Search Results for author: Lanqing Hong

Found 40 papers, 18 papers with code

DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models

1 code implementation ICCV 2023 Ruiyuan Gao, Chenchen Zhao, Lanqing Hong, Qiang Xu

There is a recent work that directly applies it to OOD detection, which employs a conditional Generative Adversarial Network (cGAN) to enlarge semantic mismatch in the image space.

Out-of-Distribution Detection

Integrating Geometric Control into Text-to-Image Diffusion Models for High-Quality Detection Data Generation via Text Prompt

no code implementations7 Jun 2023 Kai Chen, Enze Xie, Zhe Chen, Lanqing Hong, Zhenguo Li, Dit-yan Yeung

However, the usage of diffusion models to generate high-quality object detection data remains an underexplored area, where not only the image-level perceptual quality but also geometric conditions such as bounding boxes and camera views are essential.

Image Classification Layout-to-Image Generation +2

ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis

1 code implementation18 May 2023 Shoukang Hu, Kaichen Zhou, Kaiyu Li, Longhui Yu, Lanqing Hong, Tianyang Hu, Zhenguo Li, Gim Hee Lee, Ziwei Liu

In this paper, we propose ConsistentNeRF, a method that leverages depth information to regularize both multi-view and single-view 3D consistency among pixels.

3D Reconstruction SSIM

Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts

no code implementations15 May 2023 Yuyang Zhao, Enze Xie, Lanqing Hong, Zhenguo Li, Gim Hee Lee

The text-driven image and video diffusion models have achieved unprecedented success in generating realistic and diverse content.

Denoising Video Editing +1

Boosting Visual-Language Models by Exploiting Hard Samples

no code implementations9 May 2023 Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li

On a comprehensive zero-shot and retrieval benchmark, without training the model from scratch or utilizing additional data, HELIP consistently boosts existing models to achieve leading performance.

Retrieval Zero-Shot Learning

MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation

1 code implementation19 Apr 2023 Chongjian Ge, Junsong Chen, Enze Xie, Zhongdao Wang, Lanqing Hong, Huchuan Lu, Zhenguo Li, Ping Luo

These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities.

3D Object Detection Autonomous Driving +2

Out-of-distribution Few-shot Learning For Edge Devices without Model Fine-tuning

no code implementations13 Apr 2023 Xinyun Zhang, Lanqing Hong

Few-shot learning (FSL) via customization of a deep learning network with limited data has emerged as a promising technique to achieve personalized user experiences on edge devices.

Domain Generalization Few-Shot Learning

Fair-CDA: Continuous and Directional Augmentation for Group Fairness

no code implementations1 Apr 2023 Rui Sun, Fengwei Zhou, Zhenhua Dong, Chuanlong Xie, Lanqing Hong, Jiawei Li, Rui Zhang, Zhen Li, Zhenguo Li

By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable.

Data Augmentation Disentanglement +1

Mixed Autoencoder for Self-supervised Visual Representation Learning

no code implementations CVPR 2023 Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-yan Yeung

Specifically, our MixedAE outperforms MAE by +0. 3% accuracy, +1. 7 mIoU and +0. 9 AP on ImageNet-1K, ADE20K and COCO respectively with a standard ViT-Base.

Contrastive Learning Data Augmentation +1

ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning

no code implementations CVPR 2023 Hao Yang, Lanqing Hong, Aoxue Li, Tianyang Hu, Zhenguo Li, Gim Hee Lee, LiWei Wang

In this work, we first investigate the effects of synthetic data in synthetic-to-real novel view synthesis and surprisingly observe that models trained with synthetic data tend to produce sharper but less accurate volume densities.

Contrastive Learning Generalizable Novel View Synthesis +2

MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation

no code implementations ICCV 2023 Chongjian Ge, Junsong Chen, Enze Xie, Zhongdao Wang, Lanqing Hong, Huchuan Lu, Zhenguo Li, Ping Luo

These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities.

3D Object Detection Autonomous Driving +2

ConQueR: Query Contrast Voxel-DETR for 3D Object Detection

1 code implementation CVPR 2023 Benjin Zhu, Zhe Wang, Shaoshuai Shi, Hang Xu, Lanqing Hong, Hongsheng Li

We thus propose a Query Contrast mechanism to explicitly enhance queries towards their best-matched GTs over all unmatched query predictions.

3D Object Detection object-detection

Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving

no code implementations17 Oct 2022 Longhui Yu, Yifan Zhang, Lanqing Hong, Fei Chen, Zhenguo Li

Specifically, DucTeacher consists of two curriculums, i. e., (1) domain evolving curriculum seeks to learn from the data progressively to handle data distribution discrepancy by estimating the similarity between domains, and (2) distribution matching curriculum seeks to estimate the class distribution for each unlabeled domain to handle class distribution shifts.

Autonomous Driving object-detection +2

Continual Learning by Modeling Intra-Class Variation

1 code implementation11 Oct 2022 Longhui Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, Weiyang Liu

It has been observed that neural networks perform poorly when the data or tasks are presented sequentially.

Continual Learning

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

1 code implementation14 Sep 2022 Kaichen Zhou, Lanqing Hong, Changhao Chen, Hang Xu, Chaoqiang Ye, Qingyong Hu, Zhenguo Li

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames.

Depth Estimation

Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms

no code implementations12 Jun 2022 Runpeng Yu, Hong Zhu, Kaican Li, Lanqing Hong, Rui Zhang, Nanyang Ye, Shao-Lun Huang, Xiuqiang He

Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention.


Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing

no code implementations26 May 2022 Zhili Liu, Jianhua Han, Lanqing Hong, Hang Xu, Kai Chen, Chunjing Xu, Zhenguo Li

On the other hand, for existing SSL methods, it is burdensome and infeasible to use different downstream-task-customized datasets in pre-training for different tasks.

Self-Supervised Learning

Generalizing Few-Shot NAS with Gradient Matching

1 code implementation ICLR 2022 Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng

Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search.

Neural Architecture Search

CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving

no code implementations15 Mar 2022 Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei zhang, Chunjing Xu, Dit-yan Yeung, Xiaodan Liang, Zhenguo Li, Hang Xu

One main reason that impedes the development of truly reliably self-driving systems is the lack of public datasets for evaluating the performance of object detectors on corner cases.

Autonomous Driving object-detection +1

Memory Replay with Data Compression for Continual Learning

1 code implementation ICLR 2022 Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu

In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer.

Autonomous Driving class-incremental learning +6

How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?

no code implementations NeurIPS Workshop ImageNet_PPF 2021 Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng

Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.

Self-Supervised Learning

NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization

1 code implementation ICCV 2021 Haoyue Bai, Fengwei Zhou, Lanqing Hong, Nanyang Ye, S. -H. Gary Chan, Zhenguo Li

In this work, we propose robust Neural Architecture Search for OoD generalization (NAS-OoD), which optimizes the architecture with respect to its performance on generated OoD data by gradient descent.

Domain Generalization Neural Architecture Search +1

Adversarial Robustness for Unsupervised Domain Adaptation

no code implementations ICCV 2021 Muhammad Awais, Fengwei Zhou, Hang Xu, Lanqing Hong, Ping Luo, Sung-Ho Bae, Zhenguo Li

Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.

Adversarial Robustness Unsupervised Domain Adaptation

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

1 code implementation ICCV 2021 Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-yan Yeung

By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training.

Autonomous Driving Image Clustering +2

Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition

2 code implementations20 Jul 2021 Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng

Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution.

Image Classification Long-tail Learning

SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving

no code implementations21 Jun 2021 Jianhua Han, Xiwen Liang, Hang Xu, Kai Chen, Lanqing Hong, Jiageng Mao, Chaoqiang Ye, Wei zhang, Zhenguo Li, Xiaodan Liang, Chunjing Xu

Experiments show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when fine-tuning with different downstream tasks (i. e., detection, semantic/instance segmentation) in autonomous driving domain.

Autonomous Driving Instance Segmentation +5

Contextualizing Meta-Learning via Learning to Decompose

1 code implementation15 Jun 2021 Han-Jia Ye, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De-Chuan Zhan

To this end, we propose Learning to Decompose Network (LeadNet) to contextualize the meta-learned ``support-to-target'' strategy, leveraging the context of instances with one or mixed latent attributes in a support set.

Few-Shot Image Classification Meta-Learning

How Well Does Self-Supervised Pre-Training Perform with Streaming Data?

no code implementations ICLR 2022 Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng

Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.

Representation Learning Self-Supervised Learning

Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation

no code implementations5 Jan 2021 Qijun Luo, Zhili Liu, Lanqing Hong, Chongxuan Li, Kuo Yang, Liyuan Wang, Fengwei Zhou, Guilin Li, Zhenguo Li, Jun Zhu

Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.

Domain Adaptation Semi-supervised Domain Adaptation

DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning

no code implementations1 Jan 2021 Kaichen Zhou, Lanqing Hong, Fengwei Zhou, Binxin Ru, Zhenguo Li, Trigoni Niki, Jiashi Feng

Our method performs co-optimization of the neural architectures, training hyper-parameters and data augmentation policies in an end-to-end fashion without the need of model retraining.

BIG-bench Machine Learning Data Augmentation +1

Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models

1 code implementation NeurIPS Workshop ICBINB 2020 Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang

The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable.

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