论文解读 X-CLIP : Expanding Language-Image Pretrained Models for General Video Recognition - 代码天地

论文解读 X-CLIP : Expanding Language-Image Pretrained Models for General Video Recognition

企业开发 2022-09-05 01:11:49 阅读次数: 0

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转载自blog.csdn.net/flyingluohaipeng/article/details/126648783

论文解读 X-CLIP : Expanding Language-Image Pretrained Models for General Video Recognition

多模态——CLIP:Contrastive Language-Image Pre-training解读

论文解读：SlowFast Networks for Video Recognition

CLIP对比图文预训练（Contrastive Language-Image Pretraining）论文阅读笔记

【论文分享】InternVideo: General Video Foundation Models via Generative and Discriminative Learning

12【mmaction2 行为识别商用级别】X3D复现 demo实现检测自己的视频 Expanding Architecturesfor Efficient Video Recognition

[文献阅读]—Improving the Lexical Ability of Pretrained Language Models for Unsupervised NMT

论文解读：Large Language Models as Analogical Reasoners

A Survey on Multimodal Large Language Models论文解读

【论文精读】Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

论文解读：ViT | AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE

【论文精读】GPT-NER: Named Entity Recognition via Large Language Models

Grounded Language-Image Pre-training论文笔记

论文笔记：Video-based Sign Language Recognition without Temporal Segmentation

Compressed Video Action Recognition论文笔记

论文笔记：SlowFast Networks for Video Recognition

SlowFast Networks for Video Recognition论文精读

论文阅读 [TPAMI-2022] Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition

【CVPR 2023】解读LFDM：Conditional Image-to-Video Generation with Latent Flow Diffusion Models

【论文笔记】BLIP-2: Bootstrapping Language-Image Pretrainingwith Frozen Image Encoders and Large Language

《论文阅读》SetGNER:General Named Entity Recognition as Entity Set Generation

《End-to-end Video-level Representation Learning for Action Recognition》论文解读之DTPP

Optical Flow Guided Feature A Fast and Robust Motion Representation for Video Action Recognition论文解读

GPT2.0 Language Models are Unsupervised Multitask Learners 论文解读

《Toolformer: Language Models Can Teach Themselves to Use Tools》论文解读

【论文笔记】BLIP: Bootstrapping Language-Image Pre-training forUnified Vision-Language Understanding and

Deep Residual Learning for Image Recognition （ResNet）论文详细解读

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