Distinguishing fine image boundaries, particularly in noisy or low-resolution scenarios, remains formidable. Traditional approaches, heavily reliant on human annotations and rasterized edge representations, often need more precision and adaptability to diverse image conditions. This has spurred the development of new methodologies capable of overcoming these limitations. A significant challenge in this domain is the robust…

In the domain of computer vision, particularly in video-to-video (V2V) synthesis, maintaining temporal consistency across video frames has been a persistent challenge. Achieving this consistency is crucial for synthesized videos’ coherence and visual appeal, which often combine elements from varying sources or modify them according to specific prompts. Traditional methods in this field have heavily…

Raw and frequently unlabeled data can be retrieved and organized using representation learning. The ability of the model to develop a good representation depends on the quantity, quality, and diversity of the data. In doing so, the model mirrors the data’s inherent collective intelligence. The output is directly proportional to the input. Unsurprisingly, the most…

A promising new development in artificial intelligence called MobileVLM, designed to maximize the potential of mobile devices, has emerged. This cutting-edge multimodal vision language model (MMVLM) represents a major advancement in incorporating AI into common technology since it is built to function effectively in mobile situations. Researchers from Meituan Inc., Zhejiang University, and Dalian University…

Image restoration is a complex challenge that has garnered significant attention from researchers. Its primary objective is to create visually appealing and natural images while maintaining the perceptual quality of the degraded input. In cases where there is no information available concerning the subject or degradation (blind restoration), having a clear understanding of the range…

Neural graphics primitives (NGP) are promising in enabling the smooth integration of old and new assets across various applications. They represent images, shapes, volumetric and spatial-directional data, aiding in novel view synthesis (NeRFs), generative modeling, light caching, and various other applications. Notably successful are the primitives representing data through a feature grid containing trained latent…

Object segmentation across images and videos is a complex yet pivotal task. Traditionally, this field has witnessed a siloed progression, with different tasks such as referring image segmentation (RIS), few-shot image segmentation (FSS), referring video object segmentation (RVOS), and video object segmentation (VOS) evolving independently. This disjointed development resulted in inefficiencies and an inability to…

The development of multimodal large language models (MLLMs) represents a significant leap forward. These advanced systems, which integrate language and visual processing, have broad applications, from image captioning to visible question answering. However, a major challenge has been the high computational resources these models typically require. Existing models, while powerful, necessitate substantial resources for training…

The conventional NeRF and its variations demand considerable computational resources, often surpassing the typical availability in constrained settings. Additionally, client devices’ limited video memory capacity imposes significant constraints on processing and rendering extensive assets concurrently in real-time. The considerable demand for resources poses a crucial challenge in rendering expansive scenes in real-time, requiring rapid loading…

Integrating multimodal data such as text, images, audio, and video is a burgeoning field in AI, propelling advancements far beyond traditional single-mode models. Traditional AI has thrived in unimodal contexts, yet the complexity of real-world data often intertwines these modes, presenting a substantial challenge. This complexity demands a model capable of processing and seamlessly integrating…