Compared with convolutional neural networks limited bytherelativelysmall receptive fields, the advantage of transformer for visual tasks is the capacity to perceivelong-range dependencies amongallimagepatches,whilethedeficiency is that the local fine-grained information is not fully excavated.

To uncover the factual basis, we delve into this ambiguity and detail it into two flaws according to experimental insight. Specifically, the first flaw lies in that SAM prediction is sensitive to slightly different prompt variants.

Recently, the anchor acceleration, an acceleration mechanism distinct from Nes-terov's, has been discovered for minimax optimization and fixed-point problems,

Modern photo editing tools allow creating realistic manipulated images easily.

Figure 7: Learningcurvesof ATOC (left) and CommNet (right) during learningonpredator -prey.

Unfortunately, very little to no attention is paid towards addressing calibration ofDNN-based visual object detectors, that occupysimilar space and importance inmanydecision making systems astheir visual classification counterparts. In this work, we study the calibration of DNN-based object detection models, particularly under domain shift.

Wepresent MubyNet -afeed-forward,multitask, bottom upsystem fortheintegrated localization, as well as 3d pose and shape estimation, of multiple people inmonocular images.