Pre-trained vision models have been foundational to modern-day computer vision advances across various domains, such as image classification, object detection, and image segmentation. There is a rather massive amount of data inflow, creating dynamic data environments that require a continual learning process for our models. New regulations for data privacy require specific information to be…

One of the major hurdles in AI-driven image modeling is the inability to account for the diversity in image content complexity effectively. The tokenization methods so far used are static compression ratios where all images are treated equally, and the complexities of images are not considered. Due to this reason, complex images get over-compressed and…

Latent diffusion models are advanced techniques for generating high-resolution images by compressing visual data into a latent space using visual tokenizers. These tokenizers reduce computational demands while retaining essential details. However, such models suffer from a critical challenge: increasing the dimensions of the token feature increases reconstruction quality but decreases image generation quality. It thus…

Vision Transformers (ViTs) have become a cornerstone in computer vision, offering strong performance and adaptability. However, their large size and computational demands create challenges, particularly for deployment on devices with limited resources. Models like FLUX Vision Transformers, with billions of parameters, require substantial storage and memory, making them impractical for many use cases. These limitations…

Autoregressive (AR) models have changed the field of image generation, setting new benchmarks in producing high-quality visuals. These models break down the image creation process into sequential steps, each token generated based on prior tokens, creating outputs with exceptional realism and coherence. Researchers have widely adopted AR techniques for computer vision, gaming, and digital content…

While multimodal models (LMMs) have advanced significantly for text and image tasks, video-based models remain underdeveloped. Videos are inherently complex, combining spatial and temporal dimensions that demand more from computational resources. Existing methods often adapt image-based approaches directly or rely on uniform frame sampling, which poorly captures motion and temporal patterns. Moreover, training large-scale video…

Multimodal large language models (MLLMs) are advancing rapidly, enabling machines to interpret and reason about textual and visual data simultaneously. These models have transformative applications in image analysis, visual question answering, and multimodal reasoning. By bridging the gap between vision & language, they play a crucial role in improving artificial intelligence’s ability to understand and…

High-resolution, photorealistic image generation presents a multifaceted challenge in text-to-image synthesis, requiring models to achieve intricate scene creation, prompt adherence, and realistic detailing. Among current visual generation methodologies, scalability remains an issue for lowering computational costs and achieving accurate detail reconstructions, especially for the VAR models, which suffer further from quantization errors and suboptimal processing…

Vision-language models (VLMs) have come a long way, but they still face significant challenges when it comes to effectively generalizing across different tasks. These models often struggle with diverse input data types, like images of various resolutions or text prompts that require subtle understanding. On top of that, finding a balance between computational efficiency and…

Large Language Models (LLMs) have demonstrated remarkable potential in performing complex tasks by building intelligent agents. As individuals increasingly engage with the digital world, these models serve as virtual embodied interfaces for a wide range of daily activities. The emerging field of GUI automation aims to develop intelligent agents that can significantly streamline human workflows…