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Found 15 papers, 10 papers with code
Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition
Acquiring spatio-temporal states of an action is the most crucial step for action classification.
Ranked #1 on
Hand Gesture Recognition
on ChaLean test
Convolutional Neural Networks with Layer Reuse
In this paper, we propose a CNN architecture, Layer Reuse Network (LruNet), where the convolutional layers are used repeatedly without the need of introducing new layers to get a better performance.
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
We evaluate our architecture on two publicly available datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures.
Ranked #1 on
Hand Gesture Recognition
on EgoGesture
Resource Efficient 3D Convolutional Neural Networks
Recently, convolutional neural networks with 3D kernels (3D CNNs) have been very popular in computer vision community as a result of their superior ability of extracting spatio-temporal features within video frames compared to 2D CNNs.
Ranked #2 on
Action Recognition In Videos
on UCF101
Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs
The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems.
Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos
Understanding actions and gestures in video streams requires temporal reasoning of the spatial content from different time instants, i. e., spatiotemporal (ST) modeling.
Ranked #117 on
Action Recognition
on Something-Something V2
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
YOWO is a single-stage architecture with two branches to extract temporal and spatial information concurrently and predict bounding boxes and action probabilities directly from video clips in one evaluation.
Ranked #1 on
Action Recognition In Videos
on AVA v2.2
Unsupervised Monocular Depth Prediction for Indoor Continuous Video Streams
This paper studies unsupervised monocular depth prediction problem.
Deep Attention Based Semi-Supervised 2D-Pose Estimation for Surgical Instruments
To this end, a lightweight network architecture is introduced and mean teacher, virtual adversarial training and pseudo-labeling algorithms are evaluated on 2D-pose estimation for surgical instruments.
DriverMHG: A Multi-Modal Dataset for Dynamic Recognition of Driver Micro Hand Gestures and a Real-Time Recognition Framework
In this work, we propose an HCI system for dynamic recognition of driver micro hand gestures, which can have a crucial impact in automotive sector especially for safety related issues.
Dissected 3D CNNs: Temporal Skip Connections for Efficient Online Video Processing
Convolutional Neural Networks with 3D kernels (3D-CNNs) currently achieve state-of-the-art results in video recognition tasks due to their supremacy in extracting spatiotemporal features within video frames.
Driver Anomaly Detection: A Dataset and Contrastive Learning Approach
For this task, we introduce a new video-based benchmark, the Driver Anomaly Detection (DAD) dataset, which contains normal driving videos together with a set of anomalous actions in its training set.
TRAT: Tracking by Attention Using Spatio-Temporal Features
Although motion provides distinctive and complementary information especially for fast moving objects, most of the recent tracking architectures primarily focus on the objects' appearance information.
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition
In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation by using the polyhedral conic classification function.
How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild
Successful active speaker detection requires a three-stage pipeline: (i) audio-visual encoding for all speakers in the clip, (ii) inter-speaker relation modeling between a reference speaker and the background speakers within each frame, and (iii) temporal modeling for the reference speaker.
