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Found 11 papers, 10 papers with code
Depth-Aware Generative Adversarial Network for Talking Head Video Generation
In a more dense way, the depth is also utilized to learn 3D-aware cross-modal (i. e. appearance and depth) attention to guide the generation of motion fields for warping source image representations.
DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation
In this work, firstly, we present a novel self-supervised method for learning dense 3D facial geometry (ie, depth) from face videos, without requiring camera parameters and 3D geometry annotations in training.
Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head video Generation
Talking head video generation aims to animate a human face in a still image with dynamic poses and expressions using motion information derived from a target-driving video, while maintaining the person's identity in the source image.
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations.
Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization
In this work, we argue that the features extracted from the pretrained extractor, e. g., I3D, are not the WS-TALtask-specific features, thus the feature re-calibration is needed for reducing the task-irrelevant information redundancy.
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
Cross-modal Consensus Network forWeakly Supervised Temporal Action Localization
In this work, we argue that the features extracted from the pretrained extractor, e. g., I3D, are not the WS-TALtask-specific features, thus the feature re-calibration is needed for reducing the task-irrelevant information redundancy.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Hybrid Dynamic-static Context-aware Attention Network for Action Assessment in Long Videos
However, most existing works focus only on video dynamic information (i. e., motion information) but ignore the specific postures that an athlete is performing in a video, which is important for action assessment in long videos.
Ranked #2 on
Action Quality Assessment
on Rhythmic Gymnastic
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection
Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern.
Learning to Learn Relation for Important People Detection in Still Images
In this work, we propose a deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling and feature learning.
MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection
We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but without expensive supervision of manually annotating highlight segments.
Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning
Accordingly, we first exclude these surely non-existent categories by a complementary learning loss.
