Search Results for author:
Found 540 papers, 271 papers with code
Seeking the Strongest Rigid Detector
The current state of the art solutions for object detection describe each class by a set of models trained on discovered sub-classes (so called "components"), with each model itself composed of collections of interrelated parts (deformable models).
Bayesian Grammar Learning for Inverse Procedural Modeling
Given a set of labeled positive examples, we induce a grammar which can be sampled to create novel instances of the same building style.
Is There a Procedural Logic to Architecture?
Urban models are key to navigation, architecture and entertainment.
Fast Energy Minimization Using Learned State Filters
However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take.
Human Pose Estimation Using Body Parts Dependent Joint Regressors
The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts.
Sparse Quantization for Patch Description
We demonstrate the capabilities of our formulation for both keypoint matching and image classification.
Query Adaptive Similarity for Large Scale Object Retrieval
Furthermore, we propose a function to score the individual contributions into an image to image similarity within the probabilistic framework.
Random Binary Mappings for Kernel Learning and Efficient SVM
SVMs suffer from various drawbacks in terms of selecting the right kernel, which depends on the image descriptors, as well as computational and memory efficiency.
SEEDS: Superpixels Extracted via Energy-Driven Sampling
We define a robust and fast to evaluate energy function, based on enforcing color similarity between the bound- aries and the superpixel color histogram.
Creating Summaries from User Videos
Thereby we focus on user videos, which are raw videos containing a set of interesting events.
The Synthesizability of Texture Examples
This work is the first attempt to quantify this image property, and we find that texture synthesizability can be learned and predicted.
Latent Dictionary Learning for Sparse Representation based Classification
Each dictionary atom is jointly learned with a latent vector, which associates this atom to the representation of different classes.
Object Classification with Adaptable Regions
In classification of objects substantial work has gone into improving the low level representation of an image by considering various aspects such as different features, a number of feature pooling and coding techniques and considering different kernels.
Incremental Learning of NCM Forests for Large-Scale Image Classification
NCMFs not only outperform conventional random forests, but are also well suited for integrating new classes.
Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels
State-of-the-art Multi-View Stereo (MVS) algorithms deliver dense depth maps or complex meshes with very high detail, and redundancy over regular surfaces.
Ground Plane Estimation using a Hidden Markov Model
We focus on the problem of estimating the ground plane orientation and location in monocular video sequences from a moving observer.
Gesture Recognition Portfolios for Personalization
Human gestures, similar to speech and handwriting, are often unique to the individual.
Using a Deformation Field Model for Localizing Faces and Facial Points under Weak Supervision
Additionally, without any facial point annotation at the level of individual training images, our method can localize facial points with an accuracy similar to fully supervised approaches.
Comment on "Ensemble Projection for Semi-supervised Image Classification"
In a series of papers by Dai and colleagues [1, 2], a feature map (or kernel) was introduced for semi- and unsupervised learning.
From Categories to Subcategories: Large-Scale Image Classification With Partial Class Label Refinement
Compared to approaches that disregard the extra coarse labeled data, we achieve a relative improvement in subcategory classification accuracy of up to 22% in our large-scale image classification experiments.
Video Summarization by Learning Submodular Mixtures of Objectives
We present a novel method for summarizing raw, casually captured videos.
3D All The Way: Semantic Segmentation of Urban Scenes From Start to End in 3D
We propose a new approach for semantic segmentation of 3D city models.
Superpixel Meshes for Fast Edge-Preserving Surface Reconstruction
Multi-View-Stereo (MVS) methods aim for the highest detail possible, however, such detail is often not required.
Joint Vanishing Point Extraction and Tracking
Although the method is designed for unknown camera poses, it is also helpful in scenarios with known poses, since a multi-frame approach in VP detection helps to regularize in frames with weak VP line support.
Metric Imitation by Manifold Transfer for Efficient Vision Applications
In particular, MI consists of: 1) quantifying the properties of source metrics as manifold geometry, 2) transferring the manifold from source domain to target domain, and 3) learning a mapping of TFs so that the manifold is approximated as well as possible in the mapped feature domain.
Oracle MCG: A first peek into COCO Detection Challenges
The recently presented COCO detection challenge will most probably be the reference benchmark in object detection in the next years.
Is Image Super-resolution Helpful for Other Vision Tasks?
In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications.
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
We generate hypotheses in a sliding-window fashion over different activation layers and show that the final convolutional layers can find the object of interest with high recall but poor localization due to the coarseness of the feature maps.
Some like it hot - visual guidance for preference prediction
Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer inter-person preferences as well as a novel regression technique for handling visual queries without rating history.
Seven ways to improve example-based single image super resolution
In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning.
Ranked #45 on
Image Super-Resolution
on BSD100 - 4x upscaling
Boosting Object Proposals: From Pascal to COCO
Computer vision in general, and object proposals in particular, are nowadays strongly influenced by the databases on which researchers evaluate the performance of their algorithms.
A Gaussian Process Latent Variable Model for BRDF Inference
Although inferring higher dimensional BRDFs from such modest training is not a trivial problem, our method performs better than state-of-the-art parametric, semi-parametric and non-parametric approaches.
Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering
Hence, in the spirit of ensemble learning we create a set of such training sets which are all diverse, leading to diverse classifiers.
Fast Optical Flow using Dense Inverse Search
Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity.
Energy-Efficient ConvNets Through Approximate Computing
Recently ConvNets or convolutional neural networks (CNN) have come up as state-of-the-art classification and detection algorithms, achieving near-human performance in visual detection.
DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination
In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i. e. from a single 2D image of a sphere of one material under one illumination.
Direction matters: hand pose estimation from local surface normals
We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals.
Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection
We propose a novel method for Acoustic Event Detection (AED).
Sound Multimedia
Actionness Estimation Using Hybrid Fully Convolutional Networks
Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location.
Ranked #11 on
Action Detection
on J-HMDB
Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition
In this paper, we proposed a novel pipeline for image-level classification in the hyperspectral images.
Low-Cost Scene Modeling using a Density Function Improves Segmentation Performance
We propose a low cost and effective way to combine a free simulation software and free CAD models for modeling human-object interaction in order to improve human & object segmentation.
k2-means for fast and accurate large scale clustering
k^2-means builds upon the standard k-means (Lloyd's algorithm) and combines a new strategy to accelerate the convergence with a new low time complexity divisive initialization.
Dynamic Filter Networks
In a traditional convolutional layer, the learned filters stay fixed after training.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
The dataset, named DAVIS (Densely Annotated VIdeo Segmentation), consists of fifty high quality, Full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motion-blur and appearance changes.
Progressive Prioritized Multi-View Stereo
This work proposes a progressive patch based multi-view stereo algorithm able to deliver a dense point cloud at any time.
Scale-Aware Alignment of Hierarchical Image Segmentation
To demonstrate the power of our method, we perform comprehensive experiments, which show that our method, as a post-processing step, can significantly improve the quality of the hierarchical segmentation representations, and ease the usage of hierarchical image segmentation to high-level vision tasks such as object segmentation.
Fast Algorithms for Linear and Kernel SVM+
The SVM+ approach has shown excellent performance in visual recognition tasks for exploiting privileged information in the training data.
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
In this paper, a new method for generating object and action proposals in images and videos is proposed.
Generic 3D Convolutional Fusion for image restoration
Unlike other methods adapted to different tasks, our method uses the exact same convolutional network architecture to address both image denois- ing and single image super-resolution.
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016.
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
Ranked #3 on
Multimodal Activity Recognition
on EV-Action
Convolutional Oriented Boundaries
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs).
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels.
Ranked #3 on
Age Estimation
on ChaLearn 2015
DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
On the other hand, we let an iteration of feature learning and patch clustering purify the set of dedicated patches that we use.
A Riemannian Network for SPD Matrix Learning
Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds.
Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video
With this mapping, the problem of learning a cross-view metric between the two source heterogeneous spaces can be expressed as learning a single-view Euclidean distance metric in the target common Euclidean space.
Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition
Specifically, by exploiting the Riemannian geometry of the manifold of fixed-rank Positive Semidefinite (PSD) matrices, we present a new solution to reduce optimizing over the space of column full-rank transformation matrices to optimizing on the PSD manifold which has a well-established Riemannian structure.
Does V-NIR based Image Enhancement Come with Better Features?
Image enhancement using the visible (V) and near-infrared (NIR) usually enhances useful image details.
Failure Detection for Facial Landmark Detectors
Most face applications depend heavily on the accuracy of the face and facial landmarks detectors employed.
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification
The video and action classification have extremely evolved by deep neural networks specially with two stream CNN using RGB and optical flow as inputs and they present outstanding performance in terms of video analysis.
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images
These newly designed transferring techniques exploit multi-task learning frameworks to incorporate extra knowledge from other networks and additional datasets into the training procedure of event CNNs.
Deep Retinal Image Understanding
This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation.
Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban Reconstruction
Airborne acquisition and on-road mobile mapping provide complementary 3D information of an urban landscape: the former acquires roof structures, ground, and vegetation at a large scale, but lacks the facade and street-side details, while the latter is incomplete for higher floors and often totally misses out on pedestrian-only areas or undriven districts.
Dilemma First Search for Effortless Optimization of NP-Hard Problems
We evaluate DFS on two problems: First, the Knapsack problem, for which efficient algorithms exist, serves as a toy example.
One-Shot Video Object Segmentation
This paper tackles the task of semi-supervised video object segmentation, i. e., the separation of an object from the background in a video, given the mask of the first frame.
Building Deep Networks on Grassmann Manifolds
Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks.
Deep Temporal Linear Encoding Networks
Advantages of TLEs are: (a) they encode the entire video into a compact feature representation, learning the semantics and a discriminative feature space; (b) they are applicable to all kinds of networks like 2D and 3D CNNs for video classification; and (c) they model feature interactions in a more expressive way and without loss of information.
Weakly Supervised Cascaded Convolutional Networks
The final stage of both architectures is a part of a convolutional neural network that performs multiple instance learning on proposals extracted in the previous stage(s).
Ranked #2 on
Weakly Supervised Object Detection
on ImageNet
What Is Around The Camera?
How much does a single image reveal about the environment it was taken in?
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain
In this paper, we introduce the construction of a golden standard dataset that will aid research of multimodal question answering in the cultural heritage domain.
Deep Learning on Lie Groups for Skeleton-based Action Recognition
In recent years, skeleton-based action recognition has become a popular 3D classification problem.
DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition
We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components.
AENet: Learning Deep Audio Features for Video Analysis
Instead, combining visual features with our AENet features, which can be computed efficiently on a GPU, leads to significant performance improvements on action recognition and video highlight detection.
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs).
Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation
Regressing the hand pose can then be done by learning a discriminator to estimate the posterior of the latent pose given some depth maps.
McKernel: A Library for Approximate Kernel Expansions in Log-linear Time
The algorithm requires to compute the product of Walsh Hadamard Transform (WHT) matrices.
PathTrack: Fast Trajectory Annotation with Path Supervision
We further validate our approach by crowdsourcing the PathTrack dataset, with more than 15, 000 person trajectories in 720 sequences.
UntrimmedNets for Weakly Supervised Action Recognition and Detection
We exploit the learned models for action recognition (WSR) and detection (WSD) on the untrimmed video datasets of THUMOS14 and ActivityNet.
Ranked #3 on
Action Classification
on ActivityNet-1.2
Weakly Supervised Action Localization
Weakly-Supervised Action Recognition
Repeated Pattern Detection using CNN activations
We propose a new approach for detecting repeated patterns on a grid in a single image.
Learned Multi-Patch Similarity
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision.
Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos
Temporal information can provide additional cues about the location of body joints and help to alleviate these issues.
Ranked #4 on
Pose Estimation
on UPenn Action
The 2017 DAVIS Challenge on Video Object Segmentation
The DAVIS Challenge follows up on the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which has fostered the development of several novel state-of-the-art video object segmentation techniques.
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy.
On the Relation between Color Image Denoising and Classification
Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery.
Semantically-Guided Video Object Segmentation
This paper tackles the problem of semi-supervised video object segmentation, that is, segmenting an object in a sequence given its mask in the first frame.
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras.
Query-adaptive Video Summarization via Quality-aware Relevance Estimation
Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a search query has been less studied.
Speech-Based Visual Question Answering
This paper introduces speech-based visual question answering (VQA), the task of generating an answer given an image and a spoken question.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Temporal Segment Networks for Action Recognition in Videos
Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
Ranked #5 on
Video Classification
on COIN
WebVision Challenge: Visual Learning and Understanding With Web Data
The 2017 WebVision challenge consists of two tracks, the image classification task on WebVision test set, and the transfer learning task on PASCAL VOC 2012 dataset.
Pose Guided Person Image Generation
This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose.
Ranked #3 on
Gesture-to-Gesture Translation
on Senz3D
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Consensus Maximization With Linear Matrix Inequality Constraints
While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers.
An Analysis of Human-centered Geolocation
Experiments on the Fashion 144k and a Pinterest-based dataset show that the automatic methods succeed at this task to a reasonable extent.
Deep Domain Adaptation by Geodesic Distance Minimization
Our work builds on the recently proposed Deep CORAL method, which proposed to train a convolutional neural network and simultaneously minimize the Euclidean distance of convariance matrices between the source and target domains.
The WILDTRACK Multi-Camera Person Dataset
People detection methods are highly sensitive to the perpetual occlusions among the targets.
Fast Scene Understanding for Autonomous Driving
Most approaches for instance-aware semantic labeling traditionally focus on accuracy.
Semantic Instance Segmentation with a Discriminative Loss Function
In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.
Ranked #4 on
Multi-Human Parsing
on MHP v1.0
WebVision Database: Visual Learning and Understanding from Web Data
Our new WebVision database and relevant studies in this work would benefit the advance of learning state-of-the-art visual models with minimum supervision based on web data.
Semantic Foggy Scene Understanding with Synthetic Data
Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict clear-weather outdoor scenes, and then leverage these partially synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN).
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints.
Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery
In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool.
Video Object Segmentation Without Temporal Information
Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video frames.
Anchored Regression Networks Applied to Age Estimation and Super Resolution
We propose the Anchored Regression Network (ARN), a nonlinear regression network which can be seamlessly integrated into various networks or can be used stand-alone when the features have already been fixed.
Optimal Transformation Estimation With Semantic Cues
This paper addresses the problem of estimating the geometric transformation relating two distinct visual modalities (e. g. an image and a map, or a projective structure and a Euclidean 3D model) while relying only on semantic cues, such as semantically segmented regions or object bounding boxes.
Classification Driven Dynamic Image Enhancement
In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.
Optimal transport maps for distribution preserving operations on latent spaces of Generative Models
Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian.
Object Referring in Visual Scene with Spoken Language
This paper investigates Object Referring with Spoken Language (ORSpoken) by presenting two datasets and one novel approach.
Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
In contrast with prior work that restricts spatial templates to explicit spatial prepositions (e. g., "glass on table"), here we extend this concept to implicit spatial language, i. e., those relationships (generally actions) for which the spatial arrangement of the objects is only implicitly implied (e. g., "man riding horse").
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks
We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discov- ery in object detection pipelines.
Ranked #2 on
Weakly Supervised Object Detection
on COCO test-dev
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
Thus, by finetuning this network, we beat the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, e. g. Sports-1M, and finetuned on the target datasets, e. g. HMDB51/UCF101.
Dense 3D Regression for Hand Pose Estimation
Specifically, we decompose the pose parameters into a set of per-pixel estimations, i. e., 2D heat maps, 3D heat maps and unit 3D directional vector fields.
Ranked #4 on
Hand Pose Estimation
on MSRA Hands
Deep Extreme Cut: From Extreme Points to Object Segmentation
This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos.
Appearance-and-Relation Networks for Video Classification
Specifically, SMART blocks decouple the spatiotemporal learning module into an appearance branch for spatial modeling and a relation branch for temporal modeling.
Ranked #51 on
Action Recognition
on UCF101
Natural and Effective Obfuscation by Head Inpainting
As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection.
Detection-aided liver lesion segmentation using deep learning
A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments.
Improving Video Generation for Multi-functional Applications
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
To this end, we propose a new reality oriented adaptation approach for urban scene semantic segmentation by learning from synthetic data.
Wasserstein Divergence for GANs
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance.
Face Translation between Images and Videos using Identity-aware CycleGAN
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement.
Iterative Deep Learning for Network Topology Extraction
This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks.
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets.
Disentangled Person Image Generation
Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information.
Ranked #2 on
Gesture-to-Gesture Translation
on Senz3D
Error Correction for Dense Semantic Image Labeling
Pixelwise semantic image labeling is an important, yet challenging, task with many applications.
Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks
We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training.
ComboGAN: Unrestrained Scalability for Image Domain Translation
This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al.
Ranked #6 on
Facial Expression Translation
on CelebA
Object Referring in Videos with Language and Human Gaze
To that end, we present a new video dataset for OR, with 30, 000 objects over 5, 000 stereo video sequences annotated for their descriptions and gaze.
Conditional Probability Models for Deep Image Compression
During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation.
Towards End-to-End Lane Detection: an Instance Segmentation Approach
By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation.
Ranked #15 on
Lane Detection
on TuSimple
The 2018 DAVIS Challenge on Video Object Segmentation
Motivated by the analysis of the results of the 2017 edition, the main track of the competition will be the same than in the previous edition (segmentation given the full mask of the objects in the first frame -- semi-supervised scenario).
Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences
Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions.
Domain Adaptive Faster R-CNN for Object Detection in the Wild
The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
Towards Image Understanding from Deep Compression without Decoding
Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction, we propose to perform image understanding tasks such as classification and segmentation directly on the compressed representations produced by these compression methods.
Progressive Structure from Motion
Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision.
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e. g. steering angle and speed) by human drivers.
Adversarial Binary Coding for Efficient Person Re-identification
Specifically, instead of learning explicit projections or adding fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC) framework guides the extraction of binary codes implicitly and effectively.
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning
We mitigate this by introducing ensemble manifold segmentation (EMS).
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest.
Ranked #66 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
Viewpoint-aware Video Summarization
To satisfy these requirements (A)-(C) simultaneously, we proposed a novel video summarization method from multiple groups of videos.
Generative Adversarial Networks for Extreme Learned Image Compression
We present a learned image compression system based on GANs, operating at extremely low bitrates.
Failure Prediction for Autonomous Driving
This work presents a method to learn to predict the occurrence of these failures, i. e. to assess how difficult a scene is to a given driving model and to possibly give the human driver an early headsup.
Covariance Pooling For Facial Expression Recognition
In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition.
Facial Expression Recognition
Facial Expression Recognition (FER)
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.
Classification-Driven Dynamic Image Enhancement
In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.
WILDTRACK: A Multi-Camera HD Dataset for Dense Unscripted Pedestrian Detection
People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height.
Consensus Maximization for Semantic Region Correspondences
We propose a novel method for the geometric registration of semantically labeled regions.
Spatio-Temporal Channel Correlation Networks for Action Classification
Our experiments show that adding STC blocks to current state-of-the-art architectures outperforms the state-of-the-art methods on the HMDB51, UCF101 and Kinetics datasets.
Model-free Consensus Maximization for Non-Rigid Shapes
In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using `rules' on measurements.
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks
Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks.
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog.
Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length
In this paper we present a method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the perspective camera model and the isometric surface prior with unknown focal length.
Iterative Deep Learning for Road Topology Extraction
This paper tackles the task of estimating the topology of road networks from aerial images.
Sampling Algebraic Varieties for Robust Camera Autocalibration
This paper addresses the problem of robustly autocalibrating a moving camera with constant intrinsics.
Generative Domain-Migration Hashing for Sketch-to-Image Retrieval
The generative model learns a mapping that the distributions of sketches can be indistinguishable from the distribution of natural images using an adversarial loss, and simultaneously learns an inverse mapping based on the cycle consistency loss in order to enhance the indistinguishability.
stagNet: An Attentive Semantic RNN for Group Activity Recognition
Group activity recognition plays a fundamental role in a variety of applications, e. g. sports video analysis and intelligent surveillance.
Night-to-Day Image Translation for Retrieval-based Localization
We then compare the daytime and translated night images to obtain a pose estimate for the night image using the known 6-DOF position of the closest day image.
AI Benchmark: Running Deep Neural Networks on Android Smartphones
Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution.
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
This work addresses the problem of semantic image segmentation of nighttime scenes.
Ranked #12 on
Semantic Segmentation
on Nighttime Driving
Practical Full Resolution Learned Lossless Image Compression
We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000.
Ranked #3 on
Image Compression
on ImageNet32
An Unpaired Shape Transforming Method for Image Translation and Cross-Domain Retrieval
We address the problem of unpaired geometric image-to-image translation.
SMIT: Stochastic Multi-Label Image-to-Image Translation
This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality (e. g., style) associated with the translation.
Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map
The problem of localization often arises as part of a navigation process.
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
In this work, we take the advantage of additional geometric information from synthetic data, a powerful yet largely neglected cue, to bridge the domain gap.
DLOW: Domain Flow for Adaptation and Generalization
In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other.
Fast Perceptual Image Enhancement
We extend upon the results of Ignatov et al., where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras.
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding
The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog).
Ranked #5 on
Domain Adaptation
on Cityscapes-to-FoggyDriving
RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
RayNet integrates a CNN that learns view-invariant feature representations with an MRF that explicitly encodes the physics of perspective projection and occlusion.
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night via labeled synthetic images and unlabeled real images, both for progressively darker times of day, which exploits cross-time-of-day correspondences for the real images to guide the inference of their labels; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, designed for adverse conditions and including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 151 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark to perform our novel evaluation.
Ranked #7 on
Semantic Segmentation
on Nighttime Driving
End-to-end Lane Detection through Differentiable Least-Squares Fitting
The problem with such a two-step approach is that the parameters of the network are not optimized for the true task of interest (estimating the lane curvature parameters) but for a proxy task (segmenting the lane markings), resulting in sub-optimal performance.
Real-time 3D Traffic Cone Detection for Autonomous Driving
In this work, we leverage the unique structure of traffic cones and propose a pipelined approach to the problem.
Sparse and noisy LiDAR completion with RGB guidance anduncertainty
For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions.
Sparse and noisy LiDAR completion with RGB guidance and uncertainty
However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input.
Ranked #5 on
Depth Completion
on KITTI Depth Completion
A Three-Player GAN: Generating Hard Samples To Improve Classification Networks
The generator's objective is to synthesize samples that are both realistic and hard to label for the classifier.
Learning Accurate, Comfortable and Human-like Driving
Our model is trained and evaluated on the Drive360 dataset, which features 60 hours and 3000 km of real-world driving data.
Fast video object segmentation with Spatio-Temporal GANs
To achieve this, we concentrate all the heavy computational load to the training phase with two critics that enforce spatial and temporal mask consistency over the last K frames.
Branched Multi-Task Networks: Deciding What Layers To Share
In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand.
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Ranked #1 on
Video Generation
on TrailerFaces
Learning Discriminative Model Prediction for Tracking
The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking.
Ranked #5 on
Object Tracking
on FE108
A Novel BiLevel Paradigm for Image-to-Image Translation
Image-to-image (I2I) translation is a pixel-level mapping that requires a large number of paired training data and often suffers from the problems of high diversity and strong category bias in image scenes.
DynamoNet: Dynamic Action and Motion Network
With these overall objectives, to this end, we introduce a novel unified spatio-temporal 3D-CNN architecture (DynamoNet) that jointly optimizes the video classification and learning motion representation by predicting future frames as a multi-task learning problem.
Large Scale Holistic Video Understanding
HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene.
Ranked #11 on
Action Recognition
on UCF101
The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation
We present the 2019 DAVIS Challenge on Video Object Segmentation, the third edition of the DAVIS Challenge series, a public competition designed for the task of Video Object Segmentation (VOS).
Semi-Supervised Learning by Augmented Distribution Alignment
We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data.
3D Appearance Super-Resolution with Deep Learning
Experimental results demonstrate that our proposed networks successfully incorporate the 3D geometric information and super-resolve the texture maps.
Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
State-of-the-art approaches for semantic segmentation rely on deep convolutional neural networks trained on fully annotated datasets, that have been shown to be notoriously expensive to collect, both in terms of time and money.
Image Segmentation
Weakly supervised Semantic Segmentation
+1
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
In this work we propose a new clustering loss function for proposal-free instance segmentation.
Ranked #1000000000 on
Instance Segmentation
on Cityscapes test
Learning a Curve Guardian for Motorcycles
Our contributes are fourfold: 1) we predict the motorcycle's intra-lane position using a convolutional neural network (CNN), 2) we predict the motorcycle roll angle using a CNN, 3) we use an upgraded controller model that incorporates road incline for a more realistic model and prediction, 4) we design a scale-able system by utilizing HERE Technologies map database to obtain the accurate road geometry of the future path.
Dual Grid Net: hand mesh vertex regression from single depth maps
In the first stage, the network estimates a dense correspondence field for every pixel on the depth map or image grid to the mesh grid.
Learning Filter Basis for Convolutional Neural Network Compression
Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images.
Texture Underfitting for Domain Adaptation
Comprehensive semantic segmentation is one of the key components for robust scene understanding and a requirement to enable autonomous driving.
Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation
Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature.
Talk2Car: Taking Control of Your Self-Driving Car
Or more specifically, we consider the problem in an autonomous driving setting, where a passenger requests an action that can be associated with an object found in a street scene.
Convex Relaxations for Consensus and Non-Minimal Problems in 3D Vision
Notably, we further exploit the POP formulation of non-minimal solver also for the generic consensus maximization problems in 3D vision.
Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory
Our first contribution is the creation of a large-scale dataset with verbal navigation instructions.
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
This work addresses the problem of semantic scene understanding under foggy road conditions.
AI Benchmark: All About Deep Learning on Smartphones in 2019
The performance of mobile AI accelerators has been evolving rapidly in the past two years, nearly doubling with each new generation of SoCs.
Divide-and-Conquer Adversarial Learning for High-Resolution Image and Video Enhancement
This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.
Self-supervised Object Motion and Depth Estimation from Video
We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video.
