TY - JOUR AU - Liu, Xinpeng AU - Li, Yong-Lu AU - Lu, Cewu PY - 2022/06/28 Y2 - 2024/03/29 TI - Highlighting Object Category Immunity for the Generalization of Human-Object Interaction Detection JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 2 SE - AAAI Technical Track on Computer Vision II DO - 10.1609/aaai.v36i2.20075 UR - https://ojs.aaai.org/index.php/AAAI/article/view/20075 SP - 1819-1827 AB - Human-Object Interaction (HOI) detection plays a core role in activity understanding. As a compositional learning problem (human-verb-object), studying its generalization matters. However, widely-used metric mean average precision (mAP) fails to model the compositional generalization well. Thus, we propose a novel metric, mPD (mean Performance Degradation), as a complementary of mAP to evaluate the performance gap among compositions of different objects and the same verb. Surprisingly, mPD reveals that previous methods usually generalize poorly. With mPD as a cue, we propose Object Category (OC) Immunity to boost HOI generalization. The idea is to prevent model from learning spurious object-verb correlations as a short-cut to over-fit the train set. To achieve OC-immunity, we propose an OC-immune network that decouples the inputs from OC, extracts OC-immune representations, and leverages uncertainty quantification to generalize to unseen objects. In both conventional and zero-shot experiments, our method achieves decent improvements. To fully evaluate the generalization, we design a new and more difficult benchmark, on which we present significant advantage. The code is available at https://github.com/Foruck/OC-Immunity. ER -