Papers Action Classification
“Action Classification” 태그가 달린 논문 485편 · 필터 해제
Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks
As participants of the MediaEval 2022 Sport Task, we propose a two-stream network approach for the classification and detection of table tennis strokes. Each stream is a succession of 3D Convolutional Neural Network (CNN…
Action ClassificationAction DetectionFine-Grained Action DetectionStroke ClassificationBaseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms
This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2022 benchmark. This task proposes two subtasks: stroke classification from trimmed videos, and stroke detection from untri…
Action ClassificationAction DetectionStroke ClassificationAIM: Adapting Image Models for Efficient Video Action Recognition
Recent vision transformer based video models mostly follow the ``image pre-training then finetuning" paradigm and have achieved great success on multiple video benchmarks. However, full finetuning such a video model coul…
Action ClassificationAction RecognitionTemporal Action LocalizationVideo UnderstandingDeep Dependency Networks for Multi-Label Classification
We propose a simple approach which combines the strengths of probabilistic graphical models and deep learning architectures for solving the multi-label classification task, focusing specifically on image and video data. …
Action ClassificationClassificationimage-classificationImage Classification+3mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
Recent years have witnessed a big convergence of language, vision, and multi-modal pretraining. In this work, we present mPLUG-2, a new unified paradigm with modularized design for multi-modal pretraining, which can bene…
Action ClassificationImage ClassificationImage RetrievalTGIF-Frame+7Actor-Director-Critic: A Novel Deep Reinforcement Learning Framework
In this paper, we propose actor-director-critic, a new framework for deep reinforcement learning. Compared with the actor-critic framework, the director role is added, and action classification and action evaluation are …
Action ClassificationDecision MakingDeep Reinforcement LearningMuJoCo+3HierVL: Learning Hierarchical Video-Language Embeddings
Video-language embeddings are a promising avenue for injecting semantics into visual representations, but existing methods capture only short-term associations between seconds-long video clips and their accompanying text…
Action ClassificationAction RecognitionLong Term Action AnticipationLong Term Anticipation+1Hierarchical Explanations for Video Action Recognition
To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for the classification. However, existing methods often ignore the hierarchical relationship…
Action ClassificationAction RecognitionExplainable artificial intelligenceTemporal Action LocalizationSkeleTR: Towards Skeleton-based Action Recognition in the Wild
We present SkeleTR, a new framework for skeleton-based action recognition. In contrast to prior work, which focuses mainly on controlled environments, we target in-the-wild scenarios that typically involve a variable…
Action ClassificationAction DetectionAction RecognitionActivity Recognition+3ReGen: A good Generative Zero-Shot Video Classifier Should be Rewarded
This paper sets out to solve the following problem: How can we turn a generative video captioning model into an open-world video/action classification model? Video captioning models can naturally produce open-ended f…
Action ClassificationAction RecognitionDescriptivereinforcement-learning+2Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models
Vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for …
Action ClassificationAction RecognitionAttributeVideo Recognition+1Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning
Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations from scratch through reconstructing low-…
Action ClassificationAction RecognitionRepresentation LearningSelf-Supervised Action RecognitionLearning Video Representations from Large Language Models
We introduce LaViLa, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual input, and finetune them to create auto…
Action ClassificationAction RecognitionDiversityEgocentric Activity Recognition+2Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning
We present a simple approach which can turn a ViT encoder into an efficient video model, which can seamlessly work with both image and video inputs. By sparsely sampling the inputs, the model is able to do training and i…
Action ClassificationAction RecognitionAction Recognition In VideosInternVideo: General Video Foundation Models via Generative and Discriminative Learning
The foundation models have recently shown excellent performance on a variety of downstream tasks in computer vision. However, most existing vision foundation models simply focus on image-level pretraining and adpation, w…
Action ClassificationAction RecognitionContrastive LearningOpen Set Action Recognition+8Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations
Previous work on action representation learning focused on global representations for short video clips. In contrast, many practical applications, such as video alignment, strongly demand learning the intensive represent…
Action ClassificationContrastive LearningRepresentation LearningRetrieval+2Spatio-Temporal Crop Aggregation for Video Representation Learning
We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time. Our model builds long-range video features by learni…
Action ClassificationDimensionality ReductionRepresentation LearningTransfer Learning+1Post-Processing Temporal Action Detection
Existing Temporal Action Detection (TAD) methods typically take a pre-processing step in converting an input varying-length video into a fixed-length snippet representation sequence, before temporal boundary estimation a…
Action ClassificationAction DetectionQuantizationXKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning
We present XKD, a novel self-supervised framework to learn meaningful representations from unlabelled videos. XKD is trained with two pseudo objectives. First, masked data reconstruction is performed to learn modality-sp…
Action ClassificationClassificationKnowledge DistillationRepresentation Learning+4Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies
Due to the rapid temporal and fine-grained nature of complex human assembly atomic actions, traditional action segmentation approaches requiring the spatial (and often temporal) down sampling of video frames often loose …
Action ClassificationAction RecognitionAction SegmentationClassification+2