Search Results for author:
Found 69 papers, 26 papers with code
Severity Controlled Text-to-Image Generative Model Bias Manipulation
Charting the susceptibility of T2I models to such manipulation, we first expose the new possibility of a dynamic and computationally efficient exploitation of model bias by targeting the embedded language models.
Motion Mamba: Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM
Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging.
Quantifying Bias in Text-to-Image Generative Models
Bias in text-to-image (T2I) models can propagate unfair social representations and may be used to aggressively market ideas or push controversial agendas.
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models
We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy.
BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models
Based on the penetration level, BAGM takes the form of a suite of attacks that are referred to as surface, shallow and deep attacks in this article.
LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network
In this paper, we propose, to the best of our knowledge, the first framework that introduces the contrastive language-image pre-training framework (CLIP) to accurately estimate the blur map from a DP pair unsupervisedly.
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space-not every pattern of pixels is an image.
Adversarial Purification with the Manifold Hypothesis
In this work, we formulate a novel framework for adversarial robustness using the manifold hypothesis.
Contact-aware Human Motion Forecasting
In this paper, we tackle the task of scene-aware 3D human motion forecasting, which consists of predicting future human poses given a 3D scene and a past human motion.
Ranked #2 on
Human Pose Forecasting
on GTA-IM Dataset
Manifold Learning Benefits GANs
In our design, the manifold learning and coding steps are intertwined with layers of the discriminator, with the goal of attracting intermediate feature representations onto manifolds.
Semi-Supervised 3D Hand Shape and Pose Estimation with Label Propagation
To obtain 3D annotations, we are restricted to controlled environments or synthetic datasets, leading us to 3D datasets with less generalizability to real-world scenarios.
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model
Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty.
Invertible Attention
Attention has been proved to be an efficient mechanism to capture long-range dependencies.
One Ring to Rule Them All: a simple solution to multi-view 3D-Reconstruction of shapes with unknown BRDF via a small Recurrent ResNet
This paper proposes a simple method which solves an open problem of multi-view 3D-Reconstruction for objects with unknown and generic surface materials, imaged by a freely moving camera and a freely moving point light source.
Learning to Estimate Hidden Motions with Global Motion Aggregation
We demonstrate that the optical flow estimates in the occluded regions can be significantly improved without damaging the performance in non-occluded regions.
Ranked #5 on
Optical Flow Estimation
on Sintel-final
Learning Optical Flow from a Few Matches
In this paper, we show that the dense correlation volume representation is redundant and accurate flow estimation can be achieved with only a fraction of elements in it.
Ranked #11 on
Optical Flow Estimation
on KITTI 2015 (train)
Few-shot Weakly-Supervised Object Detection via Directional Statistics
When the novel objects are localized, we utilize them to learn a linear appearance model to detect novel classes in new images.
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
In this work, we introduce a Resource Aware Neuron Pruning (RANP) algorithm that prunes 3D CNNs at initialization to high sparsity levels.
Uncertainty-Aware Deep Calibrated Salient Object Detection
Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy.
Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration
The heavy defocus blur in DP pairs affects the performance of matching-based depth estimation approaches.
Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse Encoder
We achieve it with fully-connected layers with Transparent Initialization (TI) and efficient logit consistency using a sparse encoder.
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Specifically, the core idea is to obtain an importance score for each neuron based on their sensitivity to the loss function.
Calibration of Neural Networks using Splines
From this, by approximating the empirical cumulative distribution using a differentiable function via splines, we obtain a recalibration function, which maps the network outputs to actual (calibrated) class assignment probabilities.
Post-hoc Calibration of Neural Networks by g-Layers
Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions are equally important as the decisions themselves.
Single Image Optical Flow Estimation with an Event Camera
Then, we consider the special case of image blur caused by high dynamics in the visual environments and show that including the blur formation in our model further constrains flow estimation.
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms.
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
A common approach is to learn a post-hoc calibration function that transforms the output of the original network into calibrated confidence scores while maintaining the network's accuracy.
Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization
We address the problem of joint optical flow and camera motion estimation in rigid scenes by incorporating geometric constraints into an unsupervised deep learning framework.
Action Anticipation with RBF Kernelized Feature Mapping RNN
We introduce a novel Recurrent Neural Network-based algorithm for future video feature generation and action anticipation called feature mapping RNN.
Fast and Differentiable Message Passing on Pairwise Markov Random Fields
In addition to differentiability, the two main aspects that enable learning these model parameters are the forward and backward propagation time of the MRF optimization algorithm and its inference capabilities.
Mirror Descent View for Neural Network Quantization
Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity.
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation
We present a new approach for a single view, image-based object pose estimation.
Ranked #13 on
6D Pose Estimation using RGB
on Occlusion LineMOD
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
To handle the nonrigid background like a sea, we also propose a robust fusion mechanism between motion and appearance-based features.
Deep Declarative Networks: A New Hope
We show how these declarative processing nodes can be implemented in the popular PyTorch deep learning software library allowing declarative and imperative nodes to co-exist within the same network.
Learning to Find Common Objects Across Few Image Collections
We model the selection as an energy minimization problem with unary and pairwise potential functions.
Identity-preserving Face Recovery from Stylized Portraits
We develop an Identity-preserving Face Recovery from Portraits (IFRP) method that utilizes a Style Removal network (SRN) and a Discriminative Network (DN).
Recovering Faces from Portraits with Auxiliary Facial Attributes
%Our method can recover high-quality photorealistic faces from unaligned portraits while preserving the identity of the face images as well as it can reconstruct a photorealistic face image with a desired set of attributes.
High Frame Rate Video Reconstruction based on an Event Camera
Based on the abundant event data alongside a low frame rate, easily blurred images, we propose a simple yet effective approach to reconstruct high-quality and high frame rate sharp videos.
Super-Trajectories: A Compact Yet Rich Video Representation
We propose a new video representation in terms of an over-segmentation of dense trajectories covering the whole video.
Proximal Mean-field for Neural Network Quantization
Compressing large Neural Networks (NN) by quantizing the parameters, while maintaining the performance is highly desirable due to reduced memory and time complexity.
Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera
In this paper, we propose a simple and effective approach, the \textbf{Event-based Double Integral (EDI)} model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data.
Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring
The image blurring process is generally modelled as the convolution of a blur kernel with a latent image.
Generalized Range Moves
We consider move-making algorithms for energy minimization of multi-label Markov Random Fields (MRFs).
Scalable Deep $k$-Subspace Clustering
In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm.
Face Super-resolution Guided by Facial Component Heatmaps
State-of-the-art face super-resolution methods use deep convolutional neural networks to learn a mapping between low-resolution (LR) facial patterns and their corresponding high-resolution (HR) counterparts by exploring local information.
Super-Resolving Very Low-Resolution Face Images With Supplementary Attributes
An LR input contains low-frequency facial components of its HR version while its residual face image defined as the difference between the HR ground-truth and interpolated LR images contains the missing high-frequency facial details.
Block Mean Approximation for Efficient Second Order Optimization
Advanced optimization algorithms such as Newton method and AdaGrad benefit from second order derivative or second order statistics to achieve better descent directions and faster convergence rates.
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective
Such supervision, while labor-intensive and not always possible, tends to hinder the generalization ability of the learned models.
Non-Linear Temporal Subspace Representations for Activity Recognition
As these features are often non-linear, we propose a novel pooling method, kernelized rank pooling, that represents a given sequence compactly as the pre-image of the parameters of a hyperplane in a reproducing kernel Hilbert space, projections of data onto which captures their temporal order.
Identity-preserving Face Recovery from Portraits
In this paper, we present a new Identity-preserving Face Recovery from Portraits (IFRP) to recover latent photorealistic faces from unaligned stylized portraits.
Joint Dimensionality Reduction and Metric Learning: A Geometric Take
To be tractable and robust to data noise, existing metric learning algorithms commonly rely on PCA as a pre-processing step.
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces
As these features are often non-linear, we propose a novel pooling method, kernelized rank pooling, that represents a given sequence compactly as the pre-image of the parameters of a hyperplane in an RKHS, projections of data onto which captures their temporal order.
Memory Efficient Max Flow for Multi-label Submodular MRFs
Multi-label submodular Markov Random Fields (MRFs) have been shown to be solvable using max-flow based on an encoding of the labels proposed by Ishikawa, in which each variable $X_i$ is represented by $\ell$ nodes (where $\ell$ is the number of labels) arranged in a column.
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods
This lets us formulate dimensionality reduction as the problem of finding a projection that yields a low-dimensional manifold either with maximum discriminative power in the supervised scenario, or with maximum variance of the data in the unsupervised one.
Multi-Target Tracking with Time-Varying Clutter Rate and Detection Profile: Application to Time-lapse Cell Microscopy Sequences
To deal with this, we propose a bootstrap filter composed of an estimator and a tracker.
A Linear Least-Squares Solution to Elastic Shape-From-Template
We cast SfT (Shape-from-Template) as the search of a vector field (X, dX), composed of the pose X and the displacement dX that produces the deformation.
Optimizing Over Radial Kernels on Compact Manifolds
We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification.
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
To encode the geometry of the manifold in the mapping, we introduce a family of provably positive definite kernels on the Riemannian manifold of SPD matrices.
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
We then use the proposed framework to identify positive definite kernels on two specific manifolds commonly encountered in computer vision: the Riemannian manifold of symmetric positive definite matrices and the Grassmann manifold, i. e., the Riemannian manifold of linear subspaces of a Euclidean space.
Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors
While widely acknowledged as highly effective in computer vision, multi-label MRFs with non-convex priors are difficult to optimize.
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas.
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks.
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices
In particular, we search for a projection that yields a low-dimensional manifold with maximum discriminative power encoded via an affinity-weighted similarity measure based on metrics on the manifold.
On Projective Reconstruction In Arbitrary Dimensions
The current theory, due to Hartley and Schaffalitzky, is based on the Grassmann tensor, generalizing the ideas of fundamental matrix, trifocal tensor and quadrifocal tensor used in the well-studied case of 3D to 2D projections.
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds.
Mirror Surface Reconstruction from a Single Image
In such conditions, our differential geometry analysis provides a theoretical proof that the shape of the mirror surface can be uniquely recovered if the pose of the reference target is known.
Monocular Template-Based 3D Reconstruction of Extensible Surfaces with Local Linear Elasticity
We extend the method of isometric surface reconstruction and more recent work on conformal surface reconstruction.
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry.
