**Temporal Jittering** is a method used in deep learning for video, where we sample multiple training clips from each video with random start times during at every epoch.

**CMCL**, or **Crossmodal Contrastive Learning**, is a method for unifying visual and textual representations into the same semantic space based on a large-scale corpus of image collections, text corpus and image-text pairs. The CMCL aligns the visual representations and textual representations, and unifies them into the same semantic space based on image-text pairs. As shown in the Figure, to facilitate different levels of semantic alignment between vision and language, a series of text rewriting techniques are utilized to improve the diversity of cross-modal information. Specifically, for an image-text pair, various positive examples and hard negative examples can be obtained by rewriting the original caption at different levels. Moreover, to incorporate more background information from the single-modal data, text and image retrieval are also applied to augment each image-text pair with various related texts and images. The positive pairs, negative pairs, related images and texts are learned jointly by CMCL. In this way, the model can effectively unify different levels of visual and textual representations into the same semantic space, and incorporate more single-modal knowledge to enhance each other.

CMCL

UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning

Temporal Jittering

**CentripetalNet** is a keypoint-based detector which uses centripetal shift to pair corner keypoints from the same instance. CentripetalNet predicts the position and the centripetal shift of the corner points and matches corners whose shifted results are aligned.

Year	2000
Data Source	Papers with Code archive (CC BY-SA)