no code implementations • ECCV 2020 • Haoang Li, Pyojin Kim, Ji Zhao, Kyungdon Joo, Zhipeng Cai, Zhe Liu , Yun-hui Liu
In Atlanta world, given a set of image lines, we aim to cluster them by the unknown-but-sought VPs whose number is unknown.
no code implementations • 29 Mar 2024 • Honghui Xu, Yingshu Li, Olusesi Balogun, Shaoen Wu, Yue Wang, Zhipeng Cai
In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in this integration.
no code implementations • 29 Mar 2024 • Yue Wang, Zhi Tian, FXin Fan, Zhipeng Cai, Cameron Nowzari, Kai Zeng
The rapid growth of Internet of Things (IoT) has led to the widespread deployment of smart IoT devices at wireless edge for collaborative machine learning tasks, ushering in a new era of edge learning.
1 code implementation • Under review for Transaction 2024 • Mu Hu, Wei Yin, Chi Zhang, Zhipeng Cai, Xiaoxiao Long, Hao Chen, Kaixuan Wang, Gang Yu, Chunhua Shen, Shaojie Shen
For metric depth estimation, we show that the key to a zero-shot single-view model lies in resolving the metric ambiguity from various camera models and large-scale data training.
Ranked #1 on Surface Normals Estimation on NYU Depth v2 (using extra training data)
no code implementations • 17 Mar 2024 • Javad Rafiei Asl, Prajwal Panzade, Eduardo Blanco, Daniel Takabi, Zhipeng Cai
In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks.
no code implementations • 16 Feb 2024 • Xuelun Shen, Zhipeng Cai, Wei Yin, Matthias Müller, Zijun Li, Kaixuan Wang, Xiaozhi Chen, Cheng Wang
Given an architecture, GIM first trains it on standard domain-specific datasets and then combines it with complementary matching methods to create dense labels on nearby frames of novel videos.
no code implementations • 14 Feb 2024 • Prajwal Panzade, Daniel Takabi, Zhipeng Cai
In today's machine learning landscape, fine-tuning pretrained transformer models has emerged as an essential technique, particularly in scenarios where access to task-aligned training data is limited.
1 code implementation • 17 Jan 2024 • Prajwal Panzade, Daniel Takabi, Zhipeng Cai
Advancements in machine learning (ML) have significantly revolutionized medical image analysis, prompting hospitals to rely on external ML services.
1 code implementation • NeurIPS 2023 • Yixing Lao, Xiaogang Xu, Zhipeng Cai, Xihui Liu, Hengshuang Zhao
We present CorresNeRF, a novel method that leverages image correspondence priors computed by off-the-shelf methods to supervise NeRF training.
1 code implementation • NeurIPS 2023 • Xiuhong Lin, Changjie Qiu, Zhipeng Cai, Siqi Shen, Yu Zang, Weiquan Liu, Xuesheng Bian, Matthias Müller, Cheng Wang
While registration of 2D RGB images to 3D point clouds is a long-standing problem in computer vision, no prior work studies 2D-3D registration for event cameras.
no code implementations • 6 Nov 2023 • Gabriela Ben Melech Stan, Diana Wofk, Estelle Aflalo, Shao-Yen Tseng, Zhipeng Cai, Michael Paulitsch, Vasudev Lal
Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions.
no code implementations • 22 Oct 2023 • Hongxiang Gao, Xiangyao Wang, Zhenghua Chen, Min Wu, Zhipeng Cai, Lulu Zhao, Jianqing Li, Chengyu Liu
To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena.
1 code implementation • ICCV 2023 • Zhipeng Cai, Matthias Mueller
The source code, and the WAT dataset are available at https://github. com/IntelLabs/CLNeRF.
no code implementations • 20 Aug 2023 • Shuzhen Chen, Yuan Yuan, Youming Tao, Zhipeng Cai, Dongxiao Yu
Distributed stochastic optimization methods based on Newton's method offer significant advantages over first-order methods by leveraging curvature information for improved performance.
1 code implementation • ICCV 2023 • Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, Chunhua Shen
State-of-the-art (SOTA) monocular metric depth estimation methods can only handle a single camera model and are unable to perform mixed-data training due to the metric ambiguity.
Ranked #19 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)
no code implementations • 7 Jun 2023 • Zhongwei Zhan, Yingjie Wang, Peiyong Duan, Akshita Maradapu Vera Venkata Sai, Zhaowei Liu, Chaocan Xiang, Xiangrong Tong, Weilong Wang, Zhipeng Cai
The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed.
1 code implementation • 16 May 2023 • Ameya Prabhu, Zhipeng Cai, Puneet Dokania, Philip Torr, Vladlen Koltun, Ozan Sener
In this paper, we target such applications, investigating the online continual learning problem under relaxed storage constraints and limited computational budgets.
1 code implementation • 10 Apr 2023 • Motasem Alfarra, Hani Itani, Alejandro Pardo, Shyma Alhuwaider, Merey Ramazanova, Juan C. Pérez, Zhipeng Cai, Matthias Müller, Bernard Ghanem
To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed.
no code implementations • 29 Nov 2022 • Motasem Alfarra, Zhipeng Cai, Adel Bibi, Bernard Ghanem, Matthias Müller
This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries.
no code implementations • 12 Oct 2022 • Zhipeng Cai, Vladlen Koltun, Ozan Sener
The typical approach to address information retention (the ability to retain previous knowledge) is keeping a replay buffer of a fixed size and computing gradients using a mixture of new data and the replay buffer.
no code implementations • 22 Dec 2021 • Yueyang Liu, Hunmin Lee, Zhipeng Cai
Deep neural networks have a wide range of applications in solving various real-world tasks and have achieved satisfactory results, in domains such as computer vision, image classification, and natural language processing.
1 code implementation • ICCV 2021 • Zhipeng Cai, Ozan Sener, Vladlen Koltun
We argue that "online" continual learning, where data is a single continuous stream without task boundaries, enables evaluating both information retention and online learning efficacy.
no code implementations • 7 Jun 2021 • Zhipeng Cai, Zuobin Xiong, Honghui Xu, Peng Wang, Wei Li, Yi Pan
Generative Adversarial Networks (GAN) have promoted a variety of applications in computer vision, natural language processing, etc.
no code implementations • 5 Nov 2020 • Junjie Pang, Jianbo Li, Zhenzhen Xie, Yan Huang, Zhipeng Cai
In this work, we propose a collaborative city digital twin based on FL, a novel paradigm that allowing multiple city DT to share the local strategy and status in a timely manner.
1 code implementation • 30 Aug 2020 • Kaiyang Li, Guangchun Luo, Yang Ye, Wei Li, Shihao Ji, Zhipeng Cai
In this paper, we propose Adversarial Privacy Graph Embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations.
1 code implementation • ICCV 2019 • Zhipeng Cai, Tat-Jun Chin, Vladlen Koltun
First, we show that the consensus maximization tree structure used previously actually contains paths that connect nodes at both adjacent and non-adjacent levels.
1 code implementation • 25 Nov 2018 • Zhipeng Cai, Tat-Jun Chin, Alvaro Parra Bustos, Konrad Schindler
Point cloud registration is a fundamental problem in 3D scanning.
1 code implementation • ECCV 2018 • Zhipeng Cai, Tat-Jun Chin, Huu Le, David Suter
In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization.
no code implementations • ECCV 2018 • Tat-Jun Chin, Zhipeng Cai, Frank Neumann
Robust model fitting plays a vital role in computer vision, and research into algorithms for robust fitting continues to be active.