Search Results for author: Dan Xu

Found 61 papers, 40 papers with code

DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection

no code implementations20 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.

object-detection Open World Object Detection

Lipschitz Continuity Retained Binary Neural Network

1 code implementation13 Jul 2022 Yuzhang Shang, Dan Xu, Bin Duan, Ziliang Zong, Liqiang Nie, Yan Yan

Relying on the premise that the performance of a binary neural network can be largely restored with eliminated quantization error between full-precision weight vectors and their corresponding binary vectors, existing works of network binarization frequently adopt the idea of model robustness to reach the aforementioned objective.

Binarization Quantization

Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information

1 code implementation12 Jul 2022 Jiebao Zhang, Wenhua Qian, Rencan Nie, Jinde Cao, Dan Xu

Adversarial training is a simple and effective defense method to improve the robustness of CNNs to adversarial examples.

Adversarial Robustness

Network Binarization via Contrastive Learning

1 code implementation6 Jul 2022 Yuzhang Shang, Dan Xu, Ziliang Zong, Liqiang Nie, Yan Yan

Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit.

Binarization Contrastive Learning +2

Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation

1 code implementation26 Mar 2022 Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Moin Nabi, Xavier Alameda-Pineda, Elisa Ricci

This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years.

Contrastive Learning Incremental Learning +4

An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designs

no code implementations20 Mar 2022 Ren Kai Tan, Chao Qian, Dan Xu, Wenjing Ye

Most models are trained to work with the design problem similar to that used for data generation and require retraining if the design problem changes.

Cantilever Beam

Inverted Pyramid Multi-task Transformer for Dense Scene Understanding

1 code implementation15 Mar 2022 Hanrong Ye, Dan Xu

Multi-task dense scene understanding is a thriving research domain that requires simultaneous perception and reasoning on a series of correlated tasks with pixel-wise prediction.

Boundary Detection Human Parsing +6

Depth-Aware Generative Adversarial Network for Talking Head Video Generation

1 code implementation CVPR 2022 Fa-Ting Hong, Longhao Zhang, Li Shen, Dan Xu

In a more dense way, the depth is also utilized to learn 3D-aware cross-modal (i. e. appearance and depth) attention to guide the generation of motion fields for warping source image representations.

Talking Head Generation Video Generation

Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

1 code implementation CVPR 2022 Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Dan Xu

To this end, we propose a Multi-class Token Transformer, termed as MCTformer, which uses multiple class tokens to learn interactions between the class tokens and the patch tokens.

Object Localization Weakly-Supervised Semantic Segmentation

Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

1 code implementation1 Feb 2022 Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Hao Tang, Xavier Alameda-Pineda, Elisa Ricci

To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies.

Incremental Learning Semantic Segmentation

Generalized Binary Search Network for Highly-Efficient Multi-View Stereo

1 code implementation CVPR 2022 Zhenxing Mi, Di Chang, Dan Xu

The new formulation makes our method only sample a very small number of depth hypotheses in each step, which is highly memory efficient, and also greatly facilitates quick training convergence.

Depth Estimation Depth Prediction

Contrastive Mutual Information Maximization for Binary Neural Networks

no code implementations29 Sep 2021 Yuzhang Shang, Dan Xu, Ziliang Zong, Liqiang Nie, Yan Yan

Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit.

Binarization Contrastive Learning +2

Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting

no code implementations6 Aug 2021 Yongtuo Liu, Sucheng Ren, Liangyu Chai, Hanjie Wu, Jing Qin, Dan Xu, Shengfeng He

In this way, we can transfer the original spatial labeling redundancy caused by individual similarities to effective supervision signals on the unlabeled regions.

Crowd Counting

Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

no code implementations6 Aug 2021 Yongtuo Liu, Dan Xu, Sucheng Ren, Hanjie Wu, Hongmin Cai, Shengfeng He

We further leverage the derived segments to propose a crowd-aware fine-grained domain adaptation framework for crowd counting, which consists of two novel adaptation modules, i. e., Crowd Region Transfer (CRT) and Crowd Density Alignment (CDA).

Crowd Counting Domain Adaptation +1

Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization

1 code implementation27 Jul 2021 Fa-Ting Hong, Jia-Chang Feng, Dan Xu, Ying Shan, Wei-Shi Zheng

In this work, we argue that the features extracted from the pretrained extractor, e. g., I3D, are not the WS-TALtask-specific features, thus the feature re-calibration is needed for reducing the task-irrelevant information redundancy.

Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1

Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation

1 code implementation ICCV 2021 Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Ferdous Sohel, Dan Xu

Motivated by the significant inter-task correlation, we propose a novel weakly supervised multi-task framework termed as AuxSegNet, to leverage saliency detection and multi-label image classification as auxiliary tasks to improve the primary task of semantic segmentation using only image-level ground-truth labels.

Auxiliary Learning Multi-Label Image Classification +4

SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks

1 code implementation ICCV 2021 Jiapeng Tang, Jiabao Lei, Dan Xu, Feiying Ma, Kui Jia, Lei Zhang

To this end, we propose to learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks, to simultaneously achieve advanced scalability to large-scale scenes, generality to novel shapes, and applicability to raw scans in a unified framework.

Surface Reconstruction

Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes

no code implementations5 May 2021 Dan Xu, Andrea Vedaldi, Joao F. Henriques

We build on the idea of view synthesis, which uses classical camera geometry to re-render a source image from a different point-of-view, specified by a predicted relative pose and depth map.

Depth Estimation Depth Prediction +1

Delving into Localization Errors for Monocular 3D Object Detection

1 code implementation CVPR 2021 Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang

Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging.

Autonomous Driving Monocular 3D Object Detection +1

Variational Structured Attention Networks for Deep Visual Representation Learning

1 code implementation5 Mar 2021 Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda, Dan Xu, Mingli Ding, Elisa Ricci

Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to Variational STructured Attention networks (VISTA-Net).

Depth Estimation Representation Learning +1

NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting

1 code implementation10 Feb 2021 Kai Chen, Guang Chen, Dan Xu, Lijun Zhang, Yuyao Huang, Alois Knoll

Although Transformer has made breakthrough success in widespread domains especially in Natural Language Processing (NLP), applying it to time series forecasting is still a great challenge.

Time Series Forecasting

Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction

no code implementations8 Jan 2021 Dan Xu, Xavier Alameda-Pineda, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe

In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.

BSDS500 Graph Attention +2

Variational Structured Attention Networks for Dense Pixel-Wise Prediction

1 code implementation1 Jan 2021 Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda, Dan Xu, Mingli Ding, Elisa Ricci

State-of-the-art performances in dense pixel-wise prediction tasks are obtained with specifically designed convolutional networks.

Unsupervised Image Segmentation using Mutual Mean-Teaching

no code implementations16 Dec 2020 Zhichao Wu, Lei Guo, Hao Zhang, Dan Xu

Unsupervised image segmentation aims at assigning the pixels with similar feature into a same cluster without annotation, which is an important task in computer vision.

Image Segmentation Semantic Segmentation +1

LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection

no code implementations16 Nov 2020 Sanqing Qu, Guang Chen, Dan Xu, Jinhu Dong, Fan Lu, Alois Knoll

At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features.

Online Action Detection

Scope Head for Accurate Localization in Object Detection

no code implementations11 May 2020 Geng Zhan, Dan Xu, Guo Lu, Wei Wu, Chunhua Shen, Wanli Ouyang

Existing anchor-based and anchor-free object detectors in multi-stage or one-stage pipelines have achieved very promising detection performance.

object-detection Object Detection

Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis

1 code implementation31 Mar 2020 Hao Tang, Xiaojuan Qi, Dan Xu, Philip H. S. Torr, Nicu Sebe

To tackle the first challenge, we propose to use the edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module.

Image Generation

Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation

1 code implementation3 Feb 2020 Hao Tang, Dan Xu, Yan Yan, Jason J. Corso, Philip H. S. Torr, Nicu Sebe

In the first stage, the input image and the conditional semantic guidance are fed into a cycled semantic-guided generation network to produce initial coarse results.

Image-to-Image Translation Translation

Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

2 code implementations CVPR 2020 Hao Tang, Dan Xu, Yan Yan, Philip H. S. Torr, Nicu Sebe

To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details.

Image Generation Scene Generation

Asymmetric Generative Adversarial Networks for Image-to-Image Translation

1 code implementation14 Dec 2019 Hao Tang, Dan Xu, Hong Liu, Nicu Sebe

In this paper, we analyze the limitation of the existing symmetric GAN models in asymmetric translation tasks, and propose an AsymmetricGAN model with both translation and reconstruction generators of unequal sizes and different parameter-sharing strategy to adapt to the asymmetric need in both unsupervised and supervised image-to-image translation tasks.

Image-to-Image Translation Translation

AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks

2 code implementations27 Nov 2019 Hao Tang, Hong Liu, Dan Xu, Philip H. S. Torr, Nicu Sebe

State-of-the-art methods in image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data.

Image-to-Image Translation Translation

Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks

1 code implementation17 Sep 2019 Andrea Pilzer, Stéphane Lathuilière, Dan Xu, Mihai Marian Puscas, Elisa Ricci, Nicu Sebe

Extensive experiments on the publicly available datasets KITTI, Cityscapes and ApolloScape demonstrate the effectiveness of the proposed model which is competitive with other unsupervised deep learning methods for depth prediction.

Data Augmentation Depth Prediction +2

Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection

1 code implementation ICCV 2019 Yingyue Xu, Dan Xu, Xiaopeng Hong, Wanli Ouyang, Rongrong Ji, Min Xu, Guoying Zhao

We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions.

object-detection RGB Salient Object Detection +1

Dynamic Graph Message Passing Networks

1 code implementation CVPR 2020 Li Zhang, Dan Xu, Anurag Arnab, Philip H. S. Torr

We propose a dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph.

Image Classification object-detection +3

Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation

no code implementations15 Aug 2019 Mihai Marian Puscas, Dan Xu, Andrea Pilzer, Nicu Sebe

Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured Conditional Random Field (CRF) model.

Monocular Depth Estimation Unsupervised Monocular Depth Estimation

Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

1 code implementation2 Aug 2019 Hao Tang, Dan Xu, Gaowen Liu, Wei Wang, Nicu Sebe, Yan Yan

In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation.

Image Generation

Expression Conditional GAN for Facial Expression-to-Expression Translation

no code implementations14 May 2019 Hao Tang, Wei Wang, Songsong Wu, Xinya Chen, Dan Xu, Nicu Sebe, Yan Yan

In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute.

Facial Expression Translation Translation

Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

3 code implementations CVPR 2019 Hao Tang, Dan Xu, Nicu Sebe, Yanzhi Wang, Jason J. Corso, Yan Yan

In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map.

Bird View Synthesis Cross-View Image-to-Image Translation +1

Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

8 code implementations28 Mar 2019 Hao Tang, Dan Xu, Nicu Sebe, Yan Yan

To handle the limitation, in this paper we propose a novel Attention-Guided Generative Adversarial Network (AGGAN), which can detect the most discriminative semantic object and minimize changes of unwanted part for semantic manipulation problems without using extra data and models.

Translation Unsupervised Image-To-Image Translation

Attribute-Guided Sketch Generation

1 code implementation28 Jan 2019 Hao Tang, Xinya Chen, Wei Wang, Dan Xu, Jason J. Corso, Nicu Sebe, Yan Yan

To this end, we propose a novel Attribute-Guided Sketch Generative Adversarial Network (ASGAN) which is an end-to-end framework and contains two pairs of generators and discriminators, one of which is used to generate faces with attributes while the other one is employed for image-to-sketch translation.


Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

1 code implementation14 Jan 2019 Hao Tang, Dan Xu, Wei Wang, Yan Yan, Nicu Sebe

State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data.

Image-to-Image Translation Translation

Deep Micro-Dictionary Learning and Coding Network

1 code implementation11 Sep 2018 Hao Tang, Heng Wei, Wei Xiao, Wei Wang, Dan Xu, Yan Yan, Nicu Sebe

In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN).

Dictionary Learning

GestureGAN for Hand Gesture-to-Gesture Translation in the Wild

1 code implementation14 Aug 2018 Hao Tang, Wei Wang, Dan Xu, Yan Yan, Nicu Sebe

Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture.

Data Augmentation Gesture-to-Gesture Translation +1

Unsupervised Adversarial Depth Estimation using Cycled Generative Networks

2 code implementations28 Jul 2018 Andrea Pilzer, Dan Xu, Mihai Marian Puscas, Elisa Ricci, Nicu Sebe

The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other.

Monocular Depth Estimation

Group Consistent Similarity Learning via Deep CRF for Person Re-Identification

no code implementations CVPR 2018 Dapeng Chen, Dan Xu, Hongsheng Li, Nicu Sebe, Xiaogang Wang

Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities.

Person Re-Identification

Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation

1 code implementation CVPR 2018 Dan Xu, Wei Wang, Hao Tang, Hong Liu, Nicu Sebe, Elisa Ricci

Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks.

Monocular Depth Estimation

Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction

no code implementations NeurIPS 2017 Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang, Nicu Sebe

Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection.

BSDS500 Contour Detection

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

2 code implementations CVPR 2017 Dan Xu, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe

Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results.

Pedestrian Detection

Viraliency: Pooling Local Virality

1 code implementation CVPR 2017 Xavier Alameda-Pineda, Andrea Pilzer, Dan Xu, Nicu Sebe, Elisa Ricci

In our overly-connected world, the automatic recognition of virality - the quality of an image or video to be rapidly and widely spread in social networks - is of crucial importance, and has recently awaken the interest of the computer vision community.

Neural Information Retrieval: A Literature Review

no code implementations18 Nov 2016 Ye Zhang, Md Mustafizur Rahman, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek Khetan, Tyler McDonnell, An Thanh Nguyen, Dan Xu, Byron C. Wallace, Matthew Lease

A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing.

Information Retrieval speech-recognition +1

Searching Action Proposals via Spatial Actionness Estimation and Temporal Path Inference and Tracking

no code implementations23 Aug 2016 Nannan Li, Dan Xu, Zhenqiang Ying, Zhihao LI, Ge Li

In this paper, we address the problem of searching action proposals in unconstrained video clips.

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

no code implementations6 Oct 2015 Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, Nicu Sebe

We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes.

Anomaly Detection Denoising +1

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