Research map / Detection and segmentation / Weakly supervised detection and segmentation
Bootstrapping MLLM for Weakly‑Supervised Class‑Agnostic Object Counting
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
- Learning to Count without Annotations (CVPR 2024 · similarity 0.88)
- Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting (AAAI 2024 · similarity 0.88)
- Point Segment and Count: A Generalized Framework for Object Counting (CVPR 2024 · similarity 0.87)
- OmniCount: Multi-label Object Counting with Semantic-Geometric Priors (AAAI 2025 · similarity 0.87)
- CountGD: Multi-Modal Open-World Counting (NeurIPS 2024 · similarity 0.86)
- COUNTS: Benchmarking Object Detectors and Multimodal Large Language Models under Distribution Shifts (CVPR 2025 · similarity 0.86)
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.