Search Results for author:
Found 147 papers, 86 papers with code
Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations
We propose a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers.
Spatial Entropy Regularization for Vision Transformers
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.
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
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
Breaking the Chain of Gradient Leakage in Vision Transformers
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.
LeRaC: Learning Rate Curriculum
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
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
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
Temporal Alignment for History Representation in Reinforcement Learning
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.
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning.
Cross-View Panorama Image Synthesis
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.
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
Following this line of work, we propose a new hyperbolic-based model for metric learning.
Ranked #1 on
Metric Learning
on CUB-200-2011
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating
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.
Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification
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.
Local and Global GANs with Semantic-Aware Upsampling for Image Generation
To learn more discriminative class-specific feature representations for the local generation, we also propose a novel classification module.
Relation Regularized Scene Graph Generation
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.
Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling
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.
Fast Differentiable Matrix Square Root and Inverse Square Root
Computing the matrix square root and its inverse in a differentiable manner is important in a variety of computer vision tasks.
Fast Differentiable Matrix Square Root
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.
Geometry-Contrastive Transformer for Generalized 3D Pose Transfer
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.
Novel Class Discovery in Semantic Segmentation
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.
3D-Aware Semantic-Guided Generative Model for Human Synthesis
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.
Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model
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.
Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation
Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting.
Global and Local Alignment Networks for Unpaired Image-to-Image Translation
The global alignment network aims to transfer the input image from the source domain to the target domain.
AniFormer: Data-driven 3D Animation with Transformer
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.
Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation
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.
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
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.
ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation
The ISF manipulates the semantics of an input latent code to make the image generated from it lying in the desired visual domain.
Layout-to-Image Translation with Double Pooling Generative Adversarial Networks
In this paper, we address the task of layout-to-image translation, which aims to translate an input semantic layout to a realistic image.
Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer
With the strength of deep generative models, 3D pose transfer regains intensive research interests in recent years.
Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
This paper presents solo-learn, a library of self-supervised methods for visual representation learning.
Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes
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.
Neighborhood Contrastive Learning for Novel Class Discovery
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.
Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation
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.
Efficient Training of Visual Transformers with Small Datasets
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.
Source-Free Open Compound Domain Adaptation in Semantic Segmentation
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.
Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization
Controllable person image generation aims to produce realistic human images with desirable attributes (e. g., the given pose, cloth textures or hair style).
Transformer-Based Source-Free Domain Adaptation
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.
Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?
Singular Value Decomposition (SVD) is used in GCP to compute the matrix square root.
Cloth Interactive Transformer for Virtual Try-On
2D image-based virtual try-on has attracted increased attention from the multimedia and computer vision communities.
Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation
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
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction
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
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification
This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data.
Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification
Moreover, our method also achieves competitive performance compared with recent works on existing vehicle ReID datasets including VehicleID, VeRi-776 and VERI-Wild.
Curriculum Learning: A Survey
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.
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
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.
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network
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.
Semantically-Adaptive Upsampling for Layout-to-Image Translation
We propose the Semantically-Adaptive UpSampling (SA-UpSample), a general and highly effective upsampling method for the layout-to-image translation task.
How Far Can We Get with Neural Networks Straight from JPEG?
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.
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification
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.
Deep traffic light detection by overlaying synthetic context on arbitrary natural images
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.
SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
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).
Latent World Models For Intrinsically Motivated Exploration
In this work we consider partially observable environments with sparse rewards.
Dual Attention GANs for Semantic Image Synthesis
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.
Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation
In this paper we propose the use of an image retrieval system to assist the image-to-image translation task.
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
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.
Bipartite Graph Reasoning GANs for Person Image Generation
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)
Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild
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.
Deep Traffic Sign Detection and Recognition Without Target Domain Real Images
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.
XingGAN for Person Image Generation
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)
Whitening for Self-Supervised Representation Learning
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").
When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition with Limited Data
Then the activated dictionary atoms are assembled and passed to the compound dictionary learning and coding layers.
Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
We demonstrate that the proposed method is able to boost the performance of existing pose estimation pipelines on our HiEve dataset.
TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.
Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset
In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed.
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes.
Motion-supervised Co-Part Segmentation
To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation.
Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance
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.
Binary Neural Networks: A Survey
The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices.
Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis
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.
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling
Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images.
First Order Motion Model for Image Animation
To achieve this, we decouple appearance and motion information using a self-supervised formulation.
Ranked #1 on
Video Reconstruction
on Tai-Chi-HD
Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation
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.
Non-linear Neurons with Human-like Apical Dendrite Activations
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
Low-Budget Label Query through Domain Alignment Enforcement
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.
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
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.
Asymmetric Generative Adversarial Networks for Image-to-Image Translation
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.
Unified Generative Adversarial Networks for Controllable Image-to-Image Translation
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
AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks
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
Curriculum Self-Paced Learning for Cross-Domain Object Detection
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.
Event Discovery for History Representation in Reinforcement Learning
To address this problem, a possible solution is to provide the agent with information about past observations.
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
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.
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation
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
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation.
Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
Deep learning has been successfully applied to several problems related to autonomous driving.
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
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.
Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps
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.
Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation
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
GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks
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.
Impact of facial landmark localization on facial expression recognition
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?
Budget-Aware Adapters for Multi-Domain Learning
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.
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i. e., network architectures) and is therefore memory expensive.
Expression Conditional GAN for Facial Expression-to-Expression Translation
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.
Attention-based Fusion for Multi-source Human Image Generation
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.
Whitening and Coloring transform for GANs
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
Appearance and Pose-Conditioned Human Image Generation using Deformable GANs
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.
Online Adaptation through Meta-Learning for Stereo Depth Estimation
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.
Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
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
Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images
Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed.
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
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
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation
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.
Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss
A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift.
Attribute-Guided Sketch Generation
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.
Fast and Robust Dynamic Hand Gesture Recognition via Key Frames Extraction and Feature Fusion
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
Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
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.
Low-Shot Learning from Imaginary 3D Model
Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits.
Predicting Group Cohesiveness in Images
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.
Animating Arbitrary Objects via Deep Motion Transfer
This is achieved through a deep architecture that decouples appearance and motion information.
Enhancing Perceptual Attributes with Bayesian Style Generation
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.
Deep Micro-Dictionary Learning and Coding Network
In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN).
GestureGAN for Hand Gesture-to-Gesture Translation in the Wild
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
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
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.
Whitening and Coloring batch transform for GANs
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
Group Consistent Similarity Learning via Deep CRF for Person Re-Identification
Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities.
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
Depth estimation and scene parsing are two particularly important tasks in visual scene understanding.
Ranked #10 on
Depth Estimation
on NYU-Depth V2
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks.
Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval
In this paper we address the problem of learning robust cross-domain representations for sketch-based image retrieval (SBIR).
Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks
Depth cues have been proved very useful in various computer vision and robotic tasks.
Every Smile is Unique: Landmark-Guided Diverse Smile Generation
Finally, these landmark sequences are translated into face videos.
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection.
Deformable GANs for Pose-based Human Image Generation
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
Depression Severity Estimation from Multiple Modalities
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.
Abnormal Event Detection in Videos using Generative Adversarial Nets
In this paper we address the abnormality detection problem in crowded scenes.
Ranked #3 on
Abnormal Event Detection In Video
on UCSD Ped2
What your Facebook Profile Picture Reveals about your Personality
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.
Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos
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.
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds
Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios.
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results.
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
This paper addresses the problem of depth estimation from a single still image.
Ranked #9 on
Depth Estimation
on NYU-Depth V2
How to Make an Image More Memorable? A Deep Style Transfer Approach
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.
Viraliency: Pooling Local Virality
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.
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality.
Are Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life
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
Emotion-Based Crowd Representation for Abnormality Detection
We aim at publishing the dataset with the article, to be used as a benchmark for the communities.
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix Completion
A very popular approach for transductive multi-label recognition under linear classification settings is matrix completion.
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions
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).
Recurrent Face Aging
Modeling the aging process of human face is important for cross-age face verification and recognition.
A Survey on Learning to Hash
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.
Self Paced Deep Learning for Weakly Supervised Object Detection
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
The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective
This is mainly because it is hard to collect data about "city life".
Computers and Society Social and Information Networks Physics and Society
Localize Me Anywhere, Anytime: A Multi-Task Point-Retrieval Approach
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.
Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories
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.
Regressing a 3D Face Shape From a Single Image
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.
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes.
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
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.
The S-Hock Dataset: Analyzing Crowds at the Stadium
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.
Optimal Graph Learning With Partial Tags and Multiple Features for Image and Video Annotation
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.
Learning to Group Objects
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.
Complex Event Detection via Multi-source Video Attributes
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.
