The field of computer vision is moving towards addressing the long-standing issues of bias and generalization in various tasks such as object detection, image classification, and segmentation. Researchers are proposing innovative solutions to mitigate the effects of imbalanced datasets and improve the performance of models on rare or unseen categories. Notable advancements include the development of adaptive distillation methods, unbiased recovery and relabeling techniques, and novel prototype enhancement strategies. These approaches have shown significant improvements in state-of-the-art performance and have the potential to enable more robust and generalizable computer vision systems. Noteworthy papers include Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation, which proposes an Adaptive Soft-label Alignment module to calibrate entangled biases, and Unleashing the Power of Vision-Language Models for Long-Tailed Multi-Label Visual Recognition, which introduces a correlation adaptation prompt network to model label correlations from CLIP's textual encoder.