MemeTAG: Keyword-Driven Meme Classification through Tag Embedding Reconstruction

arXiv:2609.20962 · cs.CV, cs.MM · Submitted 2026-09-17 · Read on arXiv

cs.CV, cs.MM

Submitted: 2026-09-17

Updated: 2026-09-17

Terminology

• Embeddings — 24× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Alignment — 10× in this paper · explained in 1 episode
• AUC — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Aggregated Tag Inference Network — 6× in this paper
• Benchmark — 6× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Hateful Memes Challenge — 5× in this paper
• Transformer — 5× in this paper · explained in 2 episodes
• Vision-Language Model — 5× in this paper
• Semantic Alignment — 4× in this paper · explained in 3 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• International Conference on Machine Learning — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• Loss Function — 2× in this paper · explained in 2 episodes
• Multimodal Fusion — 2× in this paper · explained in 3 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Late Fusion — 1× in this paper · explained in 2 episodes
• Multi-Head Self-Attention — 1× in this paper
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• Gemini: A Family of Highly Capable Multimodal Models
• GPT-4 Technical Report
• LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL
• LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
• MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization
• Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• MM-RLHF: The Next Step Forward in Multimodal LLM Alignment

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models