In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

Underwater object detection is one of the most challenging research topics in computer
vision technology. The complex underwater environment makes underwater images suffer …

We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders
(MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can …

The goal of multi-task learning is to enable more efficient learning than single task learning
by sharing model structures for a diverse set of tasks. A standard multi-task learning …

Few-shot object detection, which aims at detecting novel objects rapidly from extremely few
annotated examples of previously unseen classes, has attracted significant research interest …

Computer vision now relies on data, but we know surprisingly little about what factors in the
data affect performance. We argue that this stems from the way data is collected. Designing …

Multi-object tracking (MOT) is an important problem in computer vision which has a wide
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …

Methods for 3D lane detection have been recently proposed to address the issue of
inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.) …

We present vMAP, an object-level dense SLAM system using neural field representations.
Each object is represented by a small MLP, enabling efficient, watertight object modelling …

The rapid advancement of deep learning models is often attributed to their ability to leverage
massive training data. In contrast such privilege has not yet fully benefited 3D deep learning …