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Found 20 papers, 12 papers with code
3D Human Scan With A Moving Event Camera
The experimental results show that the proposed method outperforms conventional frame-based methods in the estimation accuracy of both pose and body mesh.
PCT: Perspective Cue Training Framework for Multi-Camera BEV Segmentation
In this work, we address these challenges by leveraging the abundance of unlabeled data available.
TAG: Guidance-free Open-Vocabulary Semantic Segmentation
Unsupervised and open-vocabulary segmentation, proposed to tackle these issues, faces challenges, including the inability to assign specific class labels to clusters and the necessity of user-provided text queries for guidance.
MaskDiffusion: Exploiting Pre-trained Diffusion Models for Semantic Segmentation
Semantic segmentation is essential in computer vision for various applications, yet traditional approaches face significant challenges, including the high cost of annotation and extensive training for supervised learning.
Event-based Background-Oriented Schlieren
Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding.
Boosting Semantic Segmentation with Semantic Boundaries
Motivated by the recent development in improving semantic segmentation by incorporating boundaries as auxiliary tasks, we propose a multi-task framework that uses semantic boundary detection (SBD) as an auxiliary task.
FindView: Precise Target View Localization Task for Look Around Agents
With the increase in demands for service robots and automated inspection, agents need to localize in its surrounding environment to achieve more natural communication with humans by shared contexts.
Listening Human Behavior: 3D Human Pose Estimation With Acoustic Signals
Aiming to capture subtle sound changes to reveal detailed pose information, we explicitly extract phase features from the acoustic signals together with typical spectrum features and feed them into our human pose estimation network.
Fast Event-based Optical Flow Estimation by Triplet Matching
Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.).
A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework
We hope our work opens the door for future applications that unlocks the advantages of event cameras.
Document Shadow Removal with Foreground Detection Learning From Fully Synthetic Images
In this paper, we present a large-scale and diverse dataset called fully synthetic document shadow removal dataset (FSDSRD) that does not require capturing documents.
Secrets of Event-Based Optical Flow
Event cameras respond to scene dynamics and offer advantages to estimate motion.
Event Collapse in Contrast Maximization Frameworks
Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation.
Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation
To improve the properties of a 360-degree image on an output image, we also propose WS-perceptual loss and circular inference.
Alleviating Over-segmentation Errors by Detecting Action Boundaries
Our model architecture consists of a long-term feature extractor and two branches: the Action Segmentation Branch (ASB) and the Boundary Regression Branch (BRB).
Ranked #9 on
Action Segmentation
on GTEA
Retrieving and Highlighting Action with Spatiotemporal Reference
In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods.
Fast Soft Color Segmentation
We address the problem of soft color segmentation, defined as decomposing a given image into several RGBA layers, each containing only homogeneous color regions.
Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB
In this paper, we propose a novel approach for traffic accident anticipation through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a large-scale self-annotated incident database for anticipation.
Dominant Codewords Selection with Topic Model for Action Recognition
Latent Dirichlet allocation (LDA) is used to develop approximations of human motion primitives; these are mid-level representations, and they adaptively integrate dominant vectors when classifying human activities.
Depth Image Enhancement Using Local Tangent Plane Approximations
Our method is composed of two steps, a calculation of the local tangents and surface reconstruction.
