Papers Video Classification
“Video Classification” 태그가 달린 논문 495편 · 필터 해제
MotionSqueeze: Neural Motion Feature Learning for Video Understanding
Motion plays a crucial role in understanding videos and most state-of-the-art neural models for video classification incorporate motion information typically using optical flows extracted by a separate off-the-shelf meth…
Action ClassificationAction RecognitionVideo ClassificationVideo UnderstandingRegion-based Non-local Operation for Video Classification
Convolutional Neural Networks (CNNs) model long-range dependencies by deeply stacking convolution operations with small window sizes, which makes the optimizations difficult. This paper presents region-based non-local (R…
Action ClassificationAction RecognitionAction Recognition In VideosClassification+33D CNN-PCA: A Deep-Learning-Based Parameterization for Complex Geomodels
Geological parameterization enables the representation of geomodels in terms of a relatively small set of variables. Parameterization is therefore very useful in the context of data assimilation and uncertainty quantific…
Uncertainty QuantificationVideo ClassificationGeneralized Few-Shot Video Classification with Video Retrieval and Feature Generation
Few-shot learning aims to recognize novel classes from a few examples. Although significant progress has been made in the image domain, few-shot video classification is relatively unexplored. We argue that previous metho…
Few-Shot Image ClassificationFew-Shot LearningGeneral ClassificationGeneralized Few-Shot Learning+6NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTube
We present a simple NLP methodology for detecting COVID-19 misinformation videos on YouTube by leveraging user comments. We use transfer learning pre-trained models to generate a multi-label classifier that can categoriz…
MisinformationTransfer LearningVideo ClassificationSmallBigNet: Integrating Core and Contextual Views for Video Classification
Temporal convolution has been widely used for video classification. However, it is performed on spatio-temporal contexts in a limited view, which often weakens its capacity of learning video representation. To alleviate …
ClassificationGeneral ClassificationVideo ClassificationPyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales. PyConv contains a pyramid of kernels, where each level involves different types of filters with var…
Action ClassificationAction Recognitionimage-classificationImage Classification+7Learn to cycle: Time-consistent feature discovery for action recognition
Generalizing over temporal variations is a prerequisite for effective action recognition in videos. Despite significant advances in deep neural networks, it remains a challenge to focus on short-term discriminative motio…
Action ClassificationAction RecognitionAction Recognition In VideosVideo ClassificationVideo Understanding as Machine Translation
With the advent of large-scale multimodal video datasets, especially sequences with audio or transcribed speech, there has been a growing interest in self-supervised learning of video representations. Most prior work for…
Machine TranslationMetric LearningQuestion AnsweringRetrieval+4Non-Local Neural Networks With Grouped Bilinear Attentional Transforms
Modeling spatial or temporal long-range dependency plays a key role in deep neural networks. Conventional dominant solutions include recurrent operations on sequential data or deeply stacking convolutional layers with sm…
Image ClassificationVideo ClassificationOptimizing Temporal Convolutional Network inference on FPGA-based accelerators
Convolutional Neural Networks are extensively used in a wide range of applications, commonly including computer vision tasks like image and video classification, recognition, and segmentation. Recent research results dem…
SchedulingTime Series AnalysisVideo ClassificationVideo Contents Understanding using Deep Neural Networks
We propose a novel application of Transfer Learning to classify video-frame sequences over multiple classes. This is a pre-weighted model that does not require to train a fresh CNN. This representation is achieved with t…
object-detectionObject DetectionTransfer LearningVideo ClassificationPipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
Face anti-spoofing has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks. Addressing the shortage of multi-modal face dat…
Face Anti-SpoofingGeneral ClassificationVideo ClassificationTAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition
Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few ex…
3D Face ReconstructionAction DetectionAction RecognitionClassification+5Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
How can we collect and use a video dataset to further improve spatiotemporal 3D Convolutional Neural Networks (3D CNNs)? In order to positively answer this open question in video recognition, we have conducted an explora…
General ClassificationOpen-Ended Question AnsweringVideo ClassificationVideo RecognitionX3D: Expanding Architectures for Efficient Video Recognition
This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth. Inspired by feature select…
Action Classificationfeature selectionGeneral Classificationimage-classification+3Revisiting Few-shot Activity Detection with Class Similarity Control
Many interesting events in the real world are rare making preannotated machine learning ready videos a rarity in consequence. Thus, temporal activity detection models that are able to learn from a few examples are desira…
Action DetectionActivity DetectionVideo ClassificationConvolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature. Also, they have the advantage of low computation cost in contrast to con…
Activity Recognition In VideosEvent data classificationImage ClassificationVideo ClassificationMotion-Excited Sampler: Video Adversarial Attack with Sparked Prior
Deep neural networks are known to be susceptible to adversarial noise, which are tiny and imperceptible perturbations. Most of previous work on adversarial attack mainly focus on image models, while the vulnerability of …
Adversarial AttackVideo ClassificationOn Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant. We show that CNNs can and will exploit the absolute spatial location by learning filters that respond e…
General Classificationimage-classificationImage ClassificationInductive Bias+4