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Long Quan

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5 papers

2 author rows

ICLR Conference 2024 Conference Paper

JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling

Jingyang Zhang
Shiwei Li 0001
Yuanxun Lu
Tian Fang
David McKinnon
Yanghai Tsin
Long Quan
Yao Yao 0008

We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. The RGB branch is locked during network fine-tuning, which enables efficient learning of the new modality distribution while maintaining the strong generalization ability of the large-scale pre-trained diffusion model. We demonstrate the effectiveness of JointNet by using the RGB-D diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGB-D generation, dense depth prediction, depth-conditioned image generation, and high-resolution 3D panorama generation.

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ICRA Conference 2019 Conference Paper

Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation

Tianwei Shen
Zixin Luo
Lei Zhou 0011
Hanyu Deng
Runze Zhang
Tian Fang
Long Quan

Accurate relative pose is one of the key components in visual odometry (VO) and simultaneous localization and mapping (SLAM). Recently, the self-supervised learning framework that jointly optimizes the relative pose and target image depth has attracted the attention of the community. Previous works rely on the photometric error generated from depths and poses between adjacent frames, which contains large systematic error under realistic scenes due to reflective surfaces and occlusions. In this paper, we bridge the gap between geometric loss and photometric loss by introducing the matching loss constrained by epipolar geometry in a self-supervised framework. Evaluated on the KITTI dataset, our method outperforms the state-of-the-art unsupervised egomotion estimation methods by a large margin. The code and data are available at https://github.com/hlzz/DeepMatchVO.

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AAAI Conference 2019 Conference Paper

Learning Fully Dense Neural Networks for Image Semantic Segmentation

Mingmin Zhen
Jinglu Wang
Lei Zhou
Tian Fang
Long Quan

Semantic segmentation is pixel-wise classification which retains critical spatial information. The “feature map reuse” has been commonly adopted in CNN based approaches to take advantage of feature maps in the early layers for the later spatial reconstruction. Along this direction, we go a step further by proposing a fully dense neural network with an encoderdecoder structure that we abbreviate as FDNet. For each stage in the decoder module, feature maps of all the previous blocks are adaptively aggregated to feedforward as input. On the one hand, it reconstructs the spatial boundaries accurately. On the other hand, it learns more efficiently with the more efficient gradient backpropagation. In addition, we propose the boundary-aware loss function to focus more attention on the pixels near the boundary, which boosts the “hard examples” labeling. We have demonstrated the best performance of the FDNet on the two benchmark datasets: PASCAL VOC 2012, NYUDv2 over previous works when not considering training on other datasets.

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EAAI Journal 2017 Journal Article

A novel data clustering algorithm based on modified gravitational search algorithm

XiaoHong Han
Long Quan
XiaoYan Xiong
Matt Almeter
Jie Xiang
Yuan Lan

Data clustering is a popular analysis tool for data statistics in many fields such as pattern recognition, data mining, machine learning, image analysis, and bioinformatics. The aim of data clustering is to represent large datasets by a fewer number of prototypes or clusters, which brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. In this paper, a novel data clustering algorithm based on modified Gravitational Search Algorithm is proposed, which is called Bird Flock Gravitational Search Algorithm (BFGSA). The BFGSA introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main steps including initialization, identification of the nearest neighbors, and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The performance of the proposed algorithm is evaluated through 13 real benchmark datasets from the well-known UCI Machine Learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), K-means, and other four clustering algorithms from the literature. The simulation results indicate that the BFGSA can effectively be used for data clustering.

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EAAI Journal 2013 Journal Article

Facing the classification of binary problems with a hybrid system based on quantum-inspired binary gravitational search algorithm and K-NN method

XiaoHong Han
Long Quan
XiaoYan Xiong
Bing Wu

Since given classification data often contains redundant, useless or misleading features, feature selection is an important pre-processing step for solving classification problems. This problem is often solved by applying evolutionary algorithms to decrease the dimensional number of features involved. Removing irrelevant features in the feature space and identifying relevant features correctly is the primary objective, which can increase classification accuracy. In this paper, a novel QBGSA–K-NN hybrid system which hybridizes the quantum-inspired binary gravitational search algorithm (QBGSA) with the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) is proposed. The main aim of this system is to improve classification accuracy with an appropriate feature subset in binary problems. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples. The experimental results show that the proposed method is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems.

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