Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

The recent breakthroughs in natural language processing for model pretraining on large
quantities of data have opened the way for similar foundation models in computer vision …

We present ImageBind, an approach to learn a joint embedding across six different
modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …

Multimodal Large Language Model (MLLM) recently has been a new rising research
hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform …

Driven by improved architectures and better representation learning frameworks, the field of
visual recognition has enjoyed rapid modernization and performance boost in the early …

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 …

This paper demonstrates an approach for learning highly semantic image representations
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners
for computer vision. Our MAE approach is simple: we mask random patches of the input …

Video generation has witnessed significant advancements yet evaluating these models
remains a challenge. A comprehensive evaluation benchmark for video generation is …