CLIP(对比语言-图像预训练)是由OpenAI于2021年提出的多模态模型,通过对比学习实现图像与文本的跨模态语义对齐,成为计算机视觉与自然语言处理领域的里程碑。以下是其核心原理、技术特点及应用场景的详细解析:…
Contrastive Language-Image Pre-training(CLIP)[1] 技术由OpenAI团队在ICML2021 提出,这是一个非常符合 Open AI 大力出奇迹的工作风格。根据谢赛宁教授在智源大会上的分享 [2],目前大多数多模态大模型都采用了 CLIP 预训练的视觉编码器,足见 CLIP 的广泛影响力。本篇博文对 CLIP 的核心技术原理进行梳理和总结。
temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model. We evaluate several variants of our model that exploit different visual features on a standard set of YouTube videos and two movie description datasets (M-VAD and ...
Self-Supervision within each modality 这里主要是使用原图与增广后(例如crop)的图像送入Image encoder计算相似度,同时增广图像的一路停止梯度反传。这里作者还使用了一个两层的MLP,用来提高Image encoder的表达质量,结构如下: 对于文本模态,作者采用了与Bert相同的自监督策略,在每个sequence中随机选择了15%的token进行...
We introduce a novel language-image Contrastive Learning method with an Efficient large language model and prompt Fine-Tuning (CLEFT) that harnesses the strengths of the extensive pre-trained language and visual models. Furthermore, we present an efficient strategy for learning context-based prompts ...
CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities...
model.encode_image(image: Tensor) Given a batch of images, returns the image features encoded by the vision portion of the CLIP model. model.encode_text(text: Tensor) Given a batch of text tokens, returns the text features encoded by the language portion of the CLIP model. model(image: ...
Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training Haoxuan You, Luowei Zhou, Bin Xiao, Noel Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, Lu Yuan European Conference on Computer Vision (ECCV 2022)|October 2022 ...
Zeng, Yihan, et al. "CLIP2: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. 作者单位:华为诺亚方舟实验室 香港科技大学 香港中文大学 中山大学 ...
Recent advancements in Contrastive Language-Image Pre-training (CLIP) have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size ...