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Found 59 papers, 19 papers with code
A continual learning survey: Defying forgetting in classification tasks
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.
Image Demoireing with Learnable Bandpass Filters
Image demoireing is a multi-faceted image restoration task involving both texture and color restoration.
Ranked #2 on
Image Enhancement
on TIP 2018
(using extra training data)
G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation.
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds
Labelling point clouds fully is highly time-consuming and costly.
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image.
Ranked #7 on
6D Pose Estimation using RGBD
on REAL275
A Multi-Hypothesis Approach to Color Constancy
Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.
DepthTrack: Unveiling the Power of RGBD Tracking
This can be explained by the fact that there are no sufficiently large RGBD datasets to 1) train "deep depth trackers" and to 2) challenge RGB trackers with sequences for which the depth cue is essential.
HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation
In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation.
Multi-task Learning with 3D-Aware Regularization
Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.
Wavelet-Based Dual-Branch Network for Image Demoireing
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data.
Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.
Image Denoising and the Generative Accumulation of Photons
This new perspective allows us to make three contributions: We present a new strategy for self-supervised denoising, We present a new method for sampling from the posterior of possible solutions by iteratively sampling and adding small numbers of photons to the image.
More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning
Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.
Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in images
With this, we show that HRA database poses a challenge at a higher level for the well studied representation learning methods, and provide a benchmark in the task of human rights violations recognition in visual context.
Wavelet-Based Network For High Dynamic Range Imaging
The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Object detection has witnessed significant progress by relying on large, manually annotated datasets.
A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model
A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP).
Adaptive Spiral Layers for Efficient 3D Representation Learning on Meshes
The success of deep learning models on structured data has generated significant interest in extending their application to non-Euclidean domains.
Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?
We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data.
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database
Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research.
Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help?
We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations.
Evaluating Deep Convolutional Neural Networks for Material Classification
Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention.
Semantic tracking: Single-target tracking with inter-supervised convolutional networks
Since each branch in NetT is trained by the videos of a specific category or groups of similar categories, NetT encodes category-based features for tracking.
Automatic Selection of the Optimal Local Feature Detector
The efficiency and the good accuracy in determining the optimal feature detector for any operating condition, make the proposed tool suitable to be utilized in real visual applications.
A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors
Since local feature detection has been one of the most active research areas in computer vision during the last decade, a large number of detectors have been proposed.
A Novel Performance Evaluation Methodology for Single-Target Trackers
This paper addresses the problem of single-target tracker performance evaluation.
A Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization
We propose a joint object pose estimation and categorization approach which extracts information about object poses and categories from the object parts and compositions constructed at different layers of a hierarchical object representation algorithm, namely Learned Hierarchy of Parts (LHOP).
Learning a Hierarchical Compositional Shape Vocabulary for Multi-class Object Representation
At the top-level of the vocabulary, the compositions are sufficiently large and complex to represent the whole shapes of the objects.
A Summary of the 4th International Workshop on Recovering 6D Object Pose
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
Learning to Exploit Stability for 3D Scene Parsing
We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples.
Evaluating multi-class learning strategies in a generative hierarchical framework for object detection
We explore and compare their computational behavior (space and time) and detection performance as a function of the number of learned classes on several recognition data sets.
Single Target Tracking Using Adaptive Clustered Decision Trees and Dynamic Multi-Level Appearance Models
This paper presents a method for single target tracking of arbitrary objects in challenging video sequences.
Compositional Hierarchical Representation of Shape Manifolds for Classification of Non-Manifold Shapes
We represent the topological relationship between shape components using graphs, which are aggregated to construct a hierarchical graph structure for the shape vocabulary.
Rolling Shutter Correction in Manhattan World
A vast majority of consumer cameras operate the rolling shutter mechanism, which often produces distorted images due to inter-row delay while capturing an image.
Deep Dexterous Grasping of Novel Objects from a Single View
First, we present a simulator for generating and testing dexterous grasps.
AIM 2019 Challenge on Image Demoireing: Dataset and Study
In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
Diagnosing and Preventing Instabilities in Recurrent Video Processing
Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Learning a Model-Driven Variational Network for Deformable Image Registration
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images
We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs.
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals
In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks.
Depth-only Object Tracking
There are two potential reasons for the heuristics: 1) the lack of large RGBD tracking datasets to train deep RGBD trackers and 2) the long-term evaluation protocol of VOT RGBD that benefits from heuristics such as depth-based occlusion detection.
Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments
Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price.
Repurposing Existing Deep Networks for Caption and Aesthetic-Guided Image Cropping
We propose a novel optimization framework that crops a given image based on user description and aesthetics.
Self-supervised HDR Imaging from Motion and Exposure Cues
Recent High Dynamic Range (HDR) techniques extend the capabilities of current cameras where scenes with a wide range of illumination can not be accurately captured with a single low-dynamic-range (LDR) image.
HDR Reconstruction from Bracketed Exposures and Events
Our proposed approach surpasses SoTA multi-frame HDR reconstruction methods using synthetic and real events, with a 2dB and 1dB improvement in PSNR-L and PSNR-mu on the HdM HDR dataset, respectively.
Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution
Estimating the pose and shape of hands and objects under interaction finds numerous applications including augmented and virtual reality.
Ranked #6 on
hand-object pose
on DexYCB
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
Depth estimation is a core task in 3D computer vision.
S$^2$Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning
In this paper, we propose a novel semi-supervised framework that allows us to learn contact from monocular images.
Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garment
Specifically, we focus on human appearance and learn implicit pose, shape and garment representations of dressed humans from RGB images.
Content-Diverse Comparisons improve IQA
This restricts the diversity and number of image pairs that the model is exposed to during training.
Label-Efficient Object Detection via Region Proposal Network Pre-Training
In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance.
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation
The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task.
ILSH: The Imperial Light-Stage Head Dataset for Human Head View Synthesis
This paper introduces the Imperial Light-Stage Head (ILSH) dataset, a novel light-stage-captured human head dataset designed to support view synthesis academic challenges for human heads.
GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement
Object pose refinement is essential for robust object pose estimation.
