Papers Video Classification
“Video Classification” 태그가 달린 논문 495편 · 필터 해제
Token Shift Transformer for Video Classification
Transformer achieves remarkable successes in understanding 1 and 2-dimensional signals (e.g., NLP and Image Content Understanding). As a potential alternative to convolutional neural networks, it shares merits of strong …
ClassificationComputational EfficiencyVideo ClassificationVideo UnderstandingTemporal Alignment Prediction for Few-Shot Video Classification
The goal of few-shot video classification is to learn a classification model with good generalization ability when trained with only a few labeled videos. However, it is difficult to learn discriminative feature represen…
ClassificationPredictionVideo ClassificationFine-Grained AutoAugmentation for Multi-Label Classification
Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better generalization than hand-crafted ones. H…
ClassificationData AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Aligning Correlation Information for Domain Adaptation in Action Recognition
Domain adaptation (DA) approaches address domain shift and enable networks to be applied to different scenarios. Although various image DA approaches have been proposed in recent years, there is limited research towards …
Action RecognitionDomain AdaptationVideo ClassificationWhen Video Classification Meets Incremental Classes
With the rapid development of social media, tremendous videos with new classes are generated daily, which raise an urgent demand for video classification methods that can continuously update new classes while maintaining…
Classificationclass-incremental learningClass Incremental LearningIncremental Learning+2Attention Bottlenecks for Multimodal Fusion
Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio. Machine perception models, in stark contrast, are typically modality-specific and…
Action ClassificationAction RecognitionAudio ClassificationVideo ClassificationVideo Swin Transformer
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built o…
Action ClassificationAction RecognitionGeneral ClassificationInductive Bias+3TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
Recently, few-shot learning has received increasing interest. Existing efforts have been focused on image classification, with very few attempts dedicated to the more challenging few-shot video classification problem. Th…
Action ClassificationClassificationFew-Shot Learningimage-classification+2Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces
The goal of out-of-distribution (OOD) detection is to handle the situations where the test samples are drawn from a different distribution than the training data. In this paper, we argue that OOD samples can be detec…
Bayesian InferenceOut-of-Distribution DetectionOut of Distribution (OOD) DetectionVideo ClassificationGraph-Based High-Order Relation Modeling for Long-Term Action Recognition
Long-term actions involve many important visual concepts, e.g., objects, motions, and sub-actions, and there are various relations among these concepts, which we call basic relations. These basic relations will joint…
Action RecognitionLong-video Activity RecognitionRelationVideo Classification+1Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
Instance-level contrastive learning techniques, which rely on data augmentation and a contrastive loss function, have found great success in the domain of visual representation learning. They are not suitable for exploit…
Action RecognitionAction Recognition In VideosContrastive LearningData Augmentation+9CT-Net: Channel Tensorization Network for Video Classification
3D convolution is powerful for video classification but often computationally expensive, recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions. Unfortunately, most approaches fail to…
Action ClassificationAction RecognitionClassificationVideo ClassificationA Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP
There has been an increasing interest in spiking neural networks in recent years. SNNs are seen as hypothetical solutions for the bottlenecks of ANNs in pattern recognition, such as energy efficiency. But current methods…
Action RecognitionSpatio-temporal Action RecognitionVideo ClassificationVideo UnderstandingSSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation
Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised …
Action ParsingAction SegmentationSelf-Supervised LearningTemporal Action Segmentation+1A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos
The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants. Developing computer-based methods for assessing infant movements in videos is pivotal for improved c…
Video ClassificationIntFormer: Predicting pedestrian intention with the aid of the Transformer architecture
Understanding pedestrian crossing behavior is an essential goal in intelligent vehicle development, leading to an improvement in their security and traffic flow. In this paper, we developed a method called IntFormer. It …
Video ClassificationLearning Implicit Temporal Alignment for Few-shot Video Classification
Few-shot video classification aims to learn new video categories with only a few labeled examples, alleviating the burden of costly annotation in real-world applications. However, it is particularly challenging to learn …
Action Recognition In VideosClassificationFew-Shot LearningVideo ClassificationHome Action Genome: Cooperative Compositional Action Understanding
Existing research on action recognition treats activities as monolithic events occurring in videos. Recently, the benefits of formulating actions as a combination of atomic-actions have shown promise in improving action …
Action RecognitionAction UnderstandingFew-Shot action recognitionFew Shot Action Recognition+2VidTr: Video Transformer Without Convolutions
We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal information via stacked attentions and provid…
Action ClassificationAction RecognitionVideo ClassificationBeyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories
The standard way of training video models entails sampling at each iteration a single clip from a video and optimizing the clip prediction with respect to the video-level label. We argue that a single clip may not have e…
Action DetectionAction RecognitionVideo Classification