no code implementations • 24 Nov 2023 • Yuyang Zhao, Zhiwen Yan, Enze Xie, Lanqing Hong, Zhenguo Li, Gim Hee Lee
We introduce Animate124 (Animate-one-image-to-4D), the first work to animate a single in-the-wild image into 3D video through textual motion descriptions, an underexplored problem with significant applications.
no code implementations • 16 Oct 2023 • Kai Chen, Chunwei Wang, Kuo Yang, Jianhua Han, Lanqing Hong, Fei Mi, Hang Xu, Zhengying Liu, Wenyong Huang, Zhenguo Li, Dit-yan Yeung, Lifeng Shang, Xin Jiang, Qun Liu
The rapid advancement of large language models (LLMs) presents both opportunities and challenges, particularly concerning unintentional generation of harmful and toxic responses.
no code implementations • 10 Oct 2023 • Kaican Li, Yifan Zhang, Lanqing Hong, Zhenguo Li, Nevin L. Zhang
This indicates that while pre-trained representations may help improve downstream in-distribution performance, they could have minimal or even adverse effects on generalization in certain OOD scenarios of the downstream task if not used properly.
no code implementations • 9 Oct 2023 • Zhili Liu, Kai Chen, Yifan Zhang, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-yan Yeung, James Kwok
In this work, we introduce Geom-Erasing, a novel approach that successfully removes the implicit concepts with either an additional accessible classifier or detector model to encode geometric information of these concepts into text domain.
no code implementations • 4 Oct 2023 • Ruiyuan Gao, Kai Chen, Enze Xie, Lanqing Hong, Zhenguo Li, Dit-yan Yeung, Qiang Xu
With MagicDrive, we achieve high-fidelity street-view synthesis that captures nuanced 3D geometry and various scene descriptions, enhancing tasks like BEV segmentation and 3D object detection.
no code implementations • 3 Oct 2023 • Weisen Jiang, Baijiong Lin, Han Shi, Yu Zhang, Zhenguo Li, James T. Kwok
The proposed PERU-FFT and PERU-LoRA outperform existing reusing model methods by a large margin and achieve comparable performance to using a fine-tuned model per task.
no code implementations • 2 Oct 2023 • Zhenhua Xu, Yujia Zhang, Enze Xie, Zhen Zhao, Yong Guo, Kwan-Yee. K. Wong, Zhenguo Li, Hengshuang Zhao
In this paper, we present DriveGPT4, an interpretable end-to-end autonomous driving system utilizing LLMs.
1 code implementation • 1 Oct 2023 • Haiming Wang, Huajian Xin, Chuanyang Zheng, Lin Li, Zhengying Liu, Qingxing Cao, Yinya Huang, Jing Xiong, Han Shi, Enze Xie, Jian Yin, Zhenguo Li, Heng Liao, Xiaodan Liang
Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47. 1% to 50. 4%.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(Pass@100 metric)
1 code implementation • 30 Sep 2023 • Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, Zhenguo Li
We hope PIXART-$\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.
1 code implementation • 27 Sep 2023 • Chuanyang Zheng, Haiming Wang, Enze Xie, Zhengying Liu, Jiankai Sun, Huajian Xin, Jianhao Shen, Zhenguo Li, Yu Li
In addition, we introduce Conjecture Correction, an error feedback mechanism designed to interact with prover to refine formal proof conjectures with prover error messages.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(Pass@100 metric)
1 code implementation • 21 Sep 2023 • Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu
Our MetaMath-7B model achieves 66. 4% on GSM8K and 19. 4% on MATH, exceeding the state-of-the-art models of the same size by 11. 5% and 8. 7%.
Ranked #21 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
no code implementations • 10 Sep 2023 • Shuchen Xue, Mingyang Yi, Weijian Luo, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, Zhi-Ming Ma
Based on our analysis, we propose SA-Solver, which is an improved efficient stochastic Adams method for solving diffusion SDE to generate data with high quality.
1 code implementation • ICCV 2023 • Yutao Hu, Qixiong Wang, Wenqi Shao, Enze Xie, Zhenguo Li, Jungong Han, Ping Luo
In this paper, we address this issue from two perspectives.
no code implementations • 15 Aug 2023 • Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James T. Kwok
The LLM is then asked to predict the masked number with a candidate answer $A$ embedded in the template: ``If we know the answer to the above question is $\{A\}$, what is the value of unknown variable ${\bf x}$?''
2 code implementations • ICCV 2023 • Haiyang Wang, Hao Tang, Shaoshuai Shi, Aoxue Li, Zhenguo Li, Bernt Schiele, LiWei Wang
Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems.
Ranked #8 on
3D Object Detection
on nuScenes
no code implementations • 17 Jul 2023 • Tianyang Hu, Fei Chen, Haonan Wang, Jiawei Li, Wenjia Wang, Jiacheng Sun, Zhenguo Li
The minimizer of this distance is characterized as the optimal data-dependent latent that most effectively capitalizes on the generator's capacity.
no code implementations • 13 Jul 2023 • Nevin L. Zhang, Kaican Li, Han Gao, Weiyan Xie, Zhi Lin, Zhenguo Li, Luning Wang, Yongxiang Huang
Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s).
1 code implementation • 12 Jul 2023 • Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, Xihui Liu
Despite the stunning ability to generate high-quality images by recent text-to-image models, current approaches often struggle to effectively compose objects with different attributes and relationships into a complex and coherent scene.
no code implementations • 5 Jul 2023 • Jingwei Zhang, Han Shi, Jincheng Yu, Enze Xie, Zhenguo Li
Generative models can be categorized into two types: explicit generative models that define explicit density forms and allow exact likelihood inference, such as score-based diffusion models (SDMs) and normalizing flows; implicit generative models that directly learn a transformation from the prior to the data distribution, such as generative adversarial nets (GANs).
no code implementations • 4 Jul 2023 • Weijian Luo, Hao Jiang, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Zhihua Zhang
In image generation experiments, the proposed DCD is capable of training an energy-based model for generating the Celab-A $32\times 32$ dataset, which is comparable to existing EBMs.
1 code implementation • 4 Jul 2023 • Shentong Mo, Enze Xie, Ruihang Chu, Lewei Yao, Lanqing Hong, Matthias Nießner, Zhenguo Li
Recent Diffusion Transformers (e. g., DiT) have demonstrated their powerful effectiveness in generating high-quality 2D images.
Ranked #1 on
Point Cloud Generation
on ShapeNet Car
no code implementations • 28 Jun 2023 • Ruihang Chu, Enze Xie, Shentong Mo, Zhenguo Li, Matthias Nießner, Chi-Wing Fu, Jiaya Jia
We introduce a new diffusion-based approach for shape completion on 3D range scans.
no code implementations • 7 Jun 2023 • Kai Chen, Enze Xie, Zhe Chen, Yibo Wang, Lanqing Hong, Zhenguo Li, Dit-yan Yeung
However, the usage of diffusion models to generate the high-quality object detection data remains an underexplored area, where not only image-level perceptual quality but also geometric conditions such as bounding boxes and camera views are essential.
no code implementations • 5 Jun 2023 • Yimeng Chen, Tianyang Hu, Fengwei Zhou, Zhenguo Li, ZhiMing Ma
The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models.
no code implementations • 29 May 2023 • Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, Zhihua Zhang
To demonstrate the effectiveness and universality of Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion models and refining existing GAN models.
no code implementations • 24 May 2023 • Mingyang Yi, Jiacheng Sun, Zhenguo Li
To understand this contradiction, we empirically verify the difference between the sufficiently trained diffusion model and the empirical optima.
1 code implementation • 18 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.
no code implementations • 15 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.
no code implementations • 9 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.
1 code implementation • 19 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.
1 code implementation • 19 Apr 2023 • Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, Yu Li
The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that enhance this ability.
Ranked #2 on
Math Word Problem Solving
on SVAMP
no code implementations • ICCV 2023 • Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, Zhenguo Li
This paper proposes DiffFit, a parameter-efficient strategy to fine-tune large pre-trained diffusion models that enable fast adaptation to new domains.
no code implementations • CVPR 2023 • Lewei Yao, Jianhua Han, Xiaodan Liang, Dan Xu, Wei zhang, Zhenguo Li, Hang Xu
This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection (OVD).
no code implementations • 3 Apr 2023 • Tianqi Wang, Sukmin Kim, Wenxuan Ji, Enze Xie, Chongjian Ge, Junsong Chen, Zhenguo Li, Ping Luo
In addition, we propose a new task, end-to-end motion and accident prediction, which can be used to directly evaluate the accident prediction ability for different autonomous driving algorithms.
no code implementations • 1 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.
1 code implementation • 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.
1 code implementation • ICCV 2023 • Yuanfeng Ji, Zhe Chen, Enze Xie, Lanqing Hong, Xihui Liu, Zhaoqiang Liu, Tong Lu, Zhenguo Li, Ping Luo
We propose a simple, efficient, yet powerful framework for dense visual predictions based on the conditional diffusion pipeline.
Ranked #2 on
Monocular Depth Estimation
on SUN-RGBD
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.
no code implementations • 22 Feb 2023 • Yikai Wang, Jianan Wang, Guansong Lu, Hang Xu, Zhenguo Li, Wei zhang, Yanwei Fu
In the image manipulation phase, SeMani adopts a generative model to synthesize new images conditioned on the entity-irrelevant regions and target text descriptions.
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.
1 code implementation • 24 Dec 2022 • Feng Xue, Zi He, Chuanlong Xie, Falong Tan, Zhenguo Li
This advance raises a natural question: Can we leverage the diversity of multiple pre-trained models to improve the performance of post hoc detection methods?
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
no code implementations • 31 Oct 2022 • Shipeng Yan, Lanqing Hong, Hang Xu, Jianhua Han, Tinne Tuytelaars, Zhenguo Li, Xuming He
In this work, we focus on learning a VLP model with sequential chunks of image-text pair data.
no code implementations • 17 Oct 2022 • Qishi Dong, Awais Muhammad, Fengwei Zhou, Chuanlong Xie, Tianyang Hu, Yongxin Yang, Sung-Ho Bae, Zhenguo Li
We evaluate our paradigm on a diverse model zoo consisting of 35 models for various OoD tasks and demonstrate: (i) model ranking is better correlated with fine-tuning ranking than previous methods and up to 9859x faster than brute-force fine-tuning; (ii) OoD generalization after model ensemble with feature selection outperforms the state-of-the-art methods and the accuracy on most challenging task DomainNet is improved from 46. 5\% to 50. 6\%.
no code implementations • 17 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.
1 code implementation • 9 Oct 2022 • Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, LiWei Wang
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D.
Ranked #1 on
3D Object Detection
on SUN-RGBD
no code implementations • 20 Sep 2022 • Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei zhang, Zhenguo Li, Chunjing Xu, Hang Xu
We further design a concept dictionary~(with descriptions) from various online sources and detection datasets to provide prior knowledge for each concept.
1 code implementation • 14 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.
no code implementations • 14 Jul 2022 • Zhou Liu, YuJun Li, Zhengying Liu, Lin Li, Zhenguo Li
We define the normalized form of trigonometric identities, design a set of rules for the proof and put forward a method which can generate theoretically infinite trigonometric identities.
no code implementations • 14 Jul 2022 • Mingyang Yi, Ruoyu Wang, Jiachen Sun, Zhenguo Li, Zhi-Ming Ma
The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data.
no code implementations • CVPR 2022 • Ning Kang, Shanzhao Qiu, Shifeng Zhang, Zhenguo Li, Shutao Xia
Generative model based image lossless compression algorithms have seen a great success in improving compression ratio.
1 code implementation • 8 Jun 2022 • Runjian Chen, Yao Mu, Runsen Xu, Wenqi Shao, Chenhan Jiang, Hang Xu, Zhenguo Li, Ping Luo
In this paper, we propose CO^3, namely Cooperative Contrastive Learning and Contextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner.
no code implementations • 26 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.
2 code implementations • 25 May 2022 • Jiahui Gao, Renjie Pi, Yong Lin, Hang Xu, Jiacheng Ye, Zhiyong Wu, Weizhong Zhang, Xiaodan Liang, Zhenguo Li, Lingpeng Kong
In this paradigm, the synthesized data from the PLM acts as the carrier of knowledge, which is used to train a task-specific model with orders of magnitude fewer parameters than the PLM, achieving both higher performance and efficiency than prompt-based zero-shot learning methods on PLMs.
1 code implementation • CVPR 2022 • Minbin Huang, Zhijian Huang, Changlin Li, Xin Chen, Hang Xu, Zhenguo Li, Xiaodan Liang
It is able to find top 0. 16\% and 0. 29\% architectures on average on two search spaces under the budget of only 50 models.
no code implementations • CVPR 2022 • Jianan Wang, Guansong Lu, Hang Xu, Zhenguo Li, Chunjing Xu, Yanwei Fu
Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical application.
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.
2 code implementations • ICLR 2022 • Xingchen Wan, Binxin Ru, Pedro M. Esperança, Zhenguo Li
Searching for the architecture cells is a dominant paradigm in NAS.
no code implementations • 15 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.
no code implementations • ICLR 2022 • Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong, Stephen M. S. Lee, James T. Kwok
Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields.
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.
1 code implementation • 27 Jan 2022 • Weijun Hong, Guilin Li, Weinan Zhang, Ruiming Tang, Yunhe Wang, Zhenguo Li, Yong Yu
Neural architecture search (NAS) has shown encouraging results in automating the architecture design.
no code implementations • CVPR 2022 • Aoxue Li, Peng Yuan, Zhenguo Li
Semi-Supervised object detection (SSOD) aims to improve the generalization ability of object detectors with large-scale unlabeled images.
no code implementations • CVPR 2022 • Sarah Parisot, Pedro M. Esperanca, Steven McDonagh, Tamas J. Madarasz, Yongxin Yang, Zhenguo Li
In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer.
no code implementations • 10 Dec 2021 • Qi Sun, Hexin Dong, Zewei Chen, Jiacheng Sun, Zhenguo Li, Bin Dong
Gradient-based methods for the distributed training of residual networks (ResNets) typically require a forward pass of the input data, followed by back-propagating the error gradient to update model parameters, which becomes time-consuming as the network goes deeper.
no code implementations • 7 Dec 2021 • Tianyang Hu, Jun Wang, Wenjia Wang, Zhenguo Li
Comparing to cross-entropy, square loss has comparable generalization error but noticeable advantages in robustness and model calibration.
1 code implementation • NeurIPS 2021 • Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang, Ruiming Tang, Yong Yu, Zhenguo Li
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency.
no code implementations • NeurIPS 2021 • Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li
First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
no code implementations • ICLR 2022 • Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, Chunjing Xu
In this paper, we introduce a large-scale Fine-grained Interactive Language-Image Pre-training (FILIP) to achieve finer-level alignment through a cross-modal late interaction mechanism, which uses a token-wise maximum similarity between visual and textual tokens to guide the contrastive objective.
1 code implementation • 5 Nov 2021 • Chenxu Zhu, Bo Chen, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu
To address these three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), to select significant feature interactions, appropriate interaction functions and necessary embedding dimensions automatically in a unified framework.
no code implementations • NeurIPS 2021 • Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li
To eliminate the requirement of saving separate models for different target datasets, we propose a novel setting that starts from a pretrained deep generative model and compresses the data batches while adapting the model with a dynamical system for only one epoch.
no code implementations • NeurIPS 2021 • Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li
In this paper, we discuss lossless compression using normalizing flows which have demonstrated a great capacity for achieving high compression ratios.
Ranked #1 on
Image Compression
on ImageNet32
no code implementations • ICLR 2022 • Yao Zhu, Jiacheng Sun, Zhenguo Li
Adversarial transferability enables attackers to generate adversarial examples from the source model to attack the target model, which has raised security concerns about the deployment of DNNs in practice.
no code implementations • ICLR 2022 • Xiaojiang Yang, Yi Wang, Jiacheng Sun, Xing Zhang, Shifeng Zhang, Zhenguo Li, Junchi Yan
Nonlinear ICA is a fundamental problem in machine learning, aiming to identify the underlying independent components (sources) from data which is assumed to be a nonlinear function (mixing function) of these sources.
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.
no code implementations • NeurIPS Workshop DLDE 2021 • Qi Sun, Hexin Dong, Zewei Chen, Weizhen Dian, Jiacheng Sun, Yitong Sun, Zhenguo Li, Bin Dong
Backpropagation algorithm is indispensable for training modern residual networks (ResNets) and usually tends to be time-consuming due to its inherent algorithmic lockings.
no code implementations • 13 Sep 2021 • Kaichen Zhou, Lanqing Hong, Shoukang Hu, Fengwei Zhou, Binxin Ru, Jiashi Feng, Zhenguo Li
In view of these, we propose DHA, which achieves joint optimization of Data augmentation policy, Hyper-parameter and Architecture.
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.
Ranked #1 on
Domain Generalization
on NICO Vehicle
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.
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.
no code implementations • ICCV 2021 • Yao Zhu, Jiacheng Ma, Jiacheng Sun, Zewei Chen, Rongxin Jiang, Zhenguo Li
We find that adversarial training contributes to obtaining an energy function that is flat and has low energy around the real data, which is the key for generative capability.
no code implementations • ICCV 2021 • Lewei Yao, Renjie Pi, Hang Xu, Wei zhang, Zhenguo Li, Tong Zhang
In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs.
no code implementations • ICCV 2021 • Hang Xu, Ning Kang, Gengwei Zhang, Chuanlong Xie, Xiaodan Liang, Zhenguo Li
Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks.
no code implementations • 15 Jul 2021 • Jiahui Gao, Hang Xu, Han Shi, Xiaozhe Ren, Philip L. H. Yu, Xiaodan Liang, Xin Jiang, Zhenguo Li
Transformer-based pre-trained language models like BERT and its variants have recently achieved promising performance in various natural language processing (NLP) tasks.
Ranked #10 on
Semantic Textual Similarity
on MRPC
no code implementations • 21 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.
1 code implementation • 21 Jun 2021 • Jiageng Mao, Minzhe Niu, Chenhan Jiang, Hanxue Liang, Jingheng Chen, Xiaodan Liang, Yamin Li, Chaoqiang Ye, Wei zhang, Zhenguo Li, Jie Yu, Hang Xu, Chunjing Xu
To facilitate future research on exploiting unlabeled data for 3D detection, we additionally provide a benchmark in which we reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
no code implementations • CVPR 2021 • Aoxue Li, Zhenguo Li
To this end, we propose a simple yet effective Transformation Invariant Principle (TIP) that can be flexibly applied to various meta-learning models for boosting the detection performance on novel class objects.
1 code implementation • CVPR 2021 • Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu
To the best of our knowledge, this is one of the first to adopt differentiable environment splitting method to enable stable predictions across environments without environment index information, which achieves the state-of-the-art performance on datasets with strong spurious correlation, such as Colored MNIST.
1 code implementation • 15 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.
no code implementations • NeurIPS 2021 • Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, LiWei Wang
We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features.
no code implementations • CVPR 2021 • Lewei Yao, Renjie Pi, Hang Xu, Wei zhang, Zhenguo Li, Tong Zhang
For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search; To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches.
2 code implementations • CVPR 2021 • Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li
While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
no code implementations • 13 May 2021 • Wenqi Shao, Hang Yu, Zhaoyang Zhang, Hang Xu, Zhenguo Li, Ping Luo
To address this problem, we develop a probability-based pruning algorithm, called batch whitening channel pruning (BWCP), which can stochastically discard unimportant channels by modeling the probability of a channel being activated.
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.
3 code implementations • CVPR 2021 • Shifeng Zhang, Chen Zhang, Ning Kang, Zhenguo Li
We also propose a lossless compression algorithm based on iVPF.
no code implementations • 8 Mar 2021 • Jian Ding, Enze Xie, Hang Xu, Chenhan Jiang, Zhenguo Li, Ping Luo, Gui-Song Xia
Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks.
1 code implementation • 25 Feb 2021 • Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang, Zhenguo Li, James T. Kwok
A surprising result is that diagonal elements in the attention map are the least important compared with other attention positions.
1 code implementation • ICLR 2021 • Peidong Liu, Gengwei Zhang, Bochao Wang, Hang Xu, Xiaodan Liang, Yong Jiang, Zhenguo Li
For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.
2 code implementations • ICCV 2021 • Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Peize Sun, Zhenguo Li, Ping Luo
Unlike most recent methods that focused on improving accuracy of image classification, we present a novel contrastive learning approach, named DetCo, which fully explores the contrasts between global image and local image patches to learn discriminative representations for object detection.
no code implementations • 21 Jan 2021 • Haotian Ye, Chuanlong Xie, Yue Liu, Zhenguo Li
One of the definitions of OOD accuracy is worst-domain accuracy.
no code implementations • 5 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.
no code implementations • CVPR 2021 • Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu
Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications.
no code implementations • 1 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.
no code implementations • ICCV 2021 • Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei zhang, Zhenguo Li, Luc van Gool
Here we present a novel self-supervised 3D Object detection framework that seamlessly integrates the geometry-aware contrast and clustering harmonization to lift the unsupervised 3D representation learning, named GCC-3D.
no code implementations • 1 Jan 2021 • Xu Lan, Steven McDonagh, Shaogang Gong, Jiali Wang, Zhenguo Li, Sarah Parisot
Semi-Supervised Few-shot Learning (SS-FSL) investigates the benefit of incorporating unlabelled data in few-shot settings.
no code implementations • 1 Jan 2021 • Yimin Huang, YuJun Li, Zhenguo Li, Zhihua Zhang
Moreover, comparisons between different initial designs with the same model show the advantage of the proposed optimal design.
no code implementations • 1 Jan 2021 • Yao Zhu, Jiacheng Sun, Zewei Chen, Zhenguo Li
We justify the algorithm with a linear model that the added saliency maps pull data away from its closest decision boundary.
no code implementations • 1 Jan 2021 • Hang Xu, Ning Kang, Gengwei Zhang, Xiaodan Liang, Zhenguo Li
The resulting model zoo is more training efficient than SOTA NAS models, e. g. 6x faster than RegNetY-16GF, and 1. 7x faster than EfficientNetB3.
2 code implementations • 1 Jan 2021 • Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li
While existing NAS methods mostly design architectures on one single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks.
no code implementations • 22 Dec 2020 • Fengwei Zhou, Jiawei Li, Chuanlong Xie, Fei Chen, Lanqing Hong, Rui Sun, Zhenguo Li
Automated data augmentation has shown superior performance in image recognition.
1 code implementation • 17 Dec 2020 • Haoyue Bai, Rui Sun, Lanqing Hong, Fengwei Zhou, Nanyang Ye, Han-Jia Ye, S. -H. Gary Chan, Zhenguo Li
To address that, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization.
1 code implementation • 16 Dec 2020 • Huifeng Guo, Bo Chen, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He
In this paper, we propose a novel embedding learning framework for numerical features in CTR prediction (AutoDis) with high model capacity, end-to-end training and unique representation properties preserved.
no code implementations • 7 Dec 2020 • Gengwei Zhang, Yiming Gao, Hang Xu, Hao Zhang, Zhenguo Li, Xiaodan Liang
Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention.
Ranked #16 on
Panoptic Segmentation
on COCO test-dev
no code implementations • 4 Dec 2020 • Xiao-Yun Zhou, Jiacheng Sun, Nanyang Ye, Xu Lan, Qijun Luo, Bo-Lin Lai, Pedro Esperanca, Guang-Zhong Yang, Zhenguo Li
Among previous normalization methods, Batch Normalization (BN) performs well at medium and large batch sizes and is with good generalizability to multiple vision tasks, while its performance degrades significantly at small batch sizes.
no code implementations • 18 Nov 2020 • Bo Xiong, Yimin Huang, Hanrong Ye, Steffen Staab, Zhenguo Li
MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter space by factorial design and exploitation of evaluation results by factorial analysis.
1 code implementation • 3 Nov 2020 • Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Xiaozhi Fang, Yixing Xu, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zhicheng Liu, Xinghao Chen, Kai Han, Han Shu, Dehua Song, Yunhe Wang, Wei zhang, Chunjing Xu, Zhenguo Li, Wenzhi Liu, Tong Zhang
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
no code implementations • 3 Sep 2020 • Qi Sun, Hexin Dong, Zewei Chen, Weizhen Dian, Jiacheng Sun, Yitong Sun, Zhenguo Li, Bin Dong
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which are time-consuming for deep ResNets.
1 code implementation • ECCV 2020 • Hang Xu, Shaoju Wang, Xinyue Cai, Wei zhang, Xiaodan Liang, Zhenguo Li
In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
Ranked #9 on
Lane Detection
on CurveLanes
no code implementations • ECCV 2020 • Wenshuo Ma, Tingzhong Tian, Hang Xu, Yimin Huang, Zhenguo Li
By carefully analyzing the existing bounding box patterns on the feature hierarchy, we design a flexible and tight hyper-parameter space for anchor configurations.
no code implementations • ECCV 2020 • Xin Chen, Yawen Duan, Zewei Chen, Hang Xu, Zihao Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li
In spite of its remarkable progress, many algorithms are restricted to particular search spaces.
Ranked #12 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
1 code implementation • 11 Jul 2020 • Yimin Huang, Yu-Jun Li, Hanrong Ye, Zhenguo Li, Zhihua Zhang
The evaluation of hyperparameters, neural architectures, or data augmentation policies becomes a critical model selection problem in advanced deep learning with a large hyperparameter search space.
no code implementations • 2 Jul 2020 • Yifei Wang, Dan Peng, Furui Liu, Zhenguo Li, Zhitang Chen, Jiansheng Yang
Adversarial Training (AT) is proposed to alleviate the adversarial vulnerability of machine learning models by extracting only robust features from the input, which, however, inevitably leads to severe accuracy reduction as it discards the non-robust yet useful features.
no code implementations • 16 Jun 2020 • Jiacheng Sun, Xiangyong Cao, Hanwen Liang, Weiran Huang, Zewei Chen, Zhenguo Li
In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc.
no code implementations • 13 Jun 2020 • Chuanlong Xie, Haotian Ye, Fei Chen, Yue Liu, Rui Sun, Zhenguo Li
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.
3 code implementations • NeurIPS 2020 • Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, Li-Wei Wang
We study locally differentially private (LDP) bandits learning in this paper.
no code implementations • CVPR 2020 • Aoxue Li, Weiran Huang, Xu Lan, Jiashi Feng, Zhenguo Li, Li-Wei Wang
Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples.
Ranked #1 on
Few-Shot Image Classification
on ImageNet (1-shot)
1 code implementation • CVPR 2020 • Xiawu Zheng, Rongrong Ji, Qiang Wang, Qixiang Ye, Zhenguo Li, Yonghong Tian, Qi Tian
In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space.
4 code implementations • 25 Mar 2020 • Bin Liu, Chenxu Zhu, Guilin Li, Wei-Nan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu
By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.
Ranked #28 on
Click-Through Rate Prediction
on Criteo
no code implementations • 18 Feb 2020 • Linpu Fang, Hang Xu, Zhili Liu, Sarah Parisot, Zhenguo Li
In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fullyannotated data and fully exploiting cheap data with imagelevel labels.
no code implementations • 18 Feb 2020 • Hang Xu, Linpu Fang, Xiaodan Liang, Wenxiong Kang, Zhenguo Li
Finally, an InterDomain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts globally.
no code implementations • 23 Jan 2020 • Zewei Chen, Fengwei Zhou, George Trimponias, Zhenguo Li
Despite recent progress, the problem of approximating the full Pareto front accurately and efficiently remains challenging.
no code implementations • 22 Jan 2020 • Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng, Zhenguo Li
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.
no code implementations • 24 Dec 2019 • Yimin Huang, Weiran Huang, Liang Li, Zhenguo Li
In this paper, we mainly consider the scenario in which we have a common model set used for model averaging instead of selecting a single final model via a model selection procedure to account for this model's uncertainty to improve the reliability and accuracy of inferences.
no code implementations • 22 Nov 2019 • Lewei Yao, Hang Xu, Wei zhang, Xiaodan Liang, Zhenguo Li
In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection.
1 code implementation • NeurIPS 2020 • Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang
In this work, we propose BONAS (Bayesian Optimized Neural Architecture Search), a sample-based NAS framework which is accelerated using weight-sharing to evaluate multiple related architectures simultaneously.
1 code implementation • 26 Sep 2019 • Guilin Li, Xing Zhang, Zitong Wang, Matthias Tan, Jiashi Feng, Zhenguo Li, Tong Zhang
Recently, the efficiency of automatic neural architecture design has been significantly improved by gradient-based search methods such as DARTS.
no code implementations • 25 Sep 2019 • Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang
Inspired by the nature of the graph structure of a neural network, we propose BOGCN-NAS, a NAS algorithm using Bayesian Optimization with Graph Convolutional Network (GCN) predictor.
no code implementations • 13 Sep 2019 • Hanwen Liang, Shifeng Zhang, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang, Zhenguo Li
Therefore, we propose a simple and effective algorithm, named "DARTS+", to avoid the collapse and improve the original DARTS, by "early stopping" the search procedure when meeting a certain criterion.
2 code implementations • 16 May 2019 • Lin Lan, Zhenguo Li, Xiaohong Guan, Pinghui Wang
Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.
no code implementations • 28 Nov 2018 • Steven McDonagh, Sarah Parisot, Fengwei Zhou, Xing Zhang, Ales Leonardis, Zhenguo Li, Gregory Slabaugh
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
8 code implementations • 12 Apr 2018 • Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, Zhenhua Dong
In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data.
no code implementations • 22 Feb 2018 • Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He
Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning.
no code implementations • 10 Feb 2018 • Fengwei Zhou, Bin Wu, Zhenguo Li
Few-shot learning remains challenging for meta-learning that learns a learning algorithm (meta-learner) from many related tasks.
1 code implementation • 13 Aug 2017 • Chenzi Zhang, Fan Wei, Qin Liu, Zhihao Gavin Tang, Zhenguo Li
We provide a worst-case upper bound of replication factor for our heuristic on general graphs.
9 code implementations • 31 Jul 2017 • Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li
In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.
19 code implementations • 13 Mar 2017 • Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.
Ranked #1 on
Click-Through Rate Prediction
on Company*
no code implementations • CVPR 2015 • Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang
Graph-based computer vision applications rely critically on similarity metrics which compute the pairwise similarity between any pair of vertices on graphs.
no code implementations • CVPR 2014 • Go Irie, Zhenguo Li, Xiao-Ming Wu, Shih-Fu Chang
Previous efforts in hashing intend to preserve data variance or pairwise affinity, but neither is adequate in capturing the manifold structures hidden in most visual data.
no code implementations • NeurIPS 2013 • Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang
We show that either explicitly or implicitly, various well-known graph-based models exhibit a common significant \emph{harmonic} structure in its target function -- the value of a vertex is approximately the weighted average of the values of its adjacent neighbors.
no code implementations • CVPR 2013 • Go Irie, Dong Liu, Zhenguo Li, Shih-Fu Chang
nary learning methods rely on image descriptors alone or together with class labels.
no code implementations • NeurIPS 2012 • Xiao-Ming Wu, Zhenguo Li, Anthony M. So, John Wright, Shih-Fu Chang
We prove that under proper absorption rates, a random walk starting from a set $\mathcal{S}$ of low conductance will be mostly absorbed in $\mathcal{S}$.
no code implementations • NeurIPS 2009 • Xiao-Ming Wu, Anthony M. So, Zhenguo Li, Shuo-Yen R. Li
In this paper, we show that a large class of kernel learning problems can be reformulated as semidefinite-quadratic-linear programs (SQLPs), which only contain a simple positive semidefinite constraint, a second-order cone constraint and a number of linear constraints.