Instance-level video segmentation requires a solid integration of spatial and temporal information. However, current methods rely mostly on domain-specific information (online learning) to produce accurate instance-level segmentations. We propose novel approach that relies exclusively the generic spatio-temporal attention cues. Our strategy, named Multi-Attention Instance Network (MAIN), overco...

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...