no code implementations • 24 Oct 2023 • Yinjie Lei, Zixuan Wang, Feng Chen, Guoqing Wang, Peng Wang, Yang Yang
Multi-modal 3D scene understanding has gained considerable attention due to its wide applications in many areas, such as autonomous driving and human-computer interaction.
1 code implementation • 6 Sep 2023 • Sijin Chen, Hongyuan Zhu, Mingsheng Li, Xin Chen, Peng Guo, Yinjie Lei, Gang Yu, Taihao Li, Tao Chen
Moreover, we argue that object localization and description generation require different levels of scene understanding, which could be challenging for a shared set of queries to capture.
no code implementations • 26 Aug 2023 • Duo Peng, Qiuhong Ke, Yinjie Lei, Jun Liu
Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain.
1 code implementation • ICCV 2023 • Xin Lin, Chao Ren, Xiao Liu, Jie Huang, Yinjie Lei
Although unsupervised approaches based on generative adversarial networks offer a promising solution for denoising without paired datasets, they are difficult in surpassing the performance limitations of conventional GAN-based unsupervised frameworks without significantly modifying existing structures or increasing the computational complexity of denoisers.
1 code implementation • CVPR 2023 • Sijin Chen, Hongyuan Zhu, Xin Chen, Yinjie Lei, Tao Chen, Gang Yu
Compared with prior arts, our framework has several appealing advantages: 1) Without resorting to numerous hand-crafted components, our method is based on a full transformer encoder-decoder architecture with a learnable vote query driven object decoder, and a caption decoder that produces the dense captions in a set-prediction manner.
1 code implementation • ICCV 2023 • Yuwei Yang, Munawar Hayat, Zhao Jin, Hongyuan Zhu, Yinjie Lei
Given only the class-level semantic information for unseen objects, we strive to enhance the correspondence, alignment and consistency between the visual and semantic spaces, to synthesise diverse, generic and transferable visual features.
1 code implementation • CVPR 2023 • Yuwei Yang, Munawar Hayat, Zhao Jin, Chao Ren, Yinjie Lei
Despite the significant recent progress made on 3D point cloud semantic segmentation, the current methods require training data for all classes at once, and are not suitable for real-life scenarios where new categories are being continuously discovered.
1 code implementation • CVPR 2023 • Zhao Jin, Munawar Hayat, Yuwei Yang, Yulan Guo, Yinjie Lei
The current approaches for 3D visual reasoning are task-specific, and lack pre-training methods to learn generic representations that can transfer across various tasks.
1 code implementation • CVPR 2023 • Ziqin Zhou, BoWen Zhang, Yinjie Lei, Lingqiao Liu, Yifan Liu
Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme.
no code implementations • 20 Aug 2022 • Yanjie Gou, Yinjie Lei, Lingqiao Liu, Yong Dai, Chunxu Shen, Yongqi Tong
Existing works usually formulate the span detection as a 1D token tagging problem, and model the sentiment recognition with a 2D tagging matrix of token pairs.
1 code implementation • CVPR 2022 • Duo Peng, Yinjie Lei, Munawar Hayat, Yulan Guo, Wen Li
In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data.
1 code implementation • CVPR 2022 • Zhao Jin, Yinjie Lei, Naveed Akhtar, Haifeng Li, Munawar Hayat
With that, we develop a large-scale synthetic scene flow dataset GTA-SF.
no code implementations • 9 Jan 2022 • Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, Yulan Guo
In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision.
no code implementations • 4 Dec 2021 • Jian Peng, Dingqi Ye, Bo Tang, Yinjie Lei, Yu Liu, Haifeng Li
This work proposes a general framework named Cycled Memory Networks (CMN) to address the anterograde forgetting in neural networks for lifelong learning.
no code implementations • 3 Dec 2021 • Lianjie Jia, Chenyang Yu, Xiehao Ye, Tianyu Yan, Yinjie Lei, Pingping Zhang
To generate high-quality pseudo-labels and mitigate the impact of clustering errors, we propose a novel clustering relationship modeling framework for unsupervised person Re-ID.
no code implementations • 23 Nov 2021 • Yifan Chang, Wenbo Li, Jian Peng, Bo Tang, Yu Kang, Yinjie Lei, Yuanmiao Gui, Qing Zhu, Yu Liu, Haifeng Li
Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism.
no code implementations • 5 Aug 2021 • Duo Peng, Yinjie Lei, Lingqiao Liu, Pingping Zhang, Jun Liu
In this work, we propose two simple yet effective texture randomization mechanisms, Global Texture Randomization (GTR) and Local Texture Randomization (LTR), for Domain Generalization based SRSS.
1 code implementation • ICCV 2021 • Duo Peng, Yinjie Lei, Wen Li, Pingping Zhang, Yulan Guo
Domain adaptation is critical for success when confronting with the lack of annotations in a new domain.
1 code implementation • 15 Jul 2021 • Wei Liu, Pingping Zhang, Yinjie Lei, Xiaolin Huang, Jie Yang, Michael Ng
The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications.
no code implementations • 19 Oct 2020 • Yinjie Lei, Duo Peng, Pingping Zhang, Qiuhong Ke, Haifeng Li
Based on the MPFL strategy, our framework achieves a novel approach to adapt to the scale and location diversities of the scene change regions.
no code implementations • EMNLP 2021 • Yanjie Gou, Yinjie Lei, Lingqiao Liu, Yong Dai, Chunxu Shen
Incorporating knowledge bases (KB) into end-to-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context.
Ranked #2 on Task-Oriented Dialogue Systems on KVRET
no code implementations • IEEE 2020 • Hao liu, Y ulan Guo, Y anni Ma, Yinjie Lei, and Gongjian Wen
In this paper, we propose a simple yet effective Point Context Encoding (PointCE) module to capture semantic contexts of a point cloud and adaptively highlight intermediate feature maps.
no code implementations • ECCV 2020 • Yan Liu, Lingqiao Liu, Peng Wang, Pingping Zhang, Yinjie Lei
Most existing crowd counting systems rely on the availability of the object location annotation which can be expensive to obtain.
no code implementations • IEEE 2020 • Y anni Ma, Y ulan Guo, Hao liu, Yinjie Lei
In this paper, we propose a Point Global Context Reasoning (PointGCR) module to capture global contextual information along the channel dimension.
no code implementations • 29 Feb 2020 • Yinjie Lei, Yan Liu, Pingping Zhang, Lingqiao Liu
Most existing crowd counting methods require object location-level annotation, i. e., placing a dot at the center of an object.
1 code implementation • 19 Dec 2019 • Jian Peng, Bo Tang, Hao Jiang, Zhuo Li, Yinjie Lei, Tao Lin, Haifeng Li
It is due to two facts: first, as the model learns more tasks, the intersection of the low-error parameter subspace satisfying for these tasks becomes smaller or even does not exist; second, when the model learns a new task, the cumulative error keeps increasing as the model tries to protect the parameter configuration of previous tasks from interference.
no code implementations • 15 Nov 2019 • Yanjie Gou, Yinjie Lei, Lingqiao Liu, Pingping Zhang, Xi Peng
To account for this style shift, the model should adjust its parameters in accordance with entity types.
no code implementations • 8 Oct 2019 • Pingping Zhang, Wei Liu, Yinjie Lei, Hongyu Wang, Huchuan Lu
The proposed method consists of three modules, i. e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning.
no code implementations • ICCV 2019 • Pingping Zhang, Wei Liu, Yinjie Lei, Huchuan Lu, Xiaoyun Yang
To address these issues, in this work we propose a novel deep learning framework, named Cascaded Context Pyramid Network (CCPNet), to jointly infer the occupancy and semantic labels of a volumetric 3D scene from a single depth image.
Ranked #5 on 3D Semantic Scene Completion on NYUv2 (using extra training data)
1 code implementation • 23 Jul 2019 • Wei Liu, Pingping Zhang, Yinjie Lei, Xiaolin Huang, Jie Yang, Ian Reid
In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved.
no code implementations • 1 Mar 2019 • Yinjie Lei, Ziqin Zhou, Pingping Zhang, Yulan Guo, Zijun Ma, Lingqiao Liu
A sketch based 3D shape retrieval
no code implementations • 18 Dec 2018 • Shengqin Jiang, Xiaobo Lu, Yinjie Lei, Lingqiao Liu
Our rationale is that the mask prediction could be better modeled as a binary segmentation problem and the difficulty of estimating the density could be reduced if the mask is known.
no code implementations • 22 Feb 2018 • Pingping Zhang, Wei Liu, Dong Wang, Yinjie Lei, Hongyu Wang, Chunhua Shen, Huchuan Lu
Extensive experiments demonstrate that the proposed algorithm achieves competitive performance in both saliency detection and visual tracking, especially outperforming other related trackers on the non-rigid object tracking datasets.