  Short-term object interaction anticipation is an important task in egocentric
video analysis, including precise predictions of future interactions and their
timings as well as the categories and positions of the involved active objects.
To alleviate the complexity of this task, our proposed method, SOIA-DOD,
effectively decompose it into 1) detecting active object and 2) classifying
interaction and predicting their timing. Our method first detects all potential
active objects in the last frame of egocentric video by fine-tuning a
pre-trained YOLOv9. Then, we combine these potential active objects as query
with transformer encoder, thereby identifying the most promising next active
object and predicting its future interaction and time-to-contact. Experimental
results demonstrate that our method outperforms state-of-the-art models on the
challenge test set, achieving the best performance in predicting next active
objects and their interactions. Finally, our proposed ranked the third overall
top-5 mAP when including time-to-contact predictions. The source code is
available at https://github.com/KeenyJin/SOIA-DOD.
