方案是:首先对低敏感性且噪声大的patch进行压缩,示意图如下。 如图中,使用了noise magnitude mask和noise sensitivity mask,两者结合来选择要进行noise compression的patch。 由于目标模型是个黑盒,所以不能直接获知ViT划分的patch大小,因此采用从大patch size到小进行细化的搜索策略。初始化patch size为PS_0,比如初始化...
In the second step, upon detecting these regions, we identify dense areas as patches and mask them accordingly. This framework easily integrates into the preprocessing stage of any object detection model due to its independent structure, requiring no modifications to the model itself. Evaluation ...
Our 2-stage method for all feature weighting methods (APC, SC, and Uniform) for face occlusions (e.g. mask, sunglass, and crop) is substantially more robust to the Stage 1 alone baseline (ST1) on CALFW [72]. Dataset AgeDB (Mask) AgeDB (Sunglass) AgeDB (Crop) Model...
-mask: image retrieval using a masked-face query image given a gallery of normal LFW images. -sunglassand-crop: similar to the setup of-mask. The results will be saved in theresults/demodirectory. bash run_demo.sh Run retrieval using the full LFW gallery ...
Our basic idea is to design the Mask Building-Dropping process, which adaptively matches the size of important/unimportant patches by clustering points with close saliency. Experimental results on several typical 3D DNNs show that our patch-wise saliency algorithm can provide better visual guidance, ...
-sunglass and -crop: similar to the setup of -mask. The results will be saved in the results/demo directory. bash run_demo.sh Run retrieval using the full LFW gallery Set the argument args.data_folder to data in .sh files. 3.2 Reproduce results Make sure lfw-align-128 and lfw-align...