Comments (2)
Yuzuriha-Inori-x
commented on July 28, 2024
Also, if GLIP is used as the target detection model, will the number of detected objects in a certain frame be less than 10? If this happens, how will it be handled? If this frame is skipped directly, will the number of frames required to meet the object number greater than 10 be insufficient?
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ecoxial2007
commented on July 28, 2024
Typically, we adjust the confidence threshold of the bounding boxes (bbox) to ensure that the number of detected objects is greater than 10. Subsequently, we select the top 10 bounding boxes with the highest confidence. This approach ensures that we do not encounter situations where the number of bounding boxes is insufficient.
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Related Issues (12)
- Missing h5 extracted feature files HOT 2
- feature extraction flies HOT 2
- code reproduction problem HOT 2
- the meaning of qid in next_train_qa.json HOT 1
- the code for multi-gpu training HOT 1
- The extraction embedding coding of "bbox_features" HOT 5
- Incomplete extract_embedding.py code? HOT 1
- About CLIP’s text-encoder. HOT 3
- Processing of candidate words HOT 3
- Clarification on Feature Representations from text_features_all.h5 and text_features_clip.h5 HOT 5
- About bbox_features in NeXt-clip-bbox-features.zip
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