CLIP

The latest advancement in fake news detection doesn’t just analyze what is said—it also looks at how it feels. The SEER model (Semantic Enhancement and Emotional Reasoning Network) introduces an innovative approach that harnesses emotional reasoning and semantic depth to surpass existing benchmarks in multimodal fake news detection. 🧠 Beyond Consistency: The Emotional Gap in Fake News Traditionally, models focus on image-text consistency: does the photo match the caption? But this misses the forest for the trees. Fake news isn’t just mismatched—it’s emotionally manipulative. ...

When it comes to prompting vision-language models, most methods rely on textual descriptions extracted from large language models like GPT. But those descriptions—“fluffy fur, friendly eyes, golden color”—are often verbose, ambiguous, or flat-out unreliable. What if we could skip that noisy middle step entirely? That’s the premise behind DeMul (Description-free Multi-prompt Learning), a new method presented at ICLR 2025 that quietly delivers a major leap in few-shot image classification. Instead of generating descriptions for each class, DeMul directly distills the semantic knowledge of GPT embeddings into learnable prompt vectors. The result is simpler, more robust, and strikingly effective. ...