Defending pre-trained vision-language models (VLMs), such as CLIP, against adversarial attacks is crucial, as these models are widely used in diverse zero-shot tasks, including image classification. However, existing adversarial training (AT) methods...
Highlighting particularly relevant regions of an image can improve the performance of vision-language models (VLMs) on various vision-language (VL) tasks by guiding the model to attend more closely to these regions of interest. For example, VLMs can...
Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despite remarkable progress, VLMs are subject to several shortcomings due t...
Vision-based action recognition is one of the most challenging research topics of computer vision and pattern recognition. A specific application of it, namely, detecting fights from surveillance cameras in public areas, prisons, etc., is desired to...
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 …
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 …
We introduce VisIT-Bench (Visual InsTruction Benchmark), a benchmark for evaluation of instruction-following vision-language models for real-world use. Our starting point is curating 70 'instruction families' that we envision instruction tuned vision...
"You think it’s bad now? Oh, you have no idea. In his talks with Ben Thompson and Dwarkesh Patel, Zuckerberg lays out his vision for our AI future. I thank him for his candor. I’m still kind of b...
A vision transformer (ViT) is a transformer designed for computer vision. A ViT decomposes an input image into a series of patches (rather than text into
The success of Vision Transformer (ViT) in various computer vision tasks has promoted the ever-increasing prevalence of this convolution-free network. The fact that ViT works on image patches makes it potentially relevant to the problem of jigsaw puz...
Proceedings of the Second Croatian Computer Vision Workshop (CCVW 2013, http://www.fer.unizg.hr/crv/ccvw2013) held September 19, 2013, in Zagreb, Croatia. Workshop was organized by the Center of Excellence for Computer Vision of the University of Zag...
Vision Transformers have made remarkable progress in recent years, achieving state-of-the-art performance in most vision tasks. A key component of this success is due to the introduction of the Multi-Head Self-Attention (MHSA) module, which enables e...
Masked autoencoders are scalable vision learners, as the title of MAE \cite{he2022masked}, which suggests that self-supervised learning (SSL) in vision might undertake a similar trajectory as in NLP. Specifically, generative pretext tasks with the ma...
VideoFinder is an advanced video analysis tool powered by multimodal AI, designed to help users easily locate and identify specific objects or people within video content. By combining the capabilities of Llama Vision model with a streamlined web interface, it enables real-time,…
Code for Video Deepfake Detection model from "Combining EfficientNet and Vision Transformers for Video Deepfake Detection" presented at ICIAP 2021. (⭐ 266)
Building scalable vision-language models to learn from diverse, multimodal data remains an open challenge. In this paper, we introduce an Efficient Vision-languagE foundation model, namely EVE, which is one unified multimodal Transformer pre-trained...
Companion code for "Uncertainty-Driven Active Vision for Implicit Scene Reconstruction". This repository contains the code base for a Next Best View (NBV) task along with our proposed uncertainty driven solution and a set of baselines for comparison. (⭐ 33)
We built a vision system of curling robot which can be expected to play with human curling player. Basically, we built two types of vision systems for thrower and skip robots, respectively. First, the thrower robot drives towards a given point of cur...
Computer vision (CV) has achieved great success in interpreting semantic meanings from images, yet CV algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation. One of this tasks is in-b...
Bushnell Night Vision 2x24 Night Watch Monocular 260224 is a durable and flexible Night Vision Monocular scope with 2x magnification, a rubber-armored grip, a built-in tripod mount and and a built …