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Found 64 papers, 28 papers with code
Efficient Pruning LMI Conditions for Branch-and-Prune Rank and Chirality-Constrained Estimation of the Dual Absolute Quadric
We present a new globally optimal algorithm for self-calibrating a moving camera with constant parameters.
LMI-Based 2D-3D Registration: From Uncalibrated Images to Euclidean Scene
This paper investigates the problem of registering a scanned scene, represented by 3D Euclidean point coordinates, and two or more uncalibrated cameras.
Robust and Optimal Sum-of-Squares-Based Point-to-Plane Registration of Image Sets and Structured Scenes
This paper deals with the problem of registering a known structured 3D scene and its metric Structure-from-Motion (SfM) counterpart.
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Consensus Maximization With Linear Matrix Inequality Constraints
While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers.
Optimal Transformation Estimation With Semantic Cues
This paper addresses the problem of estimating the geometric transformation relating two distinct visual modalities (e. g. an image and a map, or a projective structure and a Euclidean 3D model) while relying only on semantic cues, such as semantically segmented regions or object bounding boxes.
Improving Video Generation for Multi-functional Applications
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
Face Translation between Images and Videos using Identity-aware CycleGAN
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement.
Model-free Consensus Maximization for Non-Rigid Shapes
In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using `rules' on measurements.
Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length
In this paper we present a method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the perspective camera model and the isometric surface prior with unknown focal length.
Sampling Algebraic Varieties for Robust Camera Autocalibration
This paper addresses the problem of robustly autocalibrating a moving camera with constant intrinsics.
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution.
Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map
The problem of localization often arises as part of a navigation process.
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Ranked #1 on
Video Generation
on TrailerFaces
Convex Relaxations for Consensus and Non-Minimal Problems in 3D Vision
Notably, we further exploit the POP formulation of non-minimal solver also for the generic consensus maximization problems in 3D vision.
Divide-and-Conquer Adversarial Learning for High-Resolution Image and Video Enhancement
This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing
Face anti-spoofing is a measure towards this direction for bio-metric user authentication, and in particular face recognition, that tries to prevent spoof attacks.
Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets
This paper aims at learning category-specific 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category.
Geometrically Mappable Image Features
This is achieved by guiding the learning process such that the feature and geometric distances between images are directly proportional.
Self-Calibration Supported Robust Projective Structure-from-Motion
In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints.
Learning Condition Invariant Features for Retrieval-Based Localization from 1M Images
Image features for retrieval-based localization must be invariant to dynamic objects (e. g. cars) as well as seasonal and daytime changes.
Facial Emotion Recognition with Noisy Multi-task Annotations
In our formulation, we exploit a new method to enable the emotion prediction and the joint distribution learning in a unified adversarial learning game.
Soft Contrastive Learning for Visual Localization
Our soft assignment makes a gradual distinction between close and far images in both geometric and feature spaces.
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.
CompositeTasking: Understanding Images by Spatial Composition of Tasks
Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network.
An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain.
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes
In this work, we present an unsupervised monocular framework for dense depth estimation of dynamic scenes, which jointly reconstructs rigid and non-rigid parts without explicitly modelling the camera motion.
Task Switching Network for Multi-Task Learning
Multiple tasks are performed by switching between them, performing one task at a time.
Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes.
Unsupervised Robust Domain Adaptation without Source Data
This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain adaptation without source data.
Learning Target Candidate Association to Keep Track of What Not to Track
To tackle the problem of lacking ground-truth correspondences between distractor objects in visual tracking, we propose a training strategy that combines partial annotations with self-supervision.
Ranked #4 on
Visual Object Tracking
on OTB-2015
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance.
Unsupervised Compound Domain Adaptation for Face Anti-Spoofing
The proposed method successfully adapts to the compound target domain consisting multiple new spoof types.
Rethinking Global Context in Crowd Counting
This paper investigates the role of global context for crowd counting.
GANmut: Learning Interpretable Conditional Space for Gamut of Emotions
On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability.
TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation
In many real-world settings, the target domain task requires a different taxonomy than the one imposed by the source domain.
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
In this work, we study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image.
Ranked #3 on
Lane Detection
on nuScenes
End-to-End Learning of Multi-category 3D Pose and Shape Estimation
We use a Transformer-based architecture to detect the keypoints, as well as to summarize the visual context of the image.
Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points.
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers
We use linear layers with token-consistent stochastic parameters inside the multilayer perceptron blocks, without altering the architecture of the transformer.
Collapse by Conditioning: Training Class-conditional GANs with Limited Data
On the contrary, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability.
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications.
Transforming Model Prediction for Tracking
Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function.
Ranked #20 on
Visual Object Tracking
on LaSOT
(Precision metric)
Spatially Multi-conditional Image Generation
However, multi-conditional image generation is a very challenging problem due to the heterogeneity and the sparsity of the (in practice) available conditioning labels.
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Within the proposed benchmark, we explore some commonly known essentials of standard continual learning.
Gradient Obfuscation Checklist Test Gives a False Sense of Security
It has since become a trend to use these five characteristics as a sufficient test, to determine whether or not gradient obfuscation is the main source of robustness.
Robust RGB-D Fusion for Saliency Detection
In this work, we aim for RGB-D saliency detection that is robust to the low-quality depths which primarily appear in two forms: inaccuracy due to noise and the misalignment to RGB.
Piecewise Planar Hulls for Semi-Supervised Learning of 3D Shape and Pose from 2D Images
On the one hand, the proposed method learns to segment these planar hulls from the labeled data.
Surface Normal Clustering for Implicit Representation of Manhattan Scenes
In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations.
Source-free Depth for Object Pop-out
In this work, we adapt such depth inference models for object segmentation using the objects' "pop-out" prior in 3D.
NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions
Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors.
Online Lane Graph Extraction from Onboard Video
One of the most common and useful representation of such an understanding is done in the form of BEV lane graphs.
Event-Free Moving Object Segmentation from Moving Ego Vehicle
Subsequently, we devise EmoFormer, a novel network able to exploit the event data.
Improving Online Lane Graph Extraction by Object-Lane Clustering
In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph estimates by using 3D object detection outputs.
Prior Based Online Lane Graph Extraction from Single Onboard Camera Image
Thus, online estimation of the lane graph is crucial for widespread and reliable autonomous navigation.
Deformable Neural Radiance Fields using RGB and Event Cameras
In this work, we develop a novel method to model the deformable neural radiance fields using RGB and event cameras.
Model-aware 3D Eye Gaze from Weak and Few-shot Supervisions
In this work, we propose to predict 3D eye gaze from weak supervision of eye semantic segmentation masks and direct supervision of a few 3D gaze vectors.
Lego: Learning to Disentangle and Invert Concepts Beyond Object Appearance in Text-to-Image Diffusion Models
To enable customized content creation based on a few example images of a concept, methods such as Textual Inversion and DreamBooth invert the desired concept and enable synthesizing it in new scenes.
Single-Model and Any-Modality for Video Object Tracking
In practice, most existing RGB trackers learn a single set of parameters to use them across datasets and applications.
Continuous Pose for Monocular Cameras in Neural Implicit Representation
In this paper, we showcase the effectiveness of optimizing monocular camera poses as a continuous function of time.
G-MEMP: Gaze-Enhanced Multimodal Ego-Motion Prediction in Driving
In contrast, we focus on inferring the ego trajectory of a driver's vehicle using their gaze data.
Diffusion-Based Particle-DETR for BEV Perception
For the enhanced safety of AVs, modeling perception uncertainty in BEV is crucial.
Ternary-type Opacity and Hybrid Odometry for RGB-only NeRF-SLAM
To foster this line of research, we also propose a simple yet novel visual odometry scheme that uses a hybrid combination of volumetric and warping-based image renderings.
CaLDiff: Camera Localization in NeRF via Pose Diffusion
This paper studies the problem of localizing cameras in NeRF using a diffusion model for camera pose adjustment.
