所以对于 COCO,就要求 CNN 必须要有在极小的 scale 和 很大的 scale 上(这两者之间的比例值很大,比如 0.0001 vs 0.9)之间的目标都有很好的分类能力才会有很好的性能,也就要要有对 extreme scale variation 的鲁棒性,即 scale-invariant。 对于pre-trained dataset 和要 apply 的 target dataset 之间的 scale va...
此前的解决方案是:通过更大更深的CNN提取更有判别性的尺度不变特征(learn highly discriminative scale-invariant representation),但如HR里提到,对于一个300像素的人脸、和一个10像素的人脸,二者在深层CNN中所表达的特征肯定是不一样的,这时如果寄希望于用一个CNN来cover所有的人脸尺度,将各类大小的人脸all in,肯定...
The recovered scale-invariant representation disentangles appearance from scale and frees the pixel-level classifier from the need to learn the laws of the perspective. This results in improved segmentation results due to more efficient exploitation of representation capacity and training data. We ...
3、Scale compensation anchor matching strategy 当前的anchor匹配策略都是先计算所有的人脸与anchor之间的IoU,然后从中选择大于threshold的anchor。针对分析2)中提到的问题,本文使用了尺度补偿的anchor匹配策略:首先仍然选用当前的anchor匹配策略,但将阈值调低,从0.5降到0.3,以增加匹配的anchor的平均数量;接着,对那些未匹配...
These are more robust to imaging noise, and computationally more efficient compared to the Scale Invariant Feature Transform (SIFT)34. For matching pairs of key points, we used the Hamming distance metric. To estimate the affine spatial transformation between pairs of image mosaics, in a manner ...
Range conditioned dilated convolutions for scale invariant 3d object detection. In Conference on Robot Learning, pages 627–641. PMLR, 2021. 3 [5] Bharat Lal Bhatnagar, Xianghui Xie, Ilya A Petrov, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. ...
classDetectionPredictor(BasePredictor):defpostprocess(self,preds,img,orig_imgs):preds=ops.non_max_suppression(preds,self.args.conf,self.args.iou,agnostic=self.args.agnostic_nms,max_det=self.args.max_det,classes=self.args.classes)ifnotisinstance(orig_imgs,list):orig_imgs=ops.convert_torch2numpy...
We design a Receptive Field Enhancement module called RFE to enhance receptive field of small face, and use NWD Loss to make up for the sensitivity of IoU to the location deviation of tiny objects. For face occlusion, we present an attention module named SEAM and introduce Repulsion Loss to ...
feature extraction techniques in the early stages heavily relied on manually crafted approaches such as scale-invariant feature transformation (SIFT)15and histogram of oriented gradients (HOG)16, which were tailored explicitly for distinct image feature types. The extracted features are subsequently utilize...
(3)Low tolerance for bounding box perturbation:对于一个小目标,预测边界框的轻微偏差会导致IoU显著降低。这意味着与较大目标相比,小目标对于边界框的扰动容忍度较低,这使得回归分支的学习变得更加困难。 (4)Inadequate samples for training:小目标占据的区域相对较小,并且与先验框(锚点或点)的重叠有限。这对于传...