8 papers
SigLIP-HD by Fine-to-Coarse Supervision
Lihe Yang, Zhen Zhao, Hengshuang Zhao
High-quality visual representation is a long-standing pursuit in computer vision. In the context of multimodal LLMs (MLLMs), feeding higher-resolution images can produce more fine-…
UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation
Lihe Yang, Zhen Zhao, Hengshuang Zhao
Semi-supervised semantic segmentation (SSS) aims at learning rich visual knowledge from cheap unlabeled images to enhance semantic segmentation capability. Among recent works, UniM…
SOEDiff: Efficient Distillation for Small Object Editing
Yiming Wu, Qihe Pan, Zhen Zhao +3
In this paper, we delve into a new task known as small object editing (SOE), which focuses on text-based image inpainting within a constrained, small-sized area. Despite the remark…
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model
Zhenhua Xu, Yujia Zhang, Enze Xie +5
Multimodal large language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-text…
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation
Zhanyu Wang, Longyue Wang, Zhen Zhao +7
While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of in…
Depth Anything V2
Lihe Yang, Bingyi Kang, Zilong Huang +4
This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation mo…