The field of object segmentation and tracking is moving towards more accurate and reliable methods, with a focus on incorporating motion cues and temporal information. Recent developments have led to significant improvements in long-range object tracking capabilities and the ability to handle object disappearance or occlusion. The integration of optical flow and vision-language models has also shown promising results in camouflaged object segmentation. Additionally, the use of memory-augmented student-teacher learning frameworks has enabled robots to perform dexterous manipulation tasks in a prompt-responsive manner. Notable papers include MoSAM, which achieves state-of-the-art results in video object segmentation and video instance segmentation, and ZS-VCOS, which surpasses supervised methods in zero-shot video camouflaged object segmentation. CAMELTrack also achieves state-of-the-art performance on multiple tracking benchmarks, and Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning demonstrates successful prompt-responsive policies for robotics tasks.