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Found 35 papers, 7 papers with code
Unsupervised Attention-guided Image to Image Translation
Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene.
Unsupervised Attention-guided Image-to-Image Translation
Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene.
Improving Shape Deformation in Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change.
BoIR: Box-Supervised Instance Representation for Multi-Person Pose Estimation
Our new instance embedding loss provides a learning signal on the entire area of the image with bounding box annotations, achieving globally consistent and disentangled instance representation.
RGBD-Dog: Predicting Canine Pose from RGBD Sensors
We evaluate our model on both synthetic and real RGBD images and compare our results to previously published work fitting canine models to images.
Generating Object Stamps
We present an algorithm to generate diverse foreground objects and composite them into background images using a GAN architecture.
In Search of a Data Transformation That Accelerates Neural Field Training
Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal.
Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios
Our architecture jointly learns multiple sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects; and joint registration of multiple objects.
Deep Convolutional Decision Jungle for Image Classification
Each response map-or node-in both the convolutional and fully-connected layers selectively respond to class labels s. t.
Ranked #168 on
Image Classification
on CIFAR-100
(using extra training data)
Augmented Skeleton Space Transfer for Depth-based Hand Pose Estimation
By training the HPG and HPE in a single unified optimization framework enforcing that 1) the HPE agrees with the paired depth and skeleton entries; and 2) the HPG-HPE combination satisfies the cyclic consistency (both the input and the output of HPG-HPE are skeletons) observed via the newly generated unpaired skeletons, our algorithm constructs a HPE which is robust to variations that go beyond the coverage of the existing database.
Ranking CGANs: Subjective Control over Semantic Image Attributes
In this paper, we investigate the use of generative adversarial networks in the task of image generation according to subjective measures of semantic attributes.
Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking
Large databases are often organized by hand-labeled metadata, or criteria, which are expensive to collect.
Real-time Online Action Detection Forests using Spatio-temporal Contexts
Online action detection (OAD) is challenging since 1) robust yet computationally expensive features cannot be straightforwardly used due to the real-time processing requirements and 2) the localization and classification of actions have to be performed even before they are fully observed.
Context-guided diffusion for label propagation on graphs
Existing approaches for diffusion on graphs, e. g., for label propagation, are mainly focused on isotropic diffusion, which is induced by the commonly-used graph Laplacian regularizer.
Semi-supervised Learning with Explicit Relationship Regularization
In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points.
Local High-order Regularization on Data Manifolds
The iterated graph Laplacian enables high-order regularization, but it has a high computational complexity and so cannot be applied to large problems.
On Implicit Filter Level Sparsity in Convolutional Neural Networks
We investigate filter level sparsity that emerges in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay.
Multi-Task Learning by Maximizing Statistical Dependence
We tackle this by considering task-specific estimators as random variables.
High-Order Tensor Regularization With Application to Attribute Ranking
When learning functions on manifolds, we can improve performance by regularizing with respect to the intrinsic manifold geometry rather than the ambient space.
Predictor Combination at Test Time
We present an algorithm for test-time combination of a set of reference predictors with unknown parametric forms.
Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Rendering
Once the model is successfully fitted to input RGB images, its meshes i. e. shapes and articulations, are realistic, and we augment view-points on top of estimated dense hand poses.
Joint Manifold Diffusion for Combining Predictions on Decoupled Observations
We present a new predictor combination algorithm that improves a given task predictor based on potentially relevant reference predictors.
Implicit Filter Sparsification In Convolutional Neural Networks
We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay.
Task-Aware Variational Adversarial Active Learning
Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques.
Combining Task Predictors via Enhancing Joint Predictability
Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the internals of individual predictors.
Look here! A parametric learning based approach to redirect visual attention
Extensions of our model allow for multi-style edits and the ability to both increase and attenuate attention in an image region.
GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes
We present an algorithm that learns a coarse 3D representation of objects from unposed multi-view 2D mask supervision, then uses it to generate detailed mask and image texture.
DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation
Synthetic data is becoming increasingly common for training computer vision models for a variety of tasks.
End-to-End Detection and Pose Estimation of Two Interacting Hands
Three dimensional hand pose estimation has reached a level of maturity, enabling real-world applications for single-hand cases.
Testing using Privileged Information by Adapting Features with Statistical Dependence
Then, we empirically estimate and strengthen the statistical dependence between the initial noisy predictor and the additional features via manifold denoising.
Emergence of Implicit Filter Sparsity in Convolutional Neural Networks
We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained using adaptive gradient descent techniques with L2 regularization or weight decay.
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
Robust Combination of Distributed Gradients Under Adversarial Perturbations
We consider distributed (gradient descent-based) learning scenarios where the server combines the gradients of learning objectives gathered from local clients.
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.
Active Learning Guided by Efficient Surrogate Learners
Our proposed model adeptly updates the surrogate learner for every new data instance, enabling it to emulate and capitalize on the continuous learning dynamics of the neural network without necessitating a complete retraining of the principal model for each individual label.
