no code implementations • ECCV 2020 • Yifan Yang, Guorong Li, Zhe Wu, Li Su, Qingming Huang, Nicu Sebe
We propose a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers.
no code implementations • 9 Jun 2022 • Elia Peruzzo, Enver Sangineto, Yahui Liu, Marco De Nadai, Wei Bi, Bruno Lepri, Nicu Sebe
In this paper, we explicitly encourage the emergence of this spatial clustering as a form of training regularization, this way including a self-supervised pretext task into the standard supervised learning.
1 code implementation • 26 May 2022 • Yue Song, Nicu Sebe, Wei Wang
Inspired by this observation, we propose a network branch dedicated to magnifying the importance of small eigenvalues.
Ranked #2 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Visual Categorization
+1
1 code implementation • 25 May 2022 • Yahui Liu, Bin Ren, Yue Song, Wei Bi, Nicu Sebe, Wei Wang
However, simply removing the PEs may not only harm the convergence and accuracy of ViTs but also places the model at more severe privacy risk.
no code implementations • 18 May 2022 • Florinel-Alin Croitoru, Nicolae-Catalin Ristea, Radu Tudor Ionescu, Nicu Sebe
In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a different learning rate for each layer of a neural network to create a data-free curriculum during the initial training epochs.
Ranked #1 on
Speech Emotion Recognition
on CREMA-D
1 code implementation • IEEE Transactions on Image Processing (TIP) 2022 • Jinliang Lin, Zhedong Zheng, Zhun Zhong, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe
Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.
Ranked #2 on
Image-Based Localization
on cvusa
1 code implementation • 7 Apr 2022 • Aleksandr Ermolov, Enver Sangineto, Nicu Sebe
Inspired by human memory, we propose to represent history with only important changes in the environment and, in our approach, to obtain automatically this representation using self-supervision.
no code implementations • 6 Apr 2022 • Yuyang Zhao, Zhun Zhong, Na Zhao, Nicu Sebe, Gim Hee Lee
Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning.
1 code implementation • 22 Mar 2022 • Songsong Wu, Hao Tang, Xiao-Yuan Jing, Haifeng Zhao, Jianjun Qian, Nicu Sebe, Yan Yan
In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap between the two image domains with different view-points.
1 code implementation • CVPR 2022 • Aleksandr Ermolov, Leyla Mirvakhabova, Valentin Khrulkov, Nicu Sebe, Ivan Oseledets
Following this line of work, we propose a new hyperbolic-based model for metric learning.
Ranked #1 on
Metric Learning
on CUB-200-2011
no code implementations • 5 Mar 2022 • Fengxiang Yang, Zhun Zhong, Zhiming Luo, Shaozi Li, Nicu Sebe
During local training, the DFS are used to synthesize novel domain statistics with the proposed domain hallucinating, which is achieved by re-weighting DFS with random weights.
no code implementations • 3 Mar 2022 • Yongguo Ling, Zhun Zhong, Donglin Cao, Zhiming Luo, Yaojin Lin, Shaozi Li, Nicu Sebe
In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment.
1 code implementation • 28 Feb 2022 • Hao Tang, Ling Shao, Philip H. S. Torr, Nicu Sebe
To learn more discriminative class-specific feature representations for the local generation, we also propose a novel classification module.
no code implementations • 22 Feb 2022 • Yuyu Guo, Lianli Gao, Jingkuan Song, Peng Wang, Nicu Sebe, Heng Tao Shen, Xuelong Li
Inspired by this observation, in this article, we propose a relation regularized network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG.
no code implementations • 8 Feb 2022 • Yue Song, Hao Tang, Nicu Sebe, Wei Wang
Specifically, the detail modeling focuses on capturing the object edges by supervision of explicitly decomposed detail label that consists of the pixels that are nested on the edge and near the edge.
1 code implementation • 29 Jan 2022 • Yue Song, Nicu Sebe, Wei Wang
Computing the matrix square root and its inverse in a differentiable manner is important in a variety of computer vision tasks.
1 code implementation • ICLR 2022 • Yue Song, Nicu Sebe, Wei Wang
Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution.
1 code implementation • 14 Dec 2021 • Haoyu Chen, Hao Tang, Zitong Yu, Nicu Sebe, Guoying Zhao
Specifically, we propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes.
no code implementations • CVPR 2022 • Yuyang Zhao, Zhun Zhong, Nicu Sebe, Gim Hee Lee
We introduce a new setting of Novel Class Discovery in Semantic Segmentation (NCDSS), which aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes.
no code implementations • 2 Dec 2021 • Jichao Zhang, Enver Sangineto, Hao Tang, Aliaksandr Siarohin, Zhun Zhong, Nicu Sebe, Wei Wang
However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications.
1 code implementation • CVPR 2022 • Zipeng Xu, Tianwei Lin, Hao Tang, Fu Li, Dongliang He, Nicu Sebe, Radu Timofte, Luc van Gool, Errui Ding
We propose a novel framework, i. e., Predict, Prevent, and Evaluate (PPE), for disentangled text-driven image manipulation that requires little manual annotation while being applicable to a wide variety of manipulations.
1 code implementation • 19 Nov 2021 • Guanglei Yang, Zhun Zhong, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci
Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting.
1 code implementation • 19 Nov 2021 • Guanglei Yang, Hao Tang, Humphrey Shi, Mingli Ding, Nicu Sebe, Radu Timofte, Luc van Gool, Elisa Ricci
The global alignment network aims to transfer the input image from the source domain to the target domain.
1 code implementation • 20 Oct 2021 • Haoyu Chen, Hao Tang, Nicu Sebe, Guoying Zhao
Instead, we introduce AniFormer, a novel Transformer-based architecture, that generates animated 3D sequences by directly taking the raw driving sequences and arbitrary same-type target meshes as inputs.
1 code implementation • 19 Oct 2021 • Bin Ren, Hao Tang, Nicu Sebe
To ease this problem, we propose a novel two-stage framework with a new Cascaded Cross MLP-Mixer (CrossMLP) sub-network in the first stage and one refined pixel-level loss in the second stage.
no code implementations • 29 Sep 2021 • Zhun Zhong, Yuyang Zhao, Gim Hee Lee, Nicu Sebe
Experiments on two synthetic-to-real semantic segmentation benchmarks demonstrate that AdvStyle can significantly improve the model performance on unseen real domains and show that we can achieve the state of the art.
1 code implementation • 26 Sep 2021 • Yahui Liu, Yajing Chen, Linchao Bao, Nicu Sebe, Bruno Lepri, Marco De Nadai
The ISF manipulates the semantics of an input latent code to make the image generated from it lying in the desired visual domain.
1 code implementation • 29 Aug 2021 • Hao Tang, Nicu Sebe
In this paper, we address the task of layout-to-image translation, which aims to translate an input semantic layout to a realistic image.
1 code implementation • ICCV 2021 • Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, Guoying Zhao
With the strength of deep generative models, 3D pose transfer regains intensive research interests in recent years.
4 code implementations • 3 Aug 2021 • Victor G. Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci
This paper presents solo-learn, a library of self-supervised methods for visual representation learning.
1 code implementation • 21 Jun 2021 • Hao Tang, Nicu Sebe
Both generators are mutually connected and trained in an end-to-end fashion and explicitly form three cycled subnets, i. e., one image generation cycle and two guidance generation cycles.
1 code implementation • CVPR 2021 • Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes.
no code implementations • CVPR 2021 • Yahui Liu, Enver Sangineto, Yajing Chen, Linchao Bao, Haoxian Zhang, Nicu Sebe, Bruno Lepri, Wei Wang, Marco De Nadai
In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation.
1 code implementation • NeurIPS 2021 • Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, Marco De Nadai
This task encourages the VTs to learn spatial relations within an image and makes the VT training much more robust when training data are scarce.
no code implementations • 7 Jun 2021 • Yuyang Zhao, Zhun Zhong, Zhiming Luo, Gim Hee Lee, Nicu Sebe
Second, CPSS can reduce the influence of noisy pseudo-labels and also avoid the model overfitting to the target domain during self-supervised learning, consistently boosting the performance on the target and open domains.
1 code implementation • 31 May 2021 • Jichao Zhang, Aliaksandr Siarohin, Hao Tang, Jingjing Chen, Enver Sangineto, Wei Wang, Nicu Sebe
Controllable person image generation aims to produce realistic human images with desirable attributes (e. g., the given pose, cloth textures or hair style).
1 code implementation • 28 May 2021 • Guanglei Yang, Hao Tang, Zhun Zhong, Mingli Ding, Ling Shao, Nicu Sebe, Elisa Ricci
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.
1 code implementation • ICCV 2021 • Yue Song, Nicu Sebe, Wei Wang
Singular Value Decomposition (SVD) is used in GCP to compute the matrix square root.
1 code implementation • 12 Apr 2021 • Bin Ren, Hao Tang, Fanyang Meng, Runwei Ding, Ling Shao, Philip H. S. Torr, Nicu Sebe
2D image-based virtual try-on has attracted increased attention from the multimedia and computer vision communities.
1 code implementation • CVPR 2021 • Subhankar Roy, Evgeny Krivosheev, Zhun Zhong, Nicu Sebe, Elisa Ricci
In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains.
Ranked #1 on
Multi-target Domain Adaptation
on Office-Home
1 code implementation • ICCV 2021 • Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci
While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation.
Ranked #4 on
Depth Estimation
on NYU-Depth V2
1 code implementation • CVPR 2021 • Fengxiang Yang, Zhun Zhong, Zhiming Luo, Yuanzheng Cai, Yaojin Lin, Shaozi Li, Nicu Sebe
This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data.
1 code implementation • IJCV 2021 • Shangzhi Teng, Shiliang Zhang, Qingming Huang, Nicu Sebe
Moreover, our method also achieves competitive performance compared with recent works on existing vehicle ReID datasets including VehicleID, VeRi-776 and VERI-Wild.
no code implementations • 25 Jan 2021 • Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe
Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs.
no code implementations • 8 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.
no code implementations • ICCV 2021 • Xinyan Liu, Guorong Li, Zhenjun Han, Weigang Zhang, Yifan Yang, Qingming Huang, Nicu Sebe
Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network.
no code implementations • 1 Jan 2021 • Hao Tang, Nicu Sebe
We propose the Semantically-Adaptive UpSampling (SA-UpSample), a general and highly effective upsampling method for the layout-to-image translation task.
no code implementations • 26 Dec 2020 • Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida
In this paper, we investigate the usage of CNNs that are designed to work directly with the DCT coefficients available in JPEG compressed images, proposing a handcrafted and data-driven techniques for reducing the computational complexity and the number of parameters for these models in order to keep their computational cost similar to their RGB baselines.
1 code implementation • CVPR 2021 • Yuyang Zhao, Zhun Zhong, Fengxiang Yang, Zhiming Luo, Yaojin Lin, Shaozi Li, Nicu Sebe
In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains.
1 code implementation • 7 Nov 2020 • Jean Pablo Vieira de Mello, Lucas Tabelini, Rodrigo F. Berriel, Thiago M. Paixão, Alberto F. de Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights.
1 code implementation • 16 Oct 2020 • Cristiano Saltori, Stéphane Lathuiliére, Nicu Sebe, Elisa Ricci, Fabio Galasso
In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but is also related to the geometry of the point clouds (e. g., point density variations).
1 code implementation • NeurIPS 2020 • Aleksandr Ermolov, Nicu Sebe
In this work we consider partially observable environments with sparse rewards.
1 code implementation • 29 Aug 2020 • Hao Tang, Song Bai, Nicu Sebe
We also propose two novel modules, i. e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively.
1 code implementation • 11 Aug 2020 • Raul Gomez, Yahui Liu, Marco De Nadai, Dimosthenis Karatzas, Bruno Lepri, Nicu Sebe
In this paper we propose the use of an image retrieval system to assist the image-to-image translation task.
1 code implementation • 10 Aug 2020 • Yahui Liu, Marco De Nadai, Deng Cai, Huayang Li, Xavier Alameda-Pineda, Nicu Sebe, Bruno Lepri
Our proposed model disentangles the image content from the visual attributes, and it learns to modify the latter using the textual description, before generating a new image from the content and the modified attribute representation.
1 code implementation • 10 Aug 2020 • Hao Tang, Song Bai, Philip H. S. Torr, Nicu Sebe
We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task.
Ranked #1 on
Pose Transfer
on Market-1501
(PCKh metric)
1 code implementation • 9 Aug 2020 • Jichao Zhang, Jingjing Chen, Hao Tang, Wei Wang, Yan Yan, Enver Sangineto, Nicu Sebe
In this paper we address the problem of unsupervised gaze correction in the wild, presenting a solution that works without the need for precise annotations of the gaze angle and the head pose.
no code implementations • 30 Jul 2020 • Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão, Alberto F. de Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available.
2 code implementations • ECCV 2020 • Hao Tang, Song Bai, Li Zhang, Philip H. S. Torr, Nicu Sebe
We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i. e., translating the pose of a given person to a desired one.
Ranked #1 on
Pose Transfer
on Market-1501
(IS metric)
7 code implementations • 13 Jul 2020 • Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe
Most of the current self-supervised representation learning (SSL) methods are based on the contrastive loss and the instance-discrimination task, where augmented versions of the same image instance ("positives") are contrasted with instances extracted from other images ("negatives").
1 code implementation • 21 May 2020 • Hao Tang, Hong Liu, Wei Xiao, Nicu Sebe
Then the activated dictionary atoms are assembled and passed to the compound dictionary learning and coding layers.
no code implementations • 9 May 2020 • Weiyao Lin, Huabin Liu, Shizhan Liu, Yuxi Li, Rui Qian, Tao Wang, Ning Xu, Hongkai Xiong, Guo-Jun Qi, Nicu Sebe
We demonstrate that the proposed method is able to boost the performance of existing pose estimation pipelines on our HiEve dataset.
no code implementations • 19 Apr 2020 • Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe, Elisa Ricci
In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.
no code implementations • 13 Apr 2020 • Shreya Ghosh, Abhinav Dhall, Garima Sharma, Sarthak Gupta, Nicu Sebe
In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed.
no code implementations • CVPR 2021 • Zhun Zhong, Linchao Zhu, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes.
2 code implementations • 7 Apr 2020 • Aliaksandr Siarohin, Subhankar Roy, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe
To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation.
1 code implementation • 7 Apr 2020 • Jingjing Chen, Jichao Zhang, Enver Sangineto, Jiayuan Fan, Tao Chen, Nicu Sebe
In this paper, we propose to alleviate these problems by means of a novel gaze redirection framework which exploits both a numerical and a pictorial direction guidance, jointly with a coarse-to-fine learning strategy.
1 code implementation • 31 Mar 2020 • Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, Nicu Sebe
The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices.
1 code implementation • 31 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.
1 code implementation • 15 Mar 2020 • Yahui Liu, Marco De Nadai, Jian Yao, Nicu Sebe, Bruno Lepri, Xavier Alameda-Pineda
Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images.
2 code implementations • NeurIPS 2019 • Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe
To achieve this, we decouple appearance and motion information using a self-supervised formulation.
Ranked #1 on
Video Reconstruction
on Tai-Chi-HD
1 code implementation • 3 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.
1 code implementation • 2 Feb 2020 • Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Nicolae-Catalin Ristea, Nicu Sebe
We show that a standard neuron followed by the novel apical dendrite activation (ADA) can learn the XOR logical function with 100\% accuracy.
Ranked #5 on
Speech Emotion Recognition
on CREMA-D
no code implementations • 1 Jan 2020 • Jurandy Almeida, Cristiano Saltori, Paolo Rota, Nicu Sebe
Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference capabilities.
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.
1 code implementation • 14 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.
1 code implementation • 12 Dec 2019 • Hao Tang, Hong Liu, Nicu Sebe
The proposed model consists of a single generator and a discriminator taking a conditional image and the target controllable structure as input.
Ranked #1 on
Cross-View Image-to-Image Translation
on cvusa
Facial Expression Translation
Gesture-to-Gesture Translation
+2
2 code implementations • 27 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.
Ranked #1 on
Facial Expression Translation
on CelebA
no code implementations • 15 Nov 2019 • Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe
To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e. g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN.
no code implementations • 25 Sep 2019 • Aleksandr Ermolov, Enver Sangineto, Nicu Sebe
To address this problem, a possible solution is to provide the agent with information about past observations.
1 code implementation • 17 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.
no code implementations • 15 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
1 code implementation • 2 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.
1 code implementation • 23 Jul 2019 • Lucas Tabelini Torres, Thiago M. Paixão, Rodrigo F. Berriel, Alberto F. de Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
Deep learning has been successfully applied to several problems related to autonomous driving.
1 code implementation • 19 Jul 2019 • Vinicius F. Arruda, Thiago M. Paixão, Rodrigo F. Berriel, Alberto F. De Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented.
1 code implementation • 12 Jul 2019 • Yahui Liu, Marco De Nadai, Gloria Zen, Nicu Sebe, Bruno Lepri
In this work, we propose a novel GAN architecture that decouples the required annotations into a category label - that specifies the gesture type - and a simple-to-draw category-independent conditional map - that expresses the location, rotation and size of the hand gesture.
no code implementations • CVPR 2019 • Zhen-Yu Zhang, Zhen Cui, Chunyan Xu, Yan Yan, Nicu Sebe, Jian Yang
In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly predict depth, surface normal and semantic segmentation.
Ranked #18 on
Monocular Depth Estimation
on NYU-Depth V2
1 code implementation • arXiv 2019 • Jichao Zhang, Meng Sun, Jingjing Chen, Hao Tang, Yan Yan, Xueying Qin, Nicu Sebe
Gaze correction aims to redirect the person's gaze into the camera by manipulating the eye region, and it can be considered as a specific image resynthesis problem.
no code implementations • 26 May 2019 • Romain Belmonte, Benjamin Allaert, Pierre Tirilly, Ioan Marius Bilasco, Chaabane Djeraba, Nicu Sebe
Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of these approaches on subsequent related tasks?
no code implementations • ICCV 2019 • Rodrigo Berriel, Stéphane Lathuilière, Moin Nabi, Tassilo Klein, Thiago Oliveira-Santos, Nicu Sebe, Elisa Ricci
To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network.
no code implementations • 15 May 2019 • Cristiano Saltori, Subhankar Roy, Nicu Sebe, Giovanni Iacca
Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i. e., network architectures) and is therefore memory expensive.
no code implementations • 14 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.
no code implementations • 7 May 2019 • Stéphane Lathuilière, Enver Sangineto, Aliaksandr Siarohin, Nicu Sebe
We present a generalization of the person-image generation task, in which a human image is generated conditioned on a target pose and a set X of source appearance images.
no code implementations • ICLR 2019 • Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
1 code implementation • 30 Apr 2019 • Aliaksandr Siarohin, Stéphane Lathuilière, Enver Sangineto, Nicu Sebe
Specifically, given an image xa of a person and a target pose P(xb), extracted from a different image xb, we synthesize a new image of that person in pose P(xb), while preserving the visual details in xa.
no code implementations • 17 Apr 2019 • Zhen-Yu Zhang, Stéphane Lathuilière, Andrea Pilzer, Nicu Sebe, Elisa Ricci, Jian Yang
Our proposal is evaluated on the wellestablished KITTI dataset, where we show that our online method is competitive withstate of the art algorithms trained in a batch setting.
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
1 code implementation • 2 Apr 2019 • Subhankar Roy, Enver Sangineto, Begüm Demir, Nicu Sebe
Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed.
8 code implementations • 28 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.
Ranked #1 on
Facial Expression Translation
on Bu3dfe
no code implementations • CVPR 2019 • Andrea Pilzer, Stéphane Lathuilière, Nicu Sebe, Elisa Ricci
Therefore, recent works have proposed deep architectures for addressing the monocular depth prediction task as a reconstruction problem, thus avoiding the need of collecting ground-truth depth.
1 code implementation • CVPR 2019 • Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Samuel Rota Bulo, Nicu Sebe, Elisa Ricci
A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift.
1 code implementation • 28 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.
1 code implementation • 15 Jan 2019 • Hao Tang, Hong Liu, Wei Xiao, Nicu Sebe
Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface.
Ranked #1 on
Hand Gesture Recognition
on Cambridge
1 code implementation • 14 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.
no code implementations • 4 Jan 2019 • Frederik Pahde, Mihai Puscas, Jannik Wolff, Tassilo Klein, Nicu Sebe, Moin Nabi
Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits.
no code implementations • 31 Dec 2018 • Shreya Ghosh, Abhinav Dhall, Nicu Sebe, Tom Gedeon
We study the factors that influence the perception of group-level cohesion and propose methods for estimating the human-perceived cohesion on the group cohesiveness scale.
1 code implementation • CVPR 2019 • Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe
This is achieved through a deep architecture that decouples appearance and motion information.
1 code implementation • 3 Dec 2018 • Aliaksandr Siarohin, Gloria Zen, Nicu Sebe, Elisa Ricci
Our approach takes as input a natural image and exploits recent models for deep style transfer and generative adversarial networks to change its style in order to modify a specific high-level attribute.
1 code implementation • 11 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).
1 code implementation • 14 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.
Ranked #1 on
Gesture-to-Gesture Translation
on NTU Hand Digit
2 code implementations • 28 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.
1 code implementation • ICLR 2019 • Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
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.
no code implementations • CVPR 2018 • Dan Xu, Wanli Ouyang, Xiaogang Wang, Nicu Sebe
Depth estimation and scene parsing are two particularly important tasks in visual scene understanding.
Ranked #10 on
Depth Estimation
on NYU-Depth V2
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.
no code implementations • 5 Mar 2018 • Dan Xu, Xavier Alameda-Pineda, Jingkuan Song, Elisa Ricci, Nicu Sebe
In this paper we address the problem of learning robust cross-domain representations for sketch-based image retrieval (SBIR).
1 code implementation • 1 Mar 2018 • Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, Nicu Sebe
Depth cues have been proved very useful in various computer vision and robotic tasks.
no code implementations • CVPR 2018 • Wei Wang, Xavier Alameda-Pineda, Dan Xu, Pascal Fua, Elisa Ricci, Nicu Sebe
Finally, these landmark sequences are translated into face videos.
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.
1 code implementation • CVPR 2018 • Aliaksandr Siarohin, Enver Sangineto, Stephane Lathuiliere, Nicu Sebe
Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose.
Ranked #5 on
Gesture-to-Gesture Translation
on NTU Hand Digit
Gesture-to-Gesture Translation
Image-to-Image Translation
+1
no code implementations • 10 Nov 2017 • Evgeny Stepanov, Stephane Lathuiliere, Shammur Absar Chowdhury, Arindam Ghosh, Radu-Laurentiu Vieriu, Nicu Sebe, Giuseppe Riccardi
In this AVEC challenge we explore different modalities (speech, language and visual features extracted from face) to design and develop automatic methods for the detection of depression.
no code implementations • 31 Aug 2017 • Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto, Lucio Marcenaro, Carlo Regazzoni, Nicu Sebe
In this paper we address the abnormality detection problem in crowded scenes.
Ranked #3 on
Abnormal Event Detection In Video
on UCSD Ped2
no code implementations • 3 Aug 2017 • Cristina Segalin, Fabio Celli, Luca Polonio, Michal Kosinski, David Stillwell, Nicu Sebe, Marco Cristani, Bruno Lepri
We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals.
no code implementations • CVPR 2017 • Ionut Cosmin Duta, Bogdan Ionescu, Kiyoharu Aizawa, Nicu Sebe
The proposed method addresses an important problem of video understanding: how to build a video representation that incorporates the CNN features over the entire video.
no code implementations • 23 Jun 2017 • Mahdyar Ravanbakhsh, Enver Sangineto, Moin Nabi, Nicu Sebe
Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios.
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.
2 code implementations • CVPR 2017 • Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, Nicu Sebe
This paper addresses the problem of depth estimation from a single still image.
Ranked #9 on
Depth Estimation
on NYU-Depth V2
1 code implementation • 6 Apr 2017 • Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, Xavier Alameda-Pineda, Elisa Ricci, Nicu Sebe
In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match.
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.
no code implementations • 2 Oct 2016 • Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi, Enver Sangineto, Nicu Sebe
In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality.
1 code implementation • 1 Aug 2016 • Marco De Nadai, Radu L. Vieriu, Gloria Zen, Stefan Dragicevic, Nikhil Naik, Michele Caraviello, Cesar A. Hidalgo, Nicu Sebe, Bruno Lepri
But in a world where the preference for safe looking neighborhoods is small, the connection between the perception of safety and liveliness will be either weak or nonexistent.
Computers and Society Social and Information Networks Physics and Society
no code implementations • 26 Jul 2016 • Hamidreza Rabiee, Javad Haddadnia, Hossein Mousavi, Moin Nabi, Vittorio Murino, Nicu Sebe
We aim at publishing the dataset with the article, to be used as a benchmark for the communities.
1 code implementation • CVPR 2016 • Xavier Alameda-Pineda, Elisa Ricci, Yan Yan, Nicu Sebe
A very popular approach for transductive multi-label recognition under linear classification settings is matrix completion.
no code implementations • CVPR 2016 • Sergey Tulyakov, Xavier Alameda-Pineda, Elisa Ricci, Lijun Yin, Jeffrey F. Cohn, Nicu Sebe
Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR).
no code implementations • CVPR 2016 • Wei Wang, Zhen Cui, Yan Yan, Jiashi Feng, Shuicheng Yan, Xiangbo Shu, Nicu Sebe
Modeling the aging process of human face is important for cross-age face verification and recognition.
no code implementations • 1 Jun 2016 • Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, Heng Tao Shen
In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations.
1 code implementation • 24 May 2016 • Enver Sangineto, Moin Nabi, Dubravko Culibrk, Nicu Sebe
The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training.
Ranked #26 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
1 code implementation • 13 Mar 2016 • Marco De Nadai, Jacopo Staiano, Roberto Larcher, Nicu Sebe, Daniele Quercia, Bruno Lepri
This is mainly because it is hard to collect data about "city life".
Computers and Society Social and Information Networks Physics and Society
no code implementations • ICCV 2015 • Guoyu Lu, Yan Yan, Li Ren, Jingkuan Song, Nicu Sebe, Chandra Kambhamettu
The main contribution of our paper is that we use a 3D model reconstructed by a short video as the query to realize 3D-to-3D localization under a multi-task point retrieval framework.
1 code implementation • ICCV 2015 • Mihai Marian Puscas, Enver Sangineto, Dubravko Culibrk, Nicu Sebe
The combination of appearance-based static ''objectness'' (Selective Search), motion information (Dense Trajectories) and transductive learning (detectors are forced to "overfit" on the unsupervised data used for training) makes the proposed approach extremely robust.
no code implementations • ICCV 2015 • Sergey Tulyakov, Nicu Sebe
To support the ability of our method to reliably reconstruct 3D shapes, we introduce a simple method for head pose estimation using a single image that reaches higher accuracy than the state of the art.
no code implementations • 6 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.
no code implementations • 23 Jun 2015 • Xavier Alameda-Pineda, Jacopo Staiano, Ramanathan Subramanian, Ligia Batrinca, Elisa Ricci, Bruno Lepri, Oswald Lanz, Nicu Sebe
Studying free-standing conversational groups (FCGs) in unstructured social settings (e. g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels.
no code implementations • CVPR 2015 • Davide Conigliaro, Paolo Rota, Francesco Setti, Chiara Bassetti, Nicola Conci, Nicu Sebe, Marco Cristani
In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc.
no code implementations • CVPR 2015 • Lianli Gao, Jingkuan Song, Feiping Nie, Yan Yan, Nicu Sebe, Heng Tao Shen
In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available.
no code implementations • CVPR 2014 • Victoria Yanulevskaya, Jasper Uijlings, Nicu Sebe
It has been shown that such object regions can be used to focus computer vision techniques on the parts of an image that matter most leading to significant improvements in both object localisation and semantic segmentation in recent years.
no code implementations • CVPR 2013 • Zhigang Ma, Yi Yang, Zhongwen Xu, Shuicheng Yan, Nicu Sebe, Alexander G. Hauptmann
Compared to complex event videos, these external videos contain simple contents such as objects, scenes and actions which are the basic elements of complex events.