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Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling
The main features of jayleicn/clipbert are: Multimodal Pretraining, Video Retrieval Models, Video Understanding.
Open-source alternatives to jayleicn/clipbert include: arrowluo/clip4clip — An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval". cryhanfang/clip2video — The implementation of paper CLIP2Video: Mastering Video-Text Retrieval via Image CLIP. google-research/big_vision — This project is a research framework and toolkit designed for training large-scale vision transformers and multimodal… evolvinglmms-lab/otter — Otter is a framework and toolkit for the pretraining, fine-tuning, and evaluation of vision-language models. It… facebookresearch/slowfast — SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset… llava-vl/llava-next — LLaVA-NeXT is a multimodal large language model framework and training toolkit designed to process interleaved images…
The implementation of paper CLIP2Video: Mastering Video-Text Retrieval via Image CLIP.
An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"
Otter is a framework and toolkit for the pretraining, fine-tuning, and evaluation of vision-language models. It provides a pipeline for training large language models to process high-resolution images and video frames, integrating visual encoders with textual token spaces. The system is designed for multi-visual input processing, allowing models to interpret multiple images or video sequences within a single prompt. It supports multi-round conversation management to maintain context across interactions for detailed scene comprehension and visual reasoning. The framework covers a full develop
SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset for video action recognition, enabling the training and evaluation of models designed to classify complex activities and objects within video sequences. The framework is distinguished by its use of dual-pathway spatiotemporal sampling to capture both slow and fast motions. It supports self-supervised video learning for pre-training models on unlabeled data and employs multigrid spatiotemporal training to optimize learning across multiple spatial and temporal resolutions. The