Papers Video Classification
“Video Classification” 태그가 달린 논문 495편 · 필터 해제
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study
Deep learning models need large amounts of data for training. In video recognition and classification, significant advances were achieved with the introduction of new large databases. However, the creation of large-datab…
Data AugmentationDeep LearningFacial Expression RecognitionFacial Expression Recognition (FER)+3ViViT: A Video Vision Transformer
We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then en…
Action ClassificationAction RecognitionClassificationGeneral Classification+3Busy-Quiet Video Disentangling for Video Classification
In video data, busy motion details from moving regions are conveyed within a specific frequency bandwidth in the frequency domain. Meanwhile, the rest of the frequencies of video data are encoded with quiet information w…
Action ClassificationAction RecognitionAction Recognition In VideosClassification+2Classifying Video based on Automatic Content Detection Overview
Video classification and analysis is always a popular and challenging field in computer vision. It is more than just simple image classification due to the correlation with respect to the semantic contents of subsequent …
ClassificationGeneral Classificationimage-classificationImage Classification+3Revisiting ResNets: Improved Training and Scaling Strategies
Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the ca…
Action ClassificationDocument Image ClassificationImage ClassificationSemantic Object Interaction Classification+1Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths
Quantizing deep networks with adaptive bit-widths is a promising technique for efficient inference across many devices and resource constraints. In contrast to static methods that repeat the quantization process and trai…
image-classificationImage ClassificationQuantizationTransfer Learning+1A Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection
In modern smart cities, there is a quest for the highest level of integration and automation service. In the surveillance sector, one of the main challenges is to automate the analysis of videos in real-time to identify…
Action RecognitionVideo ClassificationOn the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification
Since their inception, learning techniques under the Reservoir Computing paradigm have shown a great modeling capability for recurrent systems without the computing overheads required for other approaches. Among them, di…
Computational EfficiencyDiagnosticTime SeriesTime Series Analysis+2Distribution Adaptive INT8 Quantization for Training CNNs
Researches have demonstrated that low bit-width (e.g., INT8) quantization can be employed to accelerate the inference process. It makes the gradient quantization very promising since the backward propagation requires app…
image-classificationImage Classificationobject-detectionObject Detection+2Is Space-Time Attention All You Need for Video Understanding?
We present a convolution-free approach to video classification built exclusively on self-attention over space and time. Our method, named "TimeSformer," adapts the standard Transformer architecture to video by enabling s…
Action ClassificationAction RecognitionAllAnomaly Detection+4RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Although 3D Convolutional Neural Networks are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and computational requirements. Compressing suc…
3D Semantic SegmentationSemantic SegmentationStereo MatchingVideo ClassificationPrivacy-Preserving Video Classification with Convolutional Neural Networks
Many video classification applications require access to personal data, thereby posing an invasive security risk to the users' privacy. We propose a privacy-preserving implementation of single-frame method based video cl…
ClassificationEmotion RecognitionGeneral Classificationimage-classification+3Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks
Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually fed into the CNNs in the form of high-…
ClassificationEEGElectroencephalogram (EEG)General Classification+1Piano Skills Assessment
Can a computer determine a piano player's skill level? Is it preferable to base this assessment on visual analysis of the player's performance or should we trust our ears over our eyes? Since current CNNs have difficulty…
Action Quality AssessmentAudio ClassificationMultimodal Deep LearningSkills Assessment+3Self-Supervised Pretraining of 3D Features on any Point-Cloud
Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like 2D object recognition, video classification etc. However, pretraining is not widely used for 3D recog…
Objectobject-detectionObject DetectionObject Recognition+2Reinforcement Learning with Latent Flow
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such information is given as part of the state space o…
Atari Gamescontinuous-controlContinuous ControlMontezuma's Revenge+5Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking
Self-supervised pre-training using so-called "pretext" tasks has recently shown impressive performance across a wide range of tasks. In this work we advance self-supervised learning from permutations, that consists in sh…
General ClassificationSelf-Supervised LearningVideo ClassificationSelf-supervised Temporal Learning
Self-supervised learning (SSL) has shown its powerful ability in discriminative representations for various visual, audio, and video applications. However, most recent works still focus on the different paradigms of spat…
Contrastive LearningRetrievalSelf-Supervised LearningVideo Classification+1CNNs for JPEGs: A Study in Computational Cost
Convolutional neural networks (CNNs) have achieved astonishing advances over the past decade, defining state-of-the-art in several computer vision tasks. CNNs are capable of learning robust representations of the data di…
Edge-computingImage ClassificationVideo ClassificationTemporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates
Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources. This paper aims to exploit audio-visual information …
ClassificationGeneral ClassificationVideo Classification