Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

1 code implementation • 16 May 2024 • Tianhe Ren, Qing Jiang, Shilong Liu, Zhaoyang Zeng, Wenlong Liu, Han Gao, Hongjie Huang, Zhengyu Ma, Xiaoke Jiang, Yihao Chen, Yuda Xiong, Hao Zhang, Feng Li, Peijun Tang, Kent Yu, Lei Zhang

Empirical results demonstrate the effectiveness of Grounding DINO 1. 5, with the Grounding DINO 1. 5 Pro model attaining a 54. 3 AP on the COCO detection benchmark and a 55. 7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection.

AcademicGPT: Empowering Academic Research

no code implementations • 21 Nov 2023 • Shufa Wei, Xiaolong Xu, Xianbiao Qi, Xi Yin, Jun Xia, Jingyi Ren, Peijun Tang, Yuxiang Zhong, Yihao Chen, Xiaoqin Ren, Yuxin Liang, Liankai Huang, Kai Xie, Weikang Gui, Wei Tan, Shuanglong Sun, Yongquan Hu, Qinxian Liu, Nanjin Li, Chihao Dai, Lihua Wang, Xiaohui Liu, Lei Zhang, Yutao Xie

Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others.

General Knowledge Question Answering

Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning

no code implementations • 23 Mar 2021 • Shiyu Tang, Peijun Tang, Yanxiang Gong, Zheng Ma, Mei Xie

It draws class-wise features closer than coarse feature alignment or class-wise feature alignment only, therefore improves the model's performance to a great extent.

Contrastive Learning Semantic Segmentation +1

Contrastive Disentanglement in Generative Adversarial Networks

no code implementations • 5 Mar 2021 • Lili Pan, Peijun Tang, Zhiyong Chen, Zenglin Xu

Disentanglement is defined as the problem of learninga representation that can separate the distinct, informativefactors of variations of data.

Contrastive Learning Disentanglement