Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Today's deep learning methods focus on how to design the objective functions to make the
prediction as close as possible to the target. Meanwhile, an appropriate neural network …

In this work, we present SEEM, a promotable and interactive model for segmenting
everything everywhere all at once in an image. In SEEM, we propose a novel and versatile …

Recently the state space models (SSMs) with efficient hardware-aware designs, ie, the
Mamba deep learning model, have shown great potential for long sequence modeling …

Compared to the great progress of large-scale vision transformers (ViTs) in recent years,
large-scale models based on convolutional neural networks (CNNs) are still in an early …

Transformer has recently gained considerable popularity in low-level vision tasks, including
image super-resolution (SR). These networks utilize self-attention along different …

We present X-Decoder, a generalized decoding model that can predict pixel-level
segmentation and language tokens seamlessly. X-Decoder takes as input two types of …

Vision and text have been fully explored in contemporary video-text foundational models,
while other modalities such as audio and subtitles in videos have not received sufficient …

Visual data such as images and videos are typically modeled as discretizations of inherently
continuous, multidimensional signals. Existing continuous-signal models attempt to exploit …