This paper presents the system developedby team alexandru412 for the AdMIRe 2.0Shared Task. We participated in the Text-Onlytrack, ranking images based on idiomatic us-age without accessing pixel data. Our approachcombines a strict list-wise ranking strategy withsystematic test-time augmentation. We fine-tuned a Large Language Model (LLM) on En-glish and Portuguese data and relied on zero-shot transfer for other languages. Our systemachieved the 3rd place in the Text-Only track.

This paper presents our system for the EEUCA0022026 shared task on Multimodal Vaccine Critical Meme Detection. The task focuses on categorizing social media memes from the VaxMeme dataset into three stances: Vaccine Critical, Neutral, and Pro-Vaccine. To tackle the inherent challenges of internet sarcasm, implicit context, and high label noise, we propose a robust, heavily regularized text-fusion pipeline. Rather than relying on computationally heavy visual encoders, we extract text directly from the images via OCR and concatenate it with the user’s social media post, processing the unified context through an ERNIE 2.0-Large encoder. To combat the severe overfitting typical in subjective meme datasets, we replace the standard classification head with a Multi-Sample Dropout architecture, averaging predictions across five parallel dropout masks (p = 0.3). Our optimized, lightweight text-only pipeline achieves a peak Macro F1 score of 0.834. Furthermore, an ablation study utilizing Focal Loss reveals that our primary solution using standard Cross-Entropy provides superior robustness against the inherent label noise found in internet meme datasets.