Open YueLiao opened 3 years ago
Hi~ In fact, our dynamic interaction is not same as multi-head attention. We do not recommend you understand dynamic interaction by the concepts of query, key and value.
Hi~ In fact, our dynamic interaction is not same as multi-head attention. We do not recommend you understand dynamic interaction by the concepts of query, key and value.
Thx for your reply. I understand your mean where the dynamic filter is similar to what in the Siamese FC for single object tracking, where the proposal feature serves as a convolutional kernel and the ROI feature is a template. Moreover, I still have a question about ablation studies. Could you provide more details about the multi-head attention setting in Table 8, e.g., query, key, and value?
In fact, our dynamic interaction is not same as multi-head atten
Hi, thank you for your novel work and replies. I noticed that parameters was separated into two parts and interact with RoI separately, does it have special significance? thx in advance!
Hi, thanks for your work. I have the same question as BreezeHavana that why parameters was separated into two parts and interact with RoI separately? Has the question been answered?
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Thx for your interesting work and the code. I am confused by some points as follow when fusing ROI feats and proposal feats:
In this setting, the transformed proposal feature serves as a key, and the transformed ROI feature is regarded as a query. Firstly, producing an attention weight between ROI feature and proposal feature. Then, combining the attention weight with the other transformed proposal feature, which is a value. In this case, none of the appearance features (ROI) are kept, the interacted and output feature is a re-weighted proposal feature. The ROI feature only plays a role to compute an attention map. I do not understand why it works, predicting bounding-box without the appearance features. Additionally, it also looks different from the original paper: And it is inversed to the multi-head attention in Detr. @PeizeSun