Comments (1)
In TIN, if you want to infer the data out of HICO-DET and VCOCO, the human and object box detections and human pose estimations should be prepared first. Then, one image paired with its corresponding detected human, object boxes, human keypoints can be fed into the TIN to infer the HOIs.
In code, all the above things are zipped into a pkl file:
If you do not need the object all the time, you could also consider a simpler tool of HAKE:
https://github.com/DirtyHarryLYL/HAKE-Action-Torch/tree/Activity2Vec, which contains the human detector and pose estimator and just needs the images/videos as inputs.
from transferable-interactiveness-network.
Related Issues (20)
- about test the model,mAP value is too low HOT 1
- The eigenvector shape problem of early fusion and late fusion
- The shape of pool5_O in early and late fusion HOT 1
- self.spatial = tf.placeholder(tf.float32, shape=[None, 64, 64, 3], name = 'sp') HOT 9
- What is the reason for using negative examples? HOT 4
- problem in installing HOT 4
- Questions about the ablation studies HOT 2
- About self.HO_weight HOT 1
- V-COCO training results is bad HOT 2
- The empty detections
- the pre-trained weight
- augment and pos_augment? HOT 1
- the code for ten part boxes HOT 1
- What is the approximate speed of video real-time motion detection?
- where to find or download the file res50_faster_rcnn HOT 2
- Bad evaluation results HOT 1
- Question about final HOI classification scores HOT 4
- Issue Downloading Pretrained Weight Files HOT 3
- raceback (most recent call last): │ File "tools/Vcoco_lis_nis.py", line 304, in <module> │ generate_result = generate_pkl(mode, test_D, test_result, prior_mask, Action_dic_inv, (6, 6, 7, 0), args.prior_flag) │ File "tools/Vcoco_lis_nis.py", line 145, in generate_pkl │ score_binary_d = np.array(dic_d['binary_score']) │KeyError: 'binary_score'
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from transferable-interactiveness-network.