Research map / Vision-language and multimodal / CLIP and zero-shot
TIM++: Transductive Information Maximization for Few-Shot CLIP
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Transductive Zero-Shot and Few-Shot CLIP (CVPR 2024 · similarity 0.90)
- Boosting Vision-Language Models with Transduction (NeurIPS 2024 · similarity 0.89)
- Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP (AAAI 2025 · similarity 0.89)
- A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation (ICLR 2024 · similarity 0.88)
- A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models (CVPR 2024 · similarity 0.88)
- LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP (CVPR 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.