Virtual Multi-Modality Self-Supervised Foreground Matting for Human-Object Interaction
Most existing human matting algorithms tried to separate pure human-only foreground from the background. In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB image. The VMFM method requires no additional inputs, e.g. trimap or known background. We reformulate foreground matting as a self-supervised multi-modality problem: factor each input image into estimated depth map, segmentation mask, and interaction heatmap using three auto-encoders. In order to fully utilize the characteristics of each modality, we first train a dual encoder-to-decoder network to estimate the same alpha matte. Then we introduce a self-supervised method: Complementary Learning(CL) to predict deviation probability map and exchange reliable gradients across modalities without label. We conducted extensive experiments to analyze the effectiveness of each modality and the significance of different components in complementary learning. We demonstrate that our model outperforms the state-of-the-art methods.
Code (1)
Tasks
DecoderHuman-Object Interaction DetectionImage MattingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakly and self-supervised class-agnostic mot…
Self-Supervised LearningAutonomous DrivingScene ParsingPoint CloudsCOSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
Vision-Language Models (VLMs) trained with contrastive loss have achieved significant advancements in various vision and language tasks. However, the global nature of the contrastive loss makes VLMs focus predominantly o…
Self-Supervised LearningSemantic SegmentationUnsupervised Semantic Segmentation with Language-image Pre-trainingZero-Shot Cross-Modal Retrieval+1Visually Guided Self Supervised Learning of Speech Representations
Self supervised representation learning has recently attracted a lot of research interest for both the audio and visual modalities. However, most works typically focus on a particular modality or feature alone and there …
Emotion RecognitionRepresentation LearningSelf-Supervised LearningSpeech Emotion Recognition+2MOST: Multiple Object localization with Self-supervised Transformers for object discovery
We tackle the challenging task of unsupervised object localization in this work. Recently, transformers trained with self-supervised learning have been shown to exhibit object localization properties without being traine…
Objectobject-detectionObject DetectionObject Discovery+5Ensemble Foreground Management for Unsupervised Object Discovery
Unsupervised object discovery (UOD) aims to detect and segment objects in 2D images without handcrafted annotations. Recent progress in self-supervised representation learning has led to some success in UOD algorithms. H…
Representation LearningInstance SegmentationSaliency Detection