7 accepted papers
Semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery is critical for applications like environmental monitoring and urban planning but faces com- putational and optimization challenges. Conventional methods either lose fine details through downsampling or fragment global conte
Geometric spatial reasoning forms the foundation of many applications in artificial intelligence, yet the ability of large language models (LLMs) to operate over geometric spatial information expressed in procedural code remains underexplored. In this paper, we address this gap by formalizing the \t…
Existing Vision-Language-Action (VLA) models predominantly rely on explicit Chain-of-Thought (CoT) reasoning to bridge perception and action. While effective, this paradigm suffers from high computational costs and error propagation in multi-step tasks. In this paper, we propose Adaptive Variable Al…
Recent advancements in Large Language Models (LLMs) have shifted from explicit Chain-of-Thought (CoT) reasoning to more efficient latent reasoning, where intermediate thoughts are represented as vectors rather than text. However, latent reasoning can be brittle on challenging, out-of-distribution ta…
Semantic segmentation of ultra-high resolution images is more challenging than ordinary images since high-resolution images need to be cropped into patches in training due to GPU memory limitation. To solve this problem, we design a multibranch structure to deal with multi-resolution inputs, called…
As a special kind of signal processing technology, image processing has been developed rapidly after the appearance of convolutional neural network (CNN). At present, the semantic segmentation methods are all based on CNN and ignore the advantages of traditional image processing technology. We combi…
It is easy to obtain multi-tasking models from open source platforms or various organizations. However, using these models at the same time will bring a great burden on storage and reduce computing efficiency. In this paper, we propose a transformation-based multi-task fusion method, called transfor…