2 code implementations • 9 Apr 2024 • Jianlang Chen, Xuhong Ren, Qing Guo, Felix Juefei-Xu, Di Lin, Wei Feng, Lei Ma, Jianjun Zhao
To achieve high accuracy on both clean and adversarial data, we propose building a spatial-temporal continuous representation using the semantic text guidance of the object of interest.
no code implementations • 7 Dec 2023 • Kairui Yang, Zihao Guo, Gengjie Lin, Haotian Dong, Die Zuo, Jibin Peng, Zhao Huang, Zhecheng Xu, Fupeng Li, Ziyun Bai, Di Lin
To facilitate the research of NLD simulation, we collect the Language-to-Interaction(L2I) benchmark dataset with 120, 000 natural-language descriptions of object interactions in 6 common types of road topologies.
no code implementations • 3 Aug 2023 • Kairui Yang, Enhui Ma, Jibin Peng, Qing Guo, Di Lin, Kaicheng Yu
To this end, we propose a two-stage generative method, dubbed BEVControl, that can generate accurate foreground and background contents.
no code implementations • 16 Jul 2023 • Wuyuan Xie, Miaohui Wang, Di Lin, Boxin Shi, Jianmin Jiang
With the rapid development of high-resolution 3D vision applications, the traditional way of manipulating surface detail requires considerable memory and computing time.
no code implementations • ICCV 2023 • Haotian Dong, Enhui Ma, Lubo Wang, Miaohui Wang, Wuyuan Xie, Qing Guo, Ping Li, Lingyu Liang, Kairui Yang, Di Lin
In this paper, we propose Cross-View Synthesis Transformer (CVSformer), which consists of Multi-View Feature Synthesis and Cross-View Transformer for learning cross-view object relationships.
no code implementations • 25 May 2023 • Yihao Huang, Yue Cao, Tianlin Li, Felix Juefei-Xu, Di Lin, Ivor W. Tsang, Yang Liu, Qing Guo
Second, we extend representative adversarial attacks against SAM and study the influence of different prompts on robustness.
1 code implementation • ICCV 2023 • Xiaoguang Li, Qing Guo, Rabab Abdelfattah, Di Lin, Wei Feng, Ivor Tsang, Song Wang
In this work, we find that pretraining shadow removal networks on the image inpainting dataset can reduce the shadow remnants significantly: a naive encoder-decoder network gets competitive restoration quality w. r. t.
1 code implementation • 20 Oct 2022 • Zefan Yang, Di Lin, Dong Ni, Yi Wang
To address these issues, we propose a non-iterative method where a stream of varying (pacing) pseudo-masks teach a network via consistency training, named PacingPseudo.
1 code implementation • CVPR 2022 • Xiaoguang Li, Qing Guo, Di Lin, Ping Li, Wei Feng, Song Wang
As a result, the final method takes the advantage of effective semantic & image-level filling for high-fidelity inpainting.
1 code implementation • 7 Jan 2022 • Qing Guo, Jingyang Sun, Felix Juefei-Xu, Lei Ma, Di Lin, Wei Feng, Song Wang
First, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively.
no code implementations • 2 Jan 2022 • Zefan Yang, Di Lin, Dong Ni, Yi Wang
Automatic segmentation of abdominal organs in computed tomography (CT) images can support radiation therapy and image-guided surgery workflows.
no code implementations • 28 Jul 2021 • Qing Guo, Zhijie Wang, Felix Juefei-Xu, Di Lin, Lei Ma, Wei Feng, Yang Liu
3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e. g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds.
1 code implementation • NeurIPS 2020 • Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin
In contrast to the previous methods, RANet configures the information pathways between the pixels in different regions, enabling the region interaction to exchange the regional context for enhancing all of the pixels in the image.
no code implementations • 5 Sep 2019 • Yuanfeng Ji, Hao Chen, Dan Lin, Xiaohua Wu, Di Lin
These kinds of information can be effectively captured by the relation of different anatomical parts of hand bone.
no code implementations • CVPR 2019 • Di Lin, Dingguo Shen, Siting Shen, Yuanfeng Ji, Dani Lischinski, Daniel Cohen-Or, Hui Huang
In this work, we introduce ZigZagNet, which aggregates a richer multi-context feature map by using not only dense top-down and bottom-up propagation, but also by introducing pathways crossing between different levels of the top-down and the bottom-up hierarchies, in a zig-zag fashion.
no code implementations • ECCV 2018 • Di Lin, Yuanfeng Ji, Dani Lischinski, Daniel Cohen-Or, Hui Huang
Accurate semantic image segmentation requires the joint consideration of local appearance, semantic information, and global scene context.
1 code implementation • 12 Aug 2018 • Zhijie Wu, Xiang Wang, Di Lin, Dani Lischinski, Daniel Cohen-Or, Hui Huang
The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code.
Graphics
no code implementations • ICCV 2017 • Di Lin, Guangyong Chen, Daniel Cohen-Or, Pheng-Ann Heng, Hui Huang
Our approach is to use the available depth to split the image into layers with common visual characteristic of objects/scenes, or common "scene-resolution".
Ranked #72 on Semantic Segmentation on NYU Depth v2
no code implementations • ICML 2017 • Guangyong Chen, Shengyu Zhang, Di Lin, Hui Huang, Pheng Ann Heng
While crowdsourcing has been a cost and time efficient method to label massive samples, one critical issue is quality control, for which the key challenge is to infer the ground truth from noisy or even adversarial data by various users.
no code implementations • CVPR 2016 • Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, Jian Sun
Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure.
no code implementations • CVPR 2015 • Di Lin, Xiaoyong Shen, Cewu Lu, Jiaya Jia
Our major contribution is to propose a valve linkage function(VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system.
no code implementations • CVPR 2014 • Cewu Lu, Di Lin, Jiaya Jia, Chi-Keung Tang
Given a single outdoor image, this paper proposes a collaborative learning approach for labeling it as either sunny or cloudy.
no code implementations • CVPR 2014 • Di Lin, Cewu Lu, Renjie Liao, Jiaya Jia
We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification.