Unlike object detection methods based solely on object category, our method can accurately recognize the target object by comprehending the objects and their semantic relationships within a complex scene.
We also demonstrate our approach's ability to generalize by evaluating the scene boundary detection task, achieving 1. 1% improvement on Average Precision (AP) over the state-of-the-art.
We present a new dataset with the goal of training models to understand the layout of the objects and the context of the image then to find the main subjects among them.
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration.
Given a query and an untrimmed video, the temporal grounding model predicts the target interval, and the predicted video clip is fed into a video translation task by generating a simplified version of the input query.
But more importantly, the proposed $W$++ space achieves superior performance in both reconstruction quality and editing quality.
In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues.
Monocular depth estimation plays a crucial role in 3D recognition and understanding.
As deep neural networks (DNNs) achieve tremendous success across many application domains, researchers tried to explore in many aspects on why they generalize well.
Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions.
Neural networks have shown to be a practical way of building a very complex mapping between a pre-specified input space and output space.