Overview of Sscs: Support-set Based Cross-Supervision
Sscs, or Support-set Based Cross-Supervision, is a vide grounding module that aims to improve the effectiveness of video representations. This is accomplished through two main components: a discriminative contrastive objective and a generative caption objective. The contrastive objective learns effective representations through contrastive learning, while the caption objective trains a powerful video encoder supervised by texts.
The Challenge of Mutual Exclusion in Video Grounding
One of the main challenges in video grounding is the co-existence of visual entities in both ground-truth and background intervals. This is known as mutual exclusion, and it makes contrastive learning unsuitable to video grounding. Sscs solves this problem by boosting the cross-supervision with the support-set concept, which collects visual information from the entire video and eliminates the mutual exclusion of entities.
The Support-set Module
The support-set module is a key component of Sscs. It works by computing the weighted average of the clip-level embeddings for two video-text pairs in a shared space. This allows for a combined contrastive and caption objective that pulls representations of clips and text from the same samples close together and pushes them away from other pairs.
The Benefits of Sscs
Sscs offers several benefits when it comes to video grounding. It allows for more effective representation learning, which results in better overall performance. Additionally, it addresses the challenge of mutual exclusion, making it more suitable for use in a wide range of applications.
Applications of Sscs
Sscs can be used in a variety of applications where video grounding is needed. One example is surveillance, where it can be used to track people or objects in real-time. It can also be used in video search engines, making it easier to find specific content within large video databases. Other potential applications include video editing, robotics, and virtual reality.
