3 research outputs found
Rethinking gradient weights' influence over saliency map estimation
Class activation map (CAM) helps to formulate saliency maps that aid in
interpreting the deep neural network's prediction. Gradient-based methods are
generally faster than other branches of vision interpretability and independent
of human guidance. The performance of CAM-like studies depends on the governing
model's layer response, and the influences of the gradients. Typical
gradient-oriented CAM studies rely on weighted aggregation for saliency map
estimation by projecting the gradient maps into single weight values, which may
lead to over generalized saliency map. To address this issue, we use a global
guidance map to rectify the weighted aggregation operation during saliency
estimation, where resultant interpretations are comparatively clean er and
instance-specific. We obtain the global guidance map by performing elementwise
multiplication between the feature maps and their corresponding gradient maps.
To validate our study, we compare the proposed study with eight different
saliency visualizers. In addition, we use seven commonly used evaluation
metrics for quantitative comparison. The proposed scheme achieves significant
improvement over the test images from the ImageNet, MS-COCO 14, and PASCAL VOC
2012 datasets
Denoising single images by feature ensemble revisited
Image denoising is still a challenging issue in many computer vision
sub-domains. Recent studies show that significant improvements are made
possible in a supervised setting. However, few challenges, such as spatial
fidelity and cartoon-like smoothing remain unresolved or decisively overlooked.
Our study proposes a simple yet efficient architecture for the denoising
problem that addresses the aforementioned issues. The proposed architecture
revisits the concept of modular concatenation instead of long and deeper
cascaded connections, to recover a cleaner approximation of the given image. We
find that different modules can capture versatile representations, and
concatenated representation creates a richer subspace for low-level image
restoration. The proposed architecture's number of parameters remains smaller
than the number for most of the previous networks and still achieves
significant improvements over the current state-of-the-art networks
Rethinking Gradient Weight’s Influence over Saliency Map Estimation
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network’s prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guidance. The performance of CAM-like studies depends on the governing model’s layer response and the influences of the gradients. Typical gradient-oriented CAM studies rely on weighted aggregation for saliency map estimation by projecting the gradient maps into single-weight values, which may lead to an over-generalized saliency map. To address this issue, we use a global guidance map to rectify the weighted aggregation operation during saliency estimation, where resultant interpretations are comparatively cleaner and instance-specific. We obtain the global guidance map by performing elementwise multiplication between the feature maps and their corresponding gradient maps. To validate our study, we compare the proposed study with nine different saliency visualizers. In addition, we use seven commonly used evaluation metrics for quantitative comparison. The proposed scheme achieves significant improvement over the test images from the ImageNet, MS-COCO 14, and PASCAL VOC 2012 datasets