As the most fundamental scene understanding tasks, object detection and segmentation
have made tremendous progress in deep learning era. Due to the expensive manual …

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

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 …

Recent advances in contrastive language-image pretraining (CLIP) have demonstrated
strong capabilities in zero-shot classification by aligning visual and textual features at an …

Recent breakthroughs in vision-language models (VLMs) start a new page in the vision
community. The VLMs provide stronger and more generalizable feature embeddings …

Abstract Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities
in various multi-modal tasks. Nevertheless their performance in fine-grained image …

Open-vocabulary semantic segmentation requires models to effectively integrate visual
representations with open-vocabulary semantic labels. While Contrastive Language-Image …

Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially
CLIP in various open-vocabulary tasks, their application to semantic segmentation remains …

This paper reveals a characteristic of DEtection Transformer (DETR) that negatively impacts
its training efficacy, ie, the cross-attention and self-attention layers in DETR decoder have …

CLIP has demonstrated marked progress in visual recognition due to its powerful pre-
training on large-scale image-text pairs. However it still remains a critical challenge: how to …