We propose a test-time adaptation method for cross-domain image segmentation. Our is simple: Given new unseen instance at test time, we adapt pre-trained model by conducting instance-specific BatchNorm (statistics) calibration. approach has two core components. First, replace the manually designed calibration rule with learnable module. Second, leverage strong data augmentation to simulate rand...

Vehicle instance retrieval (IR) often requires one to recognize the fine-grained visual differences between vehicles. Besides holistic appearance of vehicles which is easily affected by viewpoint variation and distortion, vehicle parts also provide crucial cues differentiate near-identical Motivated these observations, we introduce a Part-Guided Attention Network (PGAN) pinpoint prominen...

Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features the detection, segmentation, tracking of objects they suffer video scenario due several distinct challenges such as motion blur drastic appearance change. To eliminate ambiguities introduced by using features, propos...

Cascaded architectures have brought significant performance improvement in object detection and instance segmentation. However, there are lingering issues regarding the disparity Intersection-over-Union (IoU) distribution of samples between training inference. This can potentially exacerbate accuracy. paper proposes an architecture referred to as Sample Consistency Network (SCNet) ensure that I...

Prime factorization is a difficult problem with classical computing, whose exponential hardness the foundation of Rivest-Shamir-Adleman (RSA) cryptography. With programmable quantum devices, adiabatic computing has been proposed as plausible approach to solve prime factorization, having promising advantage over computing. Here, we find there are certain hard instances that consistently intracta...

Multiple Instance Learning (MIL) proposes a new paradigm when instance labeling, in the learning step, is not possible or infeasible, by assigning a single label (positive or negative) to a set of instances called bag. In this paper, an operator based on homogeneity of positive bags for MIL is introduced. Our method consists in removing instances from the positives bags according to their simil...

We propose a multiple instance learning approach to contentbased retrieval of classroom video for the purpose of supporting human assessing the learning environment. The key element of our approach is a mapping between the semantic concepts of the assessment system and features of the video that can be measured using techniques from the fields of computer vision and speech analysis. We report o...

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, ...