1 code implementation • 8 Oct 2023 • Ronghao Dang, Jiangyan Feng, Haodong Zhang, Chongjian Ge, Lin Song, Lijun Gong, Chengju Liu, Qijun Chen, Feng Zhu, Rui Zhao, Yibing Song
In order to encompass common detection expressions, we involve emerging vision-language model (VLM) and large language model (LLM) to generate instructions guided by text prompts and object bbxs, as the generalizations of foundation models are effective to produce human-like expressions (e. g., describing object property, category, and relationship).
1 code implementation • 9 Mar 2021 • Gege Qi, Lijun Gong, Yibing Song, Kai Ma, Yefeng Zheng
However, a threat to these systems arises that adversarial attacks make CNNs vulnerable.
no code implementations • ICLR 2021 • Gege Qi, Lijun Gong, Yibing Song, Kai Ma, Yefeng Zheng
We further analyze the KL-divergence of the proposed loss function and find that the loss stabilization term makes the perturbations updated towards a fixed objective spot while deviating from the ground truth.
no code implementations • 20 Jul 2020 • Lijun Gong, Kai Ma, Yefeng Zheng
We formulate a novel distractor-aware loss that encourages large distance between the original image and its distractor in the feature space.
1 code implementation • 20 Jul 2020 • Shaoteng Liu, Lijun Gong, Kai Ma, Yefeng Zheng
In this paper, we propose a Graph REsidual rE-ranking Network (GREEN) to introduce a class dependency prior into the original image classification network.
1 code implementation • 9 Jul 2019 • Qingbin Shao, Lijun Gong, Kai Ma, Hualuo Liu, Yefeng Zheng
Accurate lesion detection in computer tomography (CT) slices benefits pathologic organ analysis in the medical diagnosis process.
no code implementations • 22 Nov 2018 • Yibing Song, Jiawei Zhang, Lijun Gong, Shengfeng He, Linchao Bao, Jinshan Pan, Qingxiong Yang, Ming-Hsuan Yang
We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image.
no code implementations • CVPR 2018 • Yibing Song, Chao Ma, Xiaohe Wu, Lijun Gong, Linchao Bao, WangMeng Zuo, Chunhua Shen, Rynson Lau, Ming-Hsuan Yang
To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.
no code implementations • ICCV 2017 • Yibing Song, Chao Ma, Lijun Gong, Jiawei Zhang, Rynson Lau, Ming-Hsuan Yang
Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training.