In this work, we propose a novel and surprisingly simple routing strategy called self-routing, where each capsule is routed independently by its subordinate routing network. Therefore, the agreement between capsules is not required anymore, but both poses and activations of upper-level capsules are obtained in a way similar to Mixture-of-Experts. Our experiments on CIFAR10, SVHN, and SmallNORB showthat the self-routing performs more robustly against white-box adversarial attacks and affine transformations, requiring less computation.

Humans reason with concepts and metaconcepts: we recognizered and green from visual input; we also understand that theydescribe the same property of objects(i.e.,thecolor).

Severalworksextend thesetodynamic scenes captured with monocular video, with promising performance.

Our experiments demonstrate that MSVMamba is highly competitive, with the MSVMamba-Tinymodel achieving83.0%

We demonstrate the effectiveness of our algorithm by generating high-quality results of different target domains, including those with extremely few examples (e.g., 10).

Theconventional closed-set DAmethods generally assume that the source and target domains share the same label space. However, this assumption is often not realistic in practice.

Typically, DNNs suffer from drastic performance degradation of previously learned tasksafterlearning newknowledge, which isawell-documented phenomenon, knownascatastrophic forgetting [8,9,10].

Multi-view multi-person 3D pose estimation aims to localize 3D skeleton joints for each person instance in a scene from multi-view camera inputs. It is a fundamental task that benefits many real-world applications (such assurveillance, sportscast, gaming and mixed reality) and ismainly tackled byreconstruction-based [6,14,4]andvolumetric [40]approaches inpreviousliterature, as showninFig.1(a)and(b).

Wepropose MCUNet, aframeworkthat jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference onmicrocontrollers.

The proposed MLP decoder aggregates information from different layers, andthus combining both local attention and global attention to render powerful representations.