A fundamental topic in computer vision for nearly half a century, stereo matching involves calculating dense disparity maps from two corrected pictures. It plays a critical role in many applications, including autonomous driving, robotics, and augmented reality, among many others. According to their cost-volume computation and optimization methodologies, existing surveys categorize end-to-end architectures into 2D…

The research on vision-language models (VLMs) has gained significant momentum, driven by their potential to revolutionize various applications, including visual assistance for visually impaired individuals. However, current evaluations of these models often need to pay more attention to the complexities introduced by multi-object scenarios and diverse cultural contexts. Two notable studies shed light on these…

Multi-modal Large Language Models (MLLMs) have various applications in visual tasks. MLLMs rely on the visual features extracted from an image to understand its content. When a low-resolution image containing fewer pixels is provided as input, it translates less information to these models to work with. Due to this limitation, these models often need to…

Humans are versatile; they can quickly apply what they’ve learned from little examples to larger contexts by combining new and old information. Not only can they foresee possible setbacks and determine what is important for success, but they swiftly learn to adjust to different situations by practicing and receiving feedback on what works. This process…

Computer vision, one of the major areas of artificial intelligence, focuses on enabling machines to interpret and understand visual data. This field encompasses image recognition, object detection, and scene understanding. Researchers continuously strive to improve the accuracy and efficiency of neural networks to tackle these complex tasks effectively. Advanced architectures, particularly Convolutional Neural Networks (CNNs),…

Large language models (LLMs) have made significant strides in handling multiple modalities and tasks, but they still need to improve their ability to process diverse inputs and perform a wide range of tasks effectively. The primary challenge lies in developing a single neural network capable of handling a broad spectrum of tasks and modalities while…

In recent years, image generation has made significant progress due to advancements in both transformers and diffusion models. Similar to trends in generative language models, many modern image generation models now use standard image tokenizers and de-tokenizers. Despite showing great success in image generation, image tokenizers encounter fundamental limitations due to the way they are…

Improving image quality and variation in diffusion models without compromising alignment with given conditions, such as class labels or text prompts, is a significant challenge. Current methods often enhance image quality at the expense of diversity, limiting their applicability in various real-world scenarios such as medical diagnosis and autonomous driving, where both high quality and…

The primary goal of Sign Language Production (SLP) is to create sign avatars that resemble humans using text inputs. The standard procedure for SLP methods based on deep learning involves several steps. First, the text is translated into gloss, a language that represents postures and gestures. This gloss is then used to generate a video…

Multimodal Large Language Models (MLLMs) represent an advanced field in artificial intelligence where models integrate visual and textual information to understand and generate responses. These models have evolved from large language models (LLMs) that excelled in text comprehension and generation to now also processing and understanding visual data, enhancing their overall capabilities significantly. The main…