Search Results for author:
Found 14 papers, 4 papers with code
FineBio: A Fine-Grained Video Dataset of Biological Experiments with Hierarchical Annotation
The dataset consists of multi-view videos of 32 participants performing mock biological experiments with a total duration of 14. 5 hours.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos
We propose a novel benchmark for cross-view knowledge transfer of dense video captioning, adapting models from web instructional videos with exocentric views to an egocentric view.
Seeking Flat Minima with Mean Teacher on Semi- and Weakly-Supervised Domain Generalization for Object Detection
In contrast to the conventional domain generalization for object detection that requires labeled data from multiple domains, SS-DGOD and WS-DGOD require labeled data only from one domain and unlabeled or weakly-labeled data from multiple domains for training.
Proposal-based Temporal Action Localization with Point-level Supervision
Point-level supervised temporal action localization (PTAL) aims at recognizing and localizing actions in untrimmed videos where only a single point (frame) within every action instance is annotated in training data.
Fine-grained Affordance Annotation for Egocentric Hand-Object Interaction Videos
Object affordance is an important concept in hand-object interaction, providing information on action possibilities based on human motor capacity and objects' physical property thus benefiting tasks such as action anticipation and robot imitation learning.
Precise Affordance Annotation for Egocentric Action Video Datasets
Object affordance is an important concept in human-object interaction, providing information on action possibilities based on human motor capacity and objects' physical property thus benefiting tasks such as action anticipation and robot imitation learning.
Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey
In this survey, we present a systematic review of 3D hand pose estimation from the perspective of efficient annotation and learning.
Domain Adaptive Hand Keypoint and Pixel Localization in the Wild
We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e. g., outdoors) when we only have labeled images taken under very different conditions (e. g., indoors).
Background Mixup Data Augmentation for Hand and Object-in-Contact Detection
Detecting the positions of human hands and objects-in-contact (hand-object detection) in each video frame is vital for understanding human activities from videos.
Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks
To realize consistent colorization, we propose here a semi-automatic colorization method based on generative adversarial networks (GAN); the method learns the painting style of a specific comic from small amount of training data.
PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing
However, the applications of deep reinforcement learning (RL) for image processing are still limited.
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing
This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing.
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Can we detect common objects in a variety of image domains without instance-level annotations?
Ranked #5 on
Weakly Supervised Object Detection
on Watercolor2k
(using extra training data)
