作者还观察到,在实验室数据集上性能更好,甚至在Penn-action数据集上更好,因为这是一个具有很少动作标签的小数据集。因此,这些结果表明,Vision Language Foundation模型在基本动作(类似于网页动作类别)上表现良好,但在如图2所示的细粒度动作上表现挣扎,仅根据其标签很难区分两个相似的动作。在开放世界设置中进行实验以...
Most existing one-shot skeleton-based action recognition focuses on raw low-level information ( e.g ., joint location), and may suffer from local information loss and low generalization ability. To alleviate these, we propose to leverage text description generated from large language models (LLM)...
Reward model training:采样指令(来自有监督指令集或者人工生成)输入LM,LM生成若干个结果,标注员对这些结果进行排序,从而训练奖励模型 RL fine-tuning:将对齐过程视为一个强化学习过程,预训练LM作为policy,指令作为其输入,其输出指令的回答,action space是所有词表,state是当前已经生成的tokens序列,奖励reward由第二步的...
视频:动作识别 (action recognition) 无语义,回归任务:轨迹预测 (motion forecasting) 2D多模态:2D VQA和图像搜索 (2D VQA and Retrieval) 3D多模态:3D VQA 在这些任务中,我们的模型不只要处理图像上像patch一样的Token,还要处理 点云中无规则的3D点 视频中形状是 T\times H\times W 的长方体形状的token 轨...
17 text = """Artificial Intelligence (AI) simulates human intelligence in machines for tasks like visual perception, speech recognition, and language translation. It has evolved from rule-based systems to data-driven models, enhancing performance through machine learning and deep learning.""" ...
那这个plan具体是什么呢?它可以是一系列用自然语言描述的action或者用编程语言描述的可执行代码等 Plan Generation text-based:通过指令利用LLM去生成执行计划,比如利用ICL的、让LLM在“使用API解决问题”的语料上微调,从而让模型可以调用API、HuggingGPT还让LLM可以调用模型 ...
17 text = """Artificial Intelligence (AI) simulates human intelligence in machines for tasks like visual perception, speech recognition, and language translation. It has evolved from rule-based systems to data-driven models, enhancing performance through machine learning and deep learning.""" ...
Action: the action to take, should be one of the above tools[fire_recognition, fire_alert, call_police, call_fireman] Action Input: theinputto the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can be repeated zeroormore times) ...
视频:动作识别 (action recognition) 无语义,回归任务:轨迹预测 (motion forecasting) 2D多模态:2D VQA和图像搜索 (2D VQA and Retrieval) 3D多模态:3D VQA 在这些任务中,我们的模型不只要处理图像上像patch一样的Token,还要处理 点云中无规则的3D点
Action: the action to take, should be one of the above tools[fire_recognition, fire_alert, call_police, call_fireman] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can be repeated zero or more times) ...