research
∙
07/31/2023
BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models
The rise in popularity of text-to-image generative artificial intelligen...
share
research
∙
10/08/2022
Contact-aware Human Motion Forecasting
In this paper, we tackle the task of scene-aware 3D human motion forecas...
research
∙
12/23/2021
Manifold Learning Benefits GANs
In this paper, we improve Generative Adversarial Networks by incorporati...
research
∙
11/30/2021
Semi-Supervised 3D Hand Shape and Pose Estimation with Label Propagation
To obtain 3D annotations, we are restricted to controlled environments o...
research
∙
06/16/2021
Invertible Attention
Attention has been proved to be an efficient mechanism to capture long-r...
research
∙
04/11/2021
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 mult...
research
∙
04/06/2021
Learning to Estimate Hidden Motions with Global Motion Aggregation
Occlusions pose a significant challenge to optical flow algorithms that ...
research
∙
04/05/2021
Learning Optical Flow from a Few Matches
State-of-the-art neural network models for optical flow estimation requi...
research
∙
03/25/2021
Few-shot Weakly-Supervised Object Detection via Directional Statistics
Detecting novel objects from few examples has become an emerging topic i...
research
∙
12/10/2020
Uncertainty-Aware Deep Calibrated Salient Object Detection
Existing deep neural network based salient object detection (SOD) method...
research
∙
12/01/2020
Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration
The dual-pixel (DP) hardware works by splitting each pixel in half and c...
research
∙
10/09/2020
Deep Learning Superpixel Semantic Segmentation with Transparent Initialization and Sparse Encoder
Even though deep learning greatly improves the performance of semantic s...
research
∙
10/06/2020
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Although 3D Convolutional Neural Networks (CNNs) are essential for most ...
research
∙
06/23/2020
Post-hoc Calibration of Neural Networks
Calibration of neural networks is a critical aspect to consider when inc...
research
∙
06/23/2020
Calibration of Neural Networks using Splines
Calibrating neural networks is of utmost importance when employing them ...
research
∙
04/01/2020
Single Image Optical Flow Estimation with an Event Camera
Event cameras are bio-inspired sensors that asynchronously report intens...
research
∙
03/18/2020
In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) methods have become increas...
research
∙
03/15/2020
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks
Predicting calibrated confidence scores for multi-class deep networks is...
research
∙
02/26/2020
Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization
We address the problem of joint optical flow and camera motion estimatio...
research
∙
11/18/2019
Action Anticipation with RBF Kernelized Feature Mapping RNN
We introduce a novel Recurrent Neural Network-based algorithm for future...
research
∙
11/18/2019
Action Anticipation with RBF KernelizedFeature Mapping RNN
We introduce a novel Recurrent Neural Network-based algorithm for future...
research
∙
10/24/2019
Fast and Differentiable Message Passing for Stereo Vision
Despite the availability of many Markov Random Field (MRF) optimization ...
research
∙
10/18/2019
Mirror Descent View for Neural Network Quantization
Quantizing large Neural Networks (NN) while maintaining the performance ...
research
∙
09/30/2019
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation
We present a new approach for a single view, image-based object pose est...
research
∙
09/29/2019
Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
The existing approaches for salient motion segmentation are unable to ex...
research
∙
09/11/2019
Deep Declarative Networks: A New Hope
We introduce a new class of end-to-end learnable models wherein data pro...
research
∙
04/29/2019
Learning to Find Common Objects Across Image Collections
We address the problem of finding a set of images containing a common, b...
research
∙
04/07/2019
Recovering Faces from Portraits with Auxiliary Facial Attributes
Recovering a photorealistic face from an artistic portrait is a challeng...
research
∙
04/07/2019
Identity-preserving Face Recovery from Stylized Portraits
Given an artistic portrait, recovering the latent photorealistic face th...
research
∙
03/12/2019
Bringing Blurry Alive at High Frame-Rate with an Event Camera
Event-based cameras can measure intensity changes (called ` events') wit...
research
∙
01/22/2019
Super-Trajectories: A Compact Yet Rich Video Representation
We propose a new video representation in terms of an over-segmentation o...
research
∙
12/11/2018
Proximal Mean-field for Neural Network Quantization
Compressing large neural networks by quantizing the parameters, while ma...
research
∙
11/26/2018
Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring
The image blurring process is generally modelled as the convolution of a...
research
∙
11/26/2018
Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera
Event-based cameras can measure intensity changes (called ` events') wit...
research
∙
11/22/2018
Generalized Range Moves
We consider move-making algorithms for energy minimization of multi-labe...
research
∙
11/02/2018
Scalable Deep k-Subspace Clustering
Subspace clustering algorithms are notorious for their scalability issue...
research
∙
04/16/2018
Block Mean Approximation for Efficient Second Order Optimization
Advanced optimization algorithms such as Newton method and AdaGrad benef...
research
∙
03/29/2018
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective
The success of current deep saliency detection methods heavily depends o...
research
∙
03/27/2018
Non-Linear Temporal Subspace Representations for Activity Recognition
Representations that can compactly and effectively capture the temporal ...
research
∙
01/08/2018
Identity-preserving Face Recovery from Portraits
Recovering the latent photorealistic faces from their artistic portraits...
research
∙
05/24/2017
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces
Representations that can compactly and effectively capture temporal evol...
research
∙
05/20/2016
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods
Representing images and videos with Symmetric Positive Definite (SPD) ma...
research
∙
07/23/2015
Multi-Target Tracking with Time-Varying Clutter Rate and Detection Profile: Application to Time-lapse Cell Microscopy Sequences
Quantitative analysis of the dynamics of tiny cellular and sub-cellular ...
research
∙
12/13/2014
Optimizing Over Radial Kernels on Compact Manifolds
We tackle the problem of optimizing over all possible positive definite ...
research
∙
12/13/2014
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
Symmetric Positive Definite (SPD) matrices have become popular to encode...
research
∙
11/30/2014
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
In this paper, we develop an approach to exploiting kernel methods with ...
research
∙
11/24/2014
Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors
While widely acknowledged as highly effective in computer vision, multi-...
research
∙
08/30/2014
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
This paper introduces sparse coding and dictionary learning for Symmetri...
research
∙
07/04/2014
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Modeling videos and image-sets as linear subspaces has proven beneficial...
research
∙
07/04/2014