Deep neural networks often work well when they are over-parameterized and trained with a
massive amount of noise and regularization, such as weight decay and dropout. Although …

Current state-of-the-art convolutional architectures for object detection are manually designed.
Here we aim to learn a better architecture of feature pyramid network for object detection. …

CNN architectures have terrific recognition performance but rely on spatial pooling which
makes it difficult to adapt them to tasks that require dense, pixel-accurate labeling. This paper …

We design an open-vocabulary image segmentation model to organize an image into meaningful
regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite attaining …

Building instance segmentation models that are data-efficient and can handle rare object
categories is an important challenge in computer vision. Leveraging data augmentations is a …

Pre-training is a dominant paradigm in computer vision. For example, supervised ImageNet
pre-training is commonly used to initialize the backbones of object detection and …

Much research on object detection focuses on building better model architectures and
detection algorithms. Changing the model architecture, however, comes at the cost of adding …

In this paper, we present a method which combines the flexibility of the neural algorithm of
artistic style with the speed of fast style transfer networks to allow real-time stylization using …

The presence of occluders significantly impacts performance of systems for object recognition.
However, occlusion is typically treated as an unstructured source of noise and explicit …

Convolutional neural networks typically encode an input image into a series of intermediate
features with decreasing resolutions. While this structure is suited to classification tasks, it …