Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

The rapid development of deep learning has driven significant progress in image semantic
segmentation—a fundamental task in computer vision. Semantic segmentation algorithms …

In this work we address various segmentation tasks each traditionally tackled by distinct or
partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Abstract The CLIP and Segment Anything Model (SAM) are remarkable vision foundation
models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is …

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel
within an image based on a wide range of text descriptions. In this work we introduce a …

Multimodal large language models (MLLMs) have recently achieved impressive general-
purpose vision-language capabilities through visual instruction tuning. However current …

This paper presents a new mechanism to facilitate the training of mask transformers for
efficient panoptic segmentation, democratizing its deployment. We observe that due to the …

Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic
groups from an open set of categories. Most existing methods explore utilizing pre-trained …