这是因为few-shot场景下数据较少,若不固定LM的话,反而会破坏LM原有的信息,甚至使模型产生灾难性遗忘。 训练时的输入是形如(image, text)的一堆pair,但是few-shot场景下可能会输入多个(image, text)pair,所以在位置编码上使用了相对位置编码,从而保证image相对于text是在其前面的。 Experiments 主要从...
Frozen achieves the necessary capacities to some degree, but a key limitation is that it achieves far from state-of-the-art performance on the specific tasks that it learns in a few shots, compared to systems that use the full training set for those tasks. As such, the main contribution o...
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Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models Zhiqiu Lin* Samuel Yu* Zhiyi Kuang Deepak Pathak Carnegie Mellon University {zhiqiul,samuelyu,zkuang,dpathak,deva}@cs.cmu.edu Deva Ramanan Abstract The ability to quickly learn...
When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vis...
MedVINT Zhang et al. (2023b), a visual instruction-tuned VLM based on Llama. As this model was not designed to do few-shot learning (e.g. the image information is prepended to the overall input), we report two modes for MedVINT: zero-shot and fine-tuned, where the model was fine...
(images and text), to generate text conditioned on this multimodal input. Building on the success of Flamingo, which was among the first vision-language models to exhibit in-context learning and few-shot learning abilities, Med-Flamingo extends these capabilities to the medical domain by pre-...
一个大一统的NLP处理框架T5打破Few-shot Learning的次元壁垒!预训练模型 1973 18 1:36:27 App 膜拜!同济大佬两小时教会了我目标检测算法YOLOv8+YOLO-world,由浅入深讲解算法原理及论文知识点! 1892 16 2:18:43 App 大模型时代必学!商汤大佬一小时精讲大语言模型推理加速实战,高性能LLM推理框架及细节优化全...
few-shot learningThe ability to interpret multimodal data, and map the targets and anomalies within, is important for an automatic recognition system. Due to the expensive and time-consuming nature of multimodal time-series data annotation in the training stage, multimodal time-series image ...
CLIP was the first model that could generalize to multipleimage classification taskswith zero- and few-shot learning. Flamingo wasn’t the first large multimodal model that couldgenerate open-ended responses(Salesforce’s BLIPcame out 3 months prior). However, Flamingo’s strong performance prompted...