The field of object detection and classification is witnessing significant advancements with a focus on improving performance in complex scenes and real-world applications. Researchers are exploring innovative approaches to address challenges such as occlusions, clutter, and class imbalance. One notable direction is the incorporation of additional information about object positioning and context to enhance classification and detection accuracy. Another area of focus is the development of dynamic and adaptive models that can refine features and attention mechanisms to prioritize discriminative regions and improve performance in diverse benchmarks. Noteworthy papers in this area include:

• DyCAF-Net, which introduces a dynamic class-aware fusion network that achieves significant improvements in precision and mAP across various benchmarks.
• Learning Using Privileged Information for Litter Detection, which presents a novel approach that combines privileged information with deep learning object detection to improve litter detection while maintaining model efficiency.