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 of GPT-2 and 3.

May 6, 2023 · In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations. Firstly, we use scene graphs to guide the construction of semantic negative examples, which results in an increased emphasis on learning structured representations.

Feb 24, 2024 · CLIP which stands for Contrastive Language-Image Pre-training, is an efficient method of learning from natural language supervision and was introduced in 2021 in the paper Learning...

CLIP (With Haiku + Jax!) CLIP-Event: Connecting Text and Images with Event Structures ; How Much Can CLIP Benefit Vision-and-Language Tasks? CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning ; CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory

In this paper, we propose Structure-CLIP, a novel ap-proach that leverages Scene Graph Knowledge (SGK) to enhance multi-modal structured representations. Firstly, in contrast to the random swap method in NegCLIP, we utilize SGK to construct word swaps that better match the under-lying intent.

Dec 31, 2022 · CLIP treats an image as a sequence of non-overlapping patches, with each patch being a visual token (similar to text token or word in NLP).

Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations This paper introduces an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge to enhance multi-modal structured representations.

Jan 11, 2024 · 在本文中，作者提出了Structure-CLIP，一种利用场景图知识 (SGK)来增强多模态结构化表示的新方法。 首先，与随机交换方法相比，作者利用SGK来构建更符合潜在意图的单词交换。 其次，作者提出了一种知识增强编码器 (KEE)，利用SGK提取基本结构信息。 通过在输入层引入结构化知识，所提出的KEE可以进一步增强结构化表示的能力。 实验结果显示了Structure-CLIP的SOTA性能及其组成部分的有效性。 此外，作者对MSCOCO进行了跨模态检索评估， …

Addressing these issues, our idea is to extract cross-modality features by CLIP from text and image data naturally related to 3D point clouds. Cross-modality features are used to train a ro-bust 3D scene graph (3DSG) feature extractor. Specifically, we propose a novel Cross-Modality Contrastive Learning 3DSGG (CCL-3DSGG) method.