5 accepted papers
In order to tackle video semantic segmentation task at a lower cost, e.g., only one frame annotated per video, lots of efforts have been devoted to investigate the utilization of those unlabeled frames by either assigning pseudo labels or performing feature enhancement. In this work, we propose a no…
Modeling in computer vision has long been dominated by convolutional neural networks (CNNs). Recently, in light of the excellent performances of self-attention mechanism in the language field, transformers tailored for visual data have drawn numerous attention and triumphed CNNs in various vision ta…
Panoptic segmentation is a challenging task aiming to simultaneously segment objects (things) at instance level and background contents (stuff) at semantic level. Existing methods mostly utilize two-stage detection network to attain instance segmentation results, and fully convolutional network to p…
In recent years, deep neural nets have triumphed over many computer vision problems, including semantic segmentation, which is a critical task in emerging autonomous driving and medical image diagnostics applications. In general, training deep neural nets requires a humongous amount of labeled data,…
Most existing binary embedding methods prefer compact binary codes (b-dimensional) to avoid high computational and memory cost of projecting high-dimensional visual features (d-dimensional, b < d). We argue that long binary codes (b O(d)) are critical to fully utilize the discriminative power of h…