Search Results for author:
Found 6 papers, 1 papers with code
On the Efficacy of Text-Based Input Modalities for Action Anticipation
Compared to existing methods, MAT has the advantage of learning additional environmental context from two kinds of text inputs: action descriptions during the pre-training stage, and the text inputs for detected objects and actions during modality feature fusion.
Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition
However, a major component of successful contrastive learning is the selection of good positive and negative samples.
Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition
In this work, we propose a multi-modal framework that learns to effectively combine features from RGB Video and IMU sensors, and show its robustness for MMAct and UTD-MHAD datasets.
End-to-End Multimodal Representation Learning for Video Dialog
Video-based dialog task is a challenging multimodal learning task that has received increasing attention over the past few years with state-of-the-art obtaining new performance records.
Video based Object 6D Pose Estimation using Transformers
We introduce a Transformer based 6D Object Pose Estimation framework VideoPose, comprising an end-to-end attention based modelling architecture, that attends to previous frames in order to estimate accurate 6D Object Poses in videos.
VideoPose: Estimating 6D object pose from videos
We introduce a simple yet effective algorithm that uses convolutional neural networks to directly estimate object poses from videos.
