Downstream Visual

Downstream visual tasks, encompassing diverse applications like image classification, object detection, and 3D scene understanding, are a central focus in computer vision research. Current efforts concentrate on improving the generalization and efficiency of visual models, particularly through advanced pre-training methods like masked image modeling and cross-view completion, often implemented using Vision Transformers or large multimodal models. Researchers are actively investigating ways to bridge the performance gap between self-supervised and supervised learning, addressing issues like feature crowding and improving the robustness of models to various data conditions and downstream tasks. These advancements are crucial for developing more efficient and effective computer vision systems across a wide range of applications.