Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos.
Ranked #1 on Multi-Object Tracking on MOT17 (using extra training data)
We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner.
Ranked #1 on Image Generation on FFHQ-U
The input are an initial estimate of the point cloud and the camera parameters.
We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers.
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP).
Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training.