Modern detection transformers (DETRs) use a set of object queries to predict a list of
bounding boxes, sort them by their classification confidence scores, and select the top …

Abstract Text-to-image (T2I) research has grown explosively in the past year owing to the
large-scale pre-trained diffusion models and many emerging personalization and editing …

Camouflaged object detection (COD) is the challenging task of identifying camouflaged
objects visually blended into surroundings. Albeit achieving remarkable success, existing …

Recently diffusion models have made remarkable progress in text-to-image (T2I) generation
synthesizing images with high fidelity and diverse contents. Despite this advancement latent …

Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for
unseen classes which is an effective way to advance ZSL. However existing generative …

In machine learning, generative modeling aims to learn to generate new data statistically
similar to the training data distribution. In this paper, we survey learning generative models …

The existing image-to-video translation methods generally follow a frame-by-frame
generative paradigm, while extracting the temporal information from a reference video or an …

In recent years there has been significant progress in the development of text-to-image
generative models. Evaluating the quality of the generative models is one essential step in …

The success of Artificial Intelligence (AI) in multiple disciplines and vertical domains in
recent years has promoted the evolution of mobile networking and the future Internet toward …

Oriented object detection, an emerging task in recent years, aims to identify and locate
objects across varied orientations. This requires the detector to accurately capture the …