English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a robust solution to the Memotion 3.0 Shared Task. Thegoal of this task is to classify the emotion and the corresponding intensityexpressed by memes, which are usually in the form of images with short captionson social media. Understanding the multi-modal features of the given memes willbe the key to solving the task. In this work, we use CLIP to extract alignedimage-text features and propose a novel meme sentiment analysis framework,consisting of a Cooperative Teaching Model (CTM) for Task A and a CascadedEmotion Classifier (CEC) for Tasks B&C. CTM is based on the idea of knowledgedistillation, and can better predict the sentiment of a given meme in Task A;CEC can leverage the emotion intensity suggestion from the prediction of Task Cto classify the emotion more precisely in Task B. Experiments show that weachieved the 2nd place ranking for both Task A and Task B and the 4th placeranking for Task C, with weighted F1-scores of 0.342, 0.784, and 0.535respectively. The results show the robustness and effectiveness of ourframework. Our code is released at github.

NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis