Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild
Human-object interaction detection (HOID) has traditionally been formulated as a supervised detection problem over predefined interaction categories. While such paradigms achieve strong performance on closed-set benchmarks, they fundamentally entangle interaction understanding with dataset-specific supervision, limiting their ability to generalize to open-world and compositional scenarios. Recent HOI detectors attempt to leverage MLLMs through prompting strategies to transfer interaction-specific knowledge. However, such prompt-based approaches primarily focus on extracting discriminative repr
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- LinkedLinked via arxiv author · 85%Ting Lei →
“Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild”
- LinkedLinked via arxiv author · 85%Jialin Liu →
“Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild”
- LinkedLinked via arxiv author · 85%Zhu Xu →
“Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild”
- LinkedLinked via arxiv author · 85%Yuxin Peng →
“Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild”
- LinkedLinked via arxiv author · 85%Tianyang Liu →
“Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild”