https://github.com/mzolfaghari/ECO-efficient-video-understanding
공식 코드ECO: Efficient Convolutional Network for Online Video Understanding
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Video Classification is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ECO: Efficient Convolutional Network for Online Video Understanding
Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos
A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos
Home Action Genome: Cooperative Compositional Action Understanding
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications
Convex Combination Consistency between Neighbors for Weakly-supervised Action Localization
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
Making a Case for 3D Convolutions for Object Segmentation in Videos
Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification
Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M dataset
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning
SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili
A Multimodal Handover Failure Detection Dataset and Baselines
Towards a Robust Framework for Multimodal Hate Detection: A Study on Video vs. Image-based Content
Malicious or Benign? Towards Effective Content Moderation for Children's Videos
A Simple Video Segmenter by Tracking Objects Along Axial Trajectories
A Simple Video Segmenter by Tracking Objects Along Axial Trajectories
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
Revisiting ResNets: Improved Training and Scaling Strategies
VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
Learning Implicit Temporal Alignment for Few-shot Video Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.