We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document understanding. Our model is trained on a comprehensive instruction-follo...
This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search a sub-structure from the original model end-to-end across multiple dim...
"ABOUT US". Empowering The Vision. Archived from the original on 2014-09-04. Retrieved 2014-09-11. "GTPN Founding Day Celebration". Empowering The Vision
Vision-language navigation in unknown environments is crucial for mobile robots. In scenarios such as household assistance and rescue, mobile robots need to understand a human command, such as "find a person wearing black". We present a novel vision-...
Vision transformers (ViTs) are top performing models on many computer vision benchmarks and can accurately predict human behavior on object recognition tasks. However, researchers question the value of using ViTs as models of biological learning beca...
A minimalist vision system uses the smallest number of pixels needed to solve a vision task. While traditional cameras use a large grid of square pixels, a minimalist camera uses freeform pixels that can take on arbitrary shapes to increase their inf...
Transformers have become central to recent advances in computer vision. However, training a vision Transformer (ViT) model from scratch can be resource intensive and time consuming. In this paper, we aim to explore approaches to reduce the training c...
Aug 20, 2021 · This is an updated version of X-Vision Preset "Vivid" by SmC12 ALL CREDITS GOES TO HIM, I just updated his product to work with latest version, nothing else. It may not look like the …
Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and...
https://reddit.com/link/1n0hw6d/video/5giuc09f8clf1/player Gaming on Apple Vision Pro is not limited to casual games. I’ve spent months trying out different approaches to high end gaming on Vision ...
Visions: How Science Will Revolutionize the 21st Century is a popular science book by Michio Kaku first published in 1997. In Visions, Kaku examines the
The projects of the Computer Vision Course taught by Rob Fergus in NYU Courant (⭐ 5)
Let's train vision transformers (ViT) for cifar 10 / cifar 100! (⭐ 708)
Experiments and data for the paper "When and why vision-language models behave like bags-of-words, and what to do about it?" Oral @ ICLR 2023 (⭐ 292)
Self-supervised learning (SSL) leverages vast unannotated medical datasets, yet steep technical barriers limit adoption by clinical researchers. We introduce Vision Foundry, a code-free, HIPAA-compliant platform that democratizes pre-training, adapta...
With the recent surge in the research of vision transformers, they have demonstrated remarkable potential for various challenging computer vision applications, such as image recognition, point cloud classification as well as video understanding. In t...
We present SOLO, a single transformer for Scalable visiOn-Language mOdeling. Current large vision-language models (LVLMs) such as LLaVA mostly employ heterogeneous architectures that connect pre-trained visual encoders with large language models (LLM...
Mississippi State University AI Club Campus Vision Challenge Repository (⭐ 5)
Scaling up the size of vision models has been the de facto standard to obtain more powerful visual representations. In this work, we discuss the point beyond which larger vision models are not necessary. First, we demonstrate the power of Scaling on...
Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation of their efficacy. This paper presents a comprehensive evaluation of publicly...