We aim at advancing open-vocabulary object detection, which detects objects described by
arbitrary text inputs. The fundamental challenge is the availability of training data. It is costly …

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 …

We present WALT, a diffusion transformer for photorealistic video generation from text
prompts. Our approach has two key design decisions. First, we use a causal encoder to jointly …

We present VideoPoet, a language model capable of synthesizing high-quality video, with
matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-…

We present F-VLM, a simple open-vocabulary object detection method built upon Frozen
Vision and Language Models. F-VLM simplifies the current multi-stage training pipeline by …

Observing the close relationship among panoptic, semantic and instance segmentation
tasks, we propose to train a universal multi-dataset multi-task segmentation model: DaTaSeg. …

We present a novel deep neural network architecture for end-to-end scene flow estimation
that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional …

While Large Language Models (LLMs) are the dominant models for generative tasks in
language, they do not perform as well as diffusion models on image and video generation. To …

Large language models have achieved great success in recent years so as their variants in
vision. Existing vision-language models can describe images in natural languages answer …

Cameras are prevalent in our daily lives, and enable many useful systems built upon computer
vision technologies such as smart cameras and home robots for service applications. …