5 accepted papers
Large Multi-modal Models (LMMs), trained on web-scale datasets predominantly composed of natural images, have demonstrated remarkable performance on general tasks. However, these models often exhibit limited specialized capabilities for domain-specific tasks that require extensive domain prior knowl…
Despite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry Problem Solving (GPS), which demands understanding diagrams, interpreting symbols, and performing complex reasoning. This limitation arises from their pre-training on natural images…
Transformer-based architectures have been proven successful in detecting 3D objects from point clouds. However, the quadratic complexity of the attention mechanism struggles to encode rich information as point cloud resolution increases. Recently, state space models (SSM) such as Mamba have gained g…
Recent progress in Large Multimodal Models (LMM) has opened up great possibilities for various applications in the field of human-machine interactions. However developing LMMs that can comprehend reason and plan in complex and diverse 3D environments remains a challenging topic especially considerin…
"Recently, the understanding of the 3D world has garnered increased attention, facilitating autonomous agents to perform further decision-making. However, the majority of existing 3D vision-language datasets and methods are often limited to specific tasks, limiting their applicability in diverse sce…