers, which were previously challenging for single-modality approaches. These models often incorporate novel attention mechanisms and quality-aware fusion techniques to ensure accurate discrimination and robust performance across diverse environments. Additionally, there is a growing emphasis on self-supervised learning and run-time adaptation to improve the generalization and adaptability of these models, particularly in real-world, dynamic settings. The integration of advanced beamforming techniques with automatic speech recognition systems is also gaining traction, offering promising results in reducing word error rates and enhancing overall transcription accuracy. Notably, the use of large-scale datasets and contrastive learning methods is proving to be effective in pretraining models, thereby reducing the dependency on annotated data and improving performance on downstream tasks. Overall, the field is moving towards more integrated, adaptable, and data-efficient solutions that leverage the strengths of both audio and visual modalities.