Papers Video Classification
“Video Classification” 태그가 달린 논문 495편 · 필터 해제
Smoothed Gaussian Mixture Models for Video Classification and Recommendation
Cluster-and-aggregate techniques such as Vector of Locally Aggregated Descriptors (VLAD), and their end-to-end discriminatively trained equivalents like NetVLAD have recently been popular for video classification and act…
Action RecognitionClassificationGeneral ClassificationVideo ClassificationVideoMix: Rethinking Data Augmentation for Video Classification
State-of-the-art video action classifiers often suffer from overfitting. They tend to be biased towards specific objects and scene cues, rather than the foreground action content, leading to sub-optimal generalization pe…
Action LocalizationAction RecognitionClassificationData Augmentation+3Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models
Committee-based models (ensembles or cascades) construct models by combining existing pre-trained ones. While ensembles and cascades are well-known techniques that were proposed before deep learning, they are not conside…
General Classificationimage-classificationImage ClassificationNeural Architecture Search+2Is normalization indispensable for training deep neural network?
Normalization operations are widely used to train deep neural networks, and they can improve both convergence and generalization in most tasks. The theories for normalization's effectiveness and new forms of normalizatio…
General Classificationimage-classificationImage ClassificationMachine Translation+4Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video Classification
Video classification researches that have recently attracted attention are the fields of temporal modeling and 3D efficient architecture. However, the temporal modeling methods are not efficient or the 3D efficient archi…
3D ArchitectureAction RecognitionGeneral ClassificationVideo Classificationt-EVA: Time-Efficient t-SNE Video Annotation
Video understanding has received more attention in the past few years due to the availability of several large-scale video datasets. However, annotating large-scale video datasets are cost-intensive. In this work, we pro…
Dimensionality ReductionVideo ClassificationVideo UnderstandingDeep Multimodality Learning for UAV Video Aesthetic Quality Assessment
Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the aesthetics of aerial videos that can provide valuable information for improving the aesthetic…
Video ClassificationRANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Although 3D Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and computational requirements. Compress…
3D Semantic SegmentationSemantic SegmentationVideo ClassificationAttention-Aware Noisy Label Learning for Image Classification
Deep convolutional neural networks (CNNs) learned on large-scale labeled samples have achieved remarkable progress in computer vision, such as image/video classification. The cheapest way to obtain a large body of labele…
ClassificationGeneral Classificationimage-classificationImage Classification+1Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
Graph Neural Networks are perfectly suited to capture latent interactions between various entities in the spatio-temporal domain (e.g. videos). However, when an explicit structure is not available, it is not obvious what…
Inductive BiasObjectVideo ClassificationMulti-Label Activity Recognition using Activity-specific Features and Activity Correlations
Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video. Most recent activity recognition networks focus on single-activities, that…
Activity RecognitionVideo ClassificationDefending Against Multiple and Unforeseen Adversarial Videos
Adversarial robustness of deep neural networks has been actively investigated. However, most existing defense approaches are limited to a specific type of adversarial perturbations. Specifically, they often fail to offer…
Adversarial RobustnessGeneral ClassificationVideo ClassificationVideo RecognitionActive Contrastive Learning of Audio-Visual Video Representations
Contrastive learning has been shown to produce generalizable representations of audio and visual data by maximizing the lower bound on the mutual information (MI) between different views of an instance. However, obtainin…
Contrastive LearningRepresentation LearningVideo ClassificationMaking a Case for 3D Convolutions for Object Segmentation in Videos
The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of in…
DecoderSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+5Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation
This paper proposes a novel model for video generation and especially makes the attempt to deal with the problem of video generation from text descriptions, i.e., synthesizing realistic videos conditioned on given texts.…
Generative Adversarial NetworkVideo ClassificationVideo GenerationVideo PredictionActor-Action Video Classification CSC 249/449 Spring 2020 Challenge Report
This technical report summarizes submissions and compiles from Actor-Action video classification challenge held as a final project in CSC 249/449 Machine Vision course (Spring 2020) at University of Rochester
General ClassificationVideo ClassificationSelf-Supervised Multi-Task Procedure Learning from Instructional Videos
We address the problem of unsupervised procedure learning from instructional videos of multiple tasks using Deep Neural Networks (DNNs). Unlike existing works, we assume that training videos come from multiple tasks with…
Procedure LearningVideo ClassificationApproximated Bilinear Modules for Temporal Modeling
We consider two less-emphasized temporal properties of video: 1. Temporal cues are fine-grained; 2. Temporal modeling needs reasoning. To tackle both problems at once, we exploit approximated bilinear modules (ABMs) for …
Action RecognitionVideo ClassificationAttentionNAS: Spatiotemporal Attention Cell Search for Video Classification
Convolutional operations have two limitations: (1) do not explicitly model where to focus as the same filter is applied to all the positions, and (2) are unsuitable for modeling long-range dependencies as they only opera…
ClassificationGeneral ClassificationVideo ClassificationUncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos
Despite the recent advances in video classification, progress in spatio-temporal action recognition has lagged behind. A major contributing factor has been the prohibitive cost of annotating videos frame-by-frame. In thi…
Action DetectionAction RecognitionMultiple Instance LearningSpatio-temporal Action Recognition+1