Transformers in remote sensing: A survey
Deep learning-based algorithms have seen a massive popularity in different areas of remote
sensing image analysis over the past decade. Recently, transformer-based architectures …
sensing image analysis over the past decade. Recently, transformer-based architectures …
[HTML][HTML] Deep learning based computer vision approaches for smart agricultural applications
The agriculture industry is undergoing a rapid digital transformation and is growing powerful
by the pillars of cutting-edge approaches like artificial intelligence and allied technologies …
by the pillars of cutting-edge approaches like artificial intelligence and allied technologies …
Image as a foreign language: Beit pretraining for vision and vision-language tasks
A big convergence of language, vision, and multimodal pretraining is emerging. In this work,
we introduce a general-purpose multimodal foundation model BEiT-3, which achieves …
we introduce a general-purpose multimodal foundation model BEiT-3, which achieves …
Open-vocabulary panoptic segmentation with text-to-image diffusion models
We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …
How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites
In this paper, we introduce InternVL 1.5, an open-source multimodal large language model
(MLLM) to bridge the capability gap between open-source and proprietary commercial …
(MLLM) to bridge the capability gap between open-source and proprietary commercial …
Videomae v2: Scaling video masked autoencoders with dual masking
Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …
generalize to a variety of downstream tasks. However, it is still challenging to train video …
Videomamba: State space model for efficient video understanding
Addressing the dual challenges of local redundancy and global dependencies in video
understanding, this work innovatively adapts the Mamba to the video domain. The proposed …
understanding, this work innovatively adapts the Mamba to the video domain. The proposed …
Eva: Exploring the limits of masked visual representation learning at scale
We launch EVA, a vision-centric foundation model to explore the limits of visual
representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained …
representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained …
Eva-02: A visual representation for neon genesis
We launch EVA-02, a next-generation Transformer-based visual representation pre-trained
to reconstruct strong and robust language-aligned vision features via masked image …
to reconstruct strong and robust language-aligned vision features via masked image …
Masked autoencoders as spatiotemporal learners
This paper studies a conceptually simple extension of Masked Autoencoders (MAE) to
spatiotemporal representation learning from videos. We randomly mask out spacetime …
spatiotemporal representation learning from videos. We randomly mask out spacetime …