3 ms·
It still seems to be using only the single frame, without past/present context. E.g. a dog sometimes is recognized as teddy bear for a split second. Is there a
by deepsun 8y ago
It still seems to be using only the single frame, without past/present context. E.g. a dog sometimes is recognized as teddy bear for a split second.
Is there any "continuous" models for that? Sounds like a simple bayesian post-processing would do a great deal (e.g. recording the probability of dogs mutating to teddy bears as very low).
- JasonGenova25 8y agoYOLO stands for "You Only Look Once" so I don't think this will ever become "continuous"
- saynay 8y agoAFAIK, the 'Look Once' part refers to other systems that re-ran a section of the frame at a time through an object detector, resulting in a lot of reprocessing. You could still look only once, but have that look include multiple sequential frames. Or do something like an LSTM of frames.
- JasonGenova25 8y agoGood point. I hadn't considered this.
- razeonex 8y agoIt maybe depends on the weights that you're loading with the model
- newen 8y agoFor each frame, it returns a list of candidate detections with confidence values if I remember correctly. Should be pretty straightforward to make it smooth using that.
- alexcnwy 8y agoyeah it’s easy to fix those with a filter on the predictions. could use bayesian approach or just smooth using a majority vote over rolling window of say 3 frames...