Search Results for author:
Found 34 papers, 18 papers with code
Deep Novel View Synthesis from Colored 3D Point Clouds
We propose a new deep neural network which takes a colored 3D point cloud of a scene, and directly synthesizes a photo-realistic image from an arbitrary viewpoint.
Stale Diffusion: Hyper-realistic 5D Movie Generation Using Old-school Methods
As the horses have long left the barn, our proposal may be seen as antiquated and irrelevant.
An Empirical Study Into What Matters for Calibrating Vision-Language Models
Vision--Language Models (VLMs) have emerged as the dominant approach for zero-shot recognition, adept at handling diverse scenarios and significant distribution changes.
SCENES: Subpixel Correspondence Estimation With Epipolar Supervision
We relax this assumption by removing the requirement of 3D structure, e. g., depth maps or point clouds, and only require camera pose information, which can be obtained from odometry.
Detecting and Restoring Non-Standard Hands in Stable Diffusion Generated Images
We introduce a pipeline to address anatomical inaccuracies in Stable Diffusion generated hand images.
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.
LoCUS: Learning Multiscale 3D-consistent Features from Posed Images
We start from the idea that the training objective can be framed as a patch retrieval problem: given an image patch in one view of a scene, we would like to retrieve (with high precision and recall) all patches in other views that map to the same real-world location.
Exploring Predicate Visual Context in Detecting Human-Object Interactions
Recently, the DETR framework has emerged as the dominant approach for human--object interaction (HOI) research.
Ranked #2 on
Human-Object Interaction Detection
on HICO-DET
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.
Rethinking Polyp Segmentation from an Out-of-Distribution Perspective
Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach.
SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Data
We present a method for the accurate 3D reconstruction of partly-symmetric objects.
A 23 MW data centre is all you need
The field of machine learning has achieved striking progress in recent years, witnessing breakthrough results on language modelling, protein folding and nitpickingly fine-grained dog breed classification.
Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching
We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images.
Exploiting Problem Structure in Deep Declarative Networks: Two Case Studies
Deep declarative networks and other recent related works have shown how to differentiate the solution map of a (continuous) parametrized optimization problem, opening up the possibility of embedding mathematical optimization problems into end-to-end learnable models.
Efficient Two-Stage Detection of Human-Object Interactions with a Novel Unary-Pairwise Transformer
Recent developments in transformer models for visual data have led to significant improvements in recognition and detection tasks.
Ranked #10 on
Human-Object Interaction Detection
on V-COCO
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
In video transformers, the time dimension is often treated in the same way as the two spatial dimensions.
Ranked #15 on
Action Recognition
on EPIC-KITCHENS-100
(using extra training data)
Zero-Shot Learning on 3D Point Cloud Objects and Beyond
Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification.
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
Geometry-Guided Street-View Panorama Synthesis from Satellite Imagery
Specifically, we observe that when a 3D point in the real world is visible in both views, there is a deterministic mapping between the projected points in the two-view images given the height information of this 3D point.
Spatially Conditioned Graphs for Detecting Human-Object Interactions
We address the problem of detecting human-object interactions in images using graphical neural networks.
Ranked #18 on
Human-Object Interaction Detection
on V-COCO
(using extra training data)
Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization
We instead propose the first fully end-to-end trainable network for solving the blind PnP problem efficiently and globally, that is, without the need for pose priors.
The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks.
Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching
Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (e. g., satellite) images.
Inferring Temporal Compositions of Actions Using Probabilistic Automata
This paper presents a framework to recognize temporal compositions of atomic actions in videos.
Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem
Conventional absolute camera pose via a Perspective-n-Point (PnP) solver often assumes that the correspondences between 2D image pixels and 3D points are given.
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.
Transductive Zero-Shot Learning for 3D Point Cloud Classification
This paper extends, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification.
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.
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects
In this paper, we therefore propose a loss to specifically address the hubness problem.
The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation
Determining the position and orientation of a calibrated camera from a single image with respect to a 3D model is an essential task for many applications.
Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence
Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications.
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization.
Ranked #6 on
Point Cloud Registration
on FP-O-H
GOGMA: Globally-Optimal Gaussian Mixture Alignment
Gaussian mixture alignment is a family of approaches that are frequently used for robustly solving the point-set registration problem.
An Adaptive Data Representation for Robust Point-Set Registration and Merging
This paper presents a framework for rigid point-set registration and merging using a robust continuous data representation.
