Recent advancements in generative models for text-to-image (T2I) tasks have led to impressive results in producing high-resolution, realistic images from textual prompts. However, extending this capability to text-to-video (T2V) models poses challenges due to the complexities introduced by motion. Current T2V models face limitations in video duration, visual quality, and realistic motion generation, primarily due…

In AI image generation, text-to-image diffusion models have become a focal point due to their ability to create photorealistic images from textual descriptions. These models use complex algorithms to interpret text and translate it into visual content, simulating creativity and understanding previously thought unique to humans. This technology holds immense potential across various domains, from…

Mathematical reasoning, part of our advanced thinking, reveals the complexities of human intelligence. It involves logical thinking and specialized knowledge, not just in words but also in pictures, crucial for understanding abilities. This has practical uses in AI. However, current AI datasets often focus narrowly, missing a full exploration of combining visual language understanding with…

In image generation, diffusion models have significantly advanced, leading to the widespread availability of top-tier models on open-source platforms. Despite these strides, challenges in text-to-image systems persist, particularly in managing diverse inputs and being confined to single-model outcomes. Unified efforts commonly address two distinct facets: first, the parsing of various prompts during the input stage,…

Contrastive pre-training using large, noisy image-text datasets has become popular for building general vision representations. These models align global image and text features in a shared space through similar and dissimilar pairs, excelling in tasks like image classification and retrieval. However, they need help with fine-grained tasks such as localization and spatial relationships. Recent efforts…

In the challenging fight against illegal poaching and human trafficking, researchers from Washington University in St. Louis’s McKelvey School of Engineering have devised a smart solution to enhance geospatial exploration. The problem at hand is how to efficiently search large areas to find and stop such activities. The current methods for local searches are limited…

There are two major challenges in visual representation learning: the computational inefficiency of Vision Transformers (ViTs) and the limited capacity of Convolutional Neural Networks (CNNs) to capture global contextual information. ViTs suffer from quadratic computational complexity while excelling in fitting capabilities and international receptive field. On the other hand, CNNs offer scalability and linear complexity…

Many people are now interested in the state space model (SSM) because of how recent research has advanced. Modern SSMs, which derive from the classic state space model, benefit from concurrent training and excel at capturing long-range dependencies. Process sequence data across many activities and modalities using SSM-based methods like linear state-space layers (LSSL), structured…

Task-agnostic model pre-training is now the norm in Natural Language Processing, driven by the recent revolution in large language models (LLMs) like ChatGPT. These models showcase proficiency in tackling intricate reasoning tasks, adhering to instructions, and serving as the backbone for widely used AI assistants. Their success is attributed to a consistent enhancement in performance…

In the constantly evolving field of machine learning, particularly in semantic segmentation, the accurate estimation and validation of uncertainty have become increasingly vital. Despite numerous studies claiming advances in uncertainty methods, there remains a disconnection between theoretical development and practical application. Fundamental questions linger, such as whether it is feasible to separate data-related (aleatoric) and…