We present Segment Anything Model 2 (SAM 2), a foundation model towards solving
promptable visual segmentation in images and videos. We build a data engine, which …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

We present Cutie a video object segmentation (VOS) network with object-level memory
reading which puts the object representation from memory back into the video object …

Tokenizer, serving as a translator to map the intricate visual data into a compact latent
space, lies at the core of visual generative models. Based on the finding that existing …

The core of video understanding tasks such as recognition captioning and tracking is to
automatically detect objects or actions in a video and analyze their temporal evolution …

Recent developments of vision large language models (LLMs) have seen remarkable
progress, yet still encounter challenges towards multimodal generalists, such as coarse …

Existing deep video models are limited by specific tasks, fixed input-output spaces, and poor
generalization capabilities, making it difficult to deploy them in real-world scenarios. In this …

Spatially dense self-supervised learning is a rapidly growing problem domain with
promising applications for unsupervised segmentation and pretraining for dense …

In this paper, we explore the visual representations produced from a pre-trained text-to-
video (T2V) diffusion model for video understanding tasks. We hypothesize that the latent …

Current prevailing Video Object Segmentation (VOS) methods usually perform dense
matching between the current and reference frames after extracting their features. One on …