Category Query Learning for Human-Object Interaction Classification
Chi Xie, Fangao Zeng, Yue Hu, Shuang Liang, Yichen Wei
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ReproduceCode
- github.com/charles-xie/cqlOfficialIn paperpytorch★ 37
Abstract
Unlike most previous HOI methods that focus on learning better human-object features, we propose a novel and complementary approach called category query learning. Such queries are explicitly associated to interaction categories, converted to image specific category representation via a transformer decoder, and learnt via an auxiliary image-level classification task. This idea is motivated by an earlier multi-label image classification method, but is for the first time applied for the challenging human-object interaction classification task. Our method is simple, general and effective. It is validated on three representative HOI baselines and achieves new state-of-the-art results on two benchmarks.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| HICO-DET | CQL+GEN-VLKT-L | mAP | 36.03 | — | Unverified |
| HICO-DET | CQL+GEN-VLKT-B | mAP | 35.36 | — | Unverified |