SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization.
Citations
23 citations
23 citations
Cites methods from "SS-CAM: Smoothed Score-CAM for Shar..."
...This metric is compared to the confidence generated by SS-CAM [11] maps and Score-CAM [10] maps with IS-CAM maps....
[...]
...Likewise, SS-CAM does well in Average Drop and Inc% but it fails to do so in AUC scores....
[...]
...Normalization: As the spatial region needs to focused on the object in the image, we leverage the features within a particular region by following the same normalization function as stated in [10], [11]....
[...]
...To perform this sub-experiment, we used N = 15 and σ = 2 (for SS-CAM)....
[...]
...When our approach is compared to SS-CAM, we get 59.25% and when compared to Score-CAM, we get 52.35% using VGG-16(higher is better); which indicates that IS-CAM performs better with respect to this metric....
[...]
15 citations
9 citations
7 citations
References
123,388 citations
55,235 citations
"SS-CAM: Smoothed Score-CAM for Shar..." refers methods in this paper
...A pretrained VGG-16 model was used to create the explanation maps....
[...]
...These metrics are evaluated over the pre-trained VGG-16 model for 2000 images randomly selected from the ILSVRC 2012 Validation set....
[...]
...Three pre- trained models namely, VGG-16 [17], ResNet-18(Residual Network with 18-layers) [6] and SqueezeNet1....
[...]
...Three pretrained models namely, VGG-16 [17], ResNet-18(Residual Network with 18-layers) [6] and SqueezeNet1....
[...]
49,914 citations
49,639 citations
"SS-CAM: Smoothed Score-CAM for Shar..." refers methods in this paper
...The images are resized with a definite size (224, 224, 3), transformed into the [0,1] range and then, normalized using ImageNet [3] weights (mean vector : [0.485, 0.456, 0.406] and standard deviation vector [0.229, 0.224, 0.225])....
[...]
...We choose 5 classes at random as this would give an equal probability of all the classes getting to be picked from the 1000 ImageNet classes, hence, removing any bias....
[...]
...We generate explanation maps for 50 images of the 5 randomly selected classes out of 1000 classes from the ILSVRC 2012 Validation dataset [3], totalling to 250 images....
[...]
...The images are resized with a definite size (224, 224, 3), transformed into the [0,1] range and then, normalized using ImageNet [3] weights (mean vector : [0....
[...]
44,703 citations
"SS-CAM: Smoothed Score-CAM for Shar..." refers background or methods in this paper
...There have been sufficient advancements in its architectures [6] [21] to cope with complex problems such as image captioning [8], image classification [20], semantic segmentation [10] and many other problems [7], [13], [22]....
[...]
...Three pretrained models namely, VGG-16 [17], ResNet-18(Residual Network with 18-layers) [6] and SqueezeNet1....
[...]