Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classification

arXiv:2609.26907 · cs.MM, cs.CL, cs.LG · Submitted 2026-09-22 · Read on arXiv

cs.MM, cs.CL, cs.LG

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/AkshitSharma1/MemePIVOT

Terminology

• AUROC — 28× in this paper · explained in 2 episodes
• Benchmark — 22× in this paper · explained in 2 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Ablation — 10× in this paper · explained in 1 episode
• Core — 9× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Grounding — 8× in this paper · explained in 3 episodes
• Optimal Transport — 7× in this paper · explained in 5 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Embeddings — 3× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Multimodal Dataset — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Frustration — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs
• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• Gemma 3 Technical Report
• DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation

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