6 accepted papers
Recent advances in vision-language models (VLMs) and reinforcement learning (RL) have driven progress in GUI automation. However, most existing methods rely on static, one-shot visual inputs and passive perception, lacking the ability to adaptively determine when, whether, and how to observe the int
Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex problems. However, existing benchmarks for evaluating LLMs’ tool usage face several limitations: (1) limited evaluation
Human-robot interaction plays a critical role in scientific experiments by ensuring efficient and reliable execution of experimental tasks. To achieve successful task completion, robots must adapt in real time to unexpected task variations, external disturbances, and safety constraints. In this work
Vision transformer has emerged as a new paradigm in computer vision, showing excellent performance while accompanied by expensive computational cost. Image token pruning is one of the main approaches for ViT compression, due to the facts that the complexity is quadratic with respect to the token num…
Learning subtle representation about object parts plays a vital role in fine-grained visual recognition (FGVR) field. The vision transformer (ViT) achieves promising results on computer vision due to its attention mechanism. Nonetheless, with the fixed size of patches in ViT, the class token in deep…
We introduce a Power-of-Two post-training quantization( PTQ) method for deep neural network that meets hardware requirements and does not call for long-time retraining. PTQ requires a small set of calibration data and is easier for deployment, but results in lower accuracy than Quantization-Aware Tr…