Recently, the transformer has enabled the speed-oriented trackers to approach state-of-the-
art (SOTA) performance with high-speed thanks to the smaller input size or the lighter …

Image classifiers are information-discarding machines, by design. Yet, how these models
discard information remains mysterious. We hypothesize that one way for image classifiers …

Spatial down-sampling techniques, such as strided convolution, Gaussian, and Nearest
down-sampling, are essential in deep neural networks. In this study, we revisit the working …

Point-based interactive image segmentation can ease the burden of mask annotation in
applications such as semantic segmentation and image editing. However, fully extracting the …

In visual recognition, scale bias is a key challenge due to the imbalance of object and image
size distribution inherent in real scene datasets. Conventional solutions involve injecting …

We present a framework that enables an object detector to self-enhance its accuracy while
preserving its efficiency. This framework is particularly useful in settings where a single …