Jie Hu (original) (raw)

	PE3R: Perception-Efficient 3D Reconstruction Jie Hu,Shizun Wang,Xinchao Wang arXiv, 2025 arxiv version /code /video /demo PE3R reconstructs 3D scenes using only 2D images and enables semantic understanding through language.
	ISTR: Mask-Embedding-Based Instance Segmentation Transformer Jie Hu,Yao Lu,Shengchuan Zhang,Liujuan Cao, IEEE Transactions on Image Processing (TIP), 2024 journal version /arxiv version /code This paper proposes a transformer-based instance segmentation framework, which encodes masks into embeddings to regress them.
	Pseudo-label Alignment for Semi-supervised Instance Segmentation Jie Hu*,Chen Chen*,Liujuan Cao Shengchuan Zhang,Annan Shu,Guannan Jiang,Rongrong Ji (*Equal Contribution) IEEE/CVF International Conference on Computer Vision (ICCV), 2023 paper /code This paper proposes a novel framework, PAIS, that aligns the pseudo-labels of unannotated images with varying class and mask quality for semi-supervised instance segmentation, achieving state-of-the-art results on COCO and Cityscapes datasets.
	You Only Segment Once: Towards Real-Time Panoptic Segmentation Jie Hu,Linyan Huang,Tianhe Ren,Shengchuan Zhang, Rongrong Ji, Liujuan Cao IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 paper /code / video In this paper, we show the complex task of panoptic segmentation can run over 30 FPS with competitive PQ performance, which is achieved by the proposed YOSO with a feature pyramid aggregator and a separable dynamic decoder.
	DistilPose: Tokenized Pose Regression with Heatmap Distillation Suhang Ye*,Yingyi Zhang*,Jie Hu*,Liujuan Cao,Shengchuan Zhang,Lei Shen,Jun Wang,Shouhong Ding,Rongrong Ji (*Equal Contribution) IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 paper /code This paper proposes DistilPose that distills heatmap-based human pose estimation models into regression-based ones to speed up the pipeline.
	Architecture Disentanglement for Deep Neural Networks Jie Hu,Liujuan Cao,Qixiang Ye,Tong Tong,Shengchuan Zhang,Ke Li,Feiyue Huang,Rongrong Ji,Ling Shao IEEE/CVF International Conference on Computer Vision (ICCV), oral, 2021 paper /code This paper proposes to disentangle deep neural networks via information bottleneck to understand the inner workings of them.
	Image-to-image Translation via Hierarchical Style Disentanglement Xinyang Li,Shengchuan Zhang,Jie Hu,Liujuan Cao,Xiaopeng Hong,Xudong Mao,Feiyue Huang,Yongjian Wu,Rongrong Ji IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), oral, 2021 paper /code This paper presents a novel method for image-to-image translation that uses a hierarchical tree to re-arrange the image attributes and achieve style disentanglement.
	Information Competing Process for Learning Diversified Representations Jie Hu,Rongrong Ji,Shengchuan Zhang,Xiaoshuai Sun,Qixiang Ye,Chia-Wen Lin,Qi Tian Neural Information Processing Systems (NeurIPS), 2019 paper /code This paper proposes a new approach that separates a representation into two parts with different mutual information constraints.
	Towards Visual Feature Translation Jie Hu,Rongrong Ji,Hong Liu,Shengchuan Zhang,Cheng Deng,Qi Tian IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 paper /code This paper proposes to break through the barrier of using features across different visual search systems.