Modality Encoder:负责将不同模态的输入数据编码为模型可理解的表示, 目前技术可以实现输入图片、视频、音频文件,对于图像而言,可能涉及到将像素 数据转换成一个特征向量,该向量捕捉了图像中的重要信息;Input Projector:将不同模态的输入数据映射到共享的语义空间,这意味着 无论输入数据的形式如何,它们都会被转换成一个...
第二个阶段则是基于Transformer按照自回归的方式对图片的token进行预测,同时用d-VAE的Decoder生成图片,其中对文本用BPE进行tokenization得到256个token,和 32\times32=1024 个图像token连接在一起,通过Transformer按照自回归方式进行概率建模。 文中提到了对图像 x 、文本caption y 和图片对应的token z 的联合分布概率进...
因此在visual encoder部分,他们选择的是一层简单的从头学习的linear projection,尽可能地不引入任何bias,只是承担feature dimension一致和简单地对齐功能。如果把linear projection层看成mllm里的projector,那也可以把Fuyu理解为不具有vision encoder的多模态大模型 Fuyu-8B 结构 由于无需pretrained vision encoder,因此摆脱了...
self.multi_modal_projector = LlavaMultiModalProjector( vision_hidden_size=config.vision_config.hidden_size, text_hidden_size=config.text_config.hidden_size, projector_hidden_act=config.projector_hidden_act) self.quant_config = quant_config self.language_model = LlamaModel(config.text_config, cache...
( model, image_size = 256, hidden_layer = 'to_latent', # hidden layer name or index, from which to extract the embedding projection_hidden_size = 256, # projector network hidden dimension projection_layers = 4, # number of layers in projection network num_classes_K = 65336, # output ...
Decoder和Encoder 一、什么是Decoder和Encoder 在Netty里面,有四个核心概念,它们分别是: Channel:一个客户端与服务器通信的通道。 ChannelHandler:业务逻辑处理器, 通常情况下,业务逻辑都是存在于ChannelHandler之中。 ChannelInboundHandler:输入 sed java对象 封装 泛型 子类 转载 mob604756f44f2a 2020-01-19 ...
Output Scaling— Video scaling at the output (decoder) allows seamless switching from any source, at any resolution, to any display or projector, while preserving video fidelity Infrared (IR)— Infrared emitter connection allows control of low-cost, IR-only display devices Onboard Control— All ...
Onboard Control– All N-Series encoders and decoders have on-board, built-in control capability via events that can trigger any number of TCP/UDP commands to other IP controllable devices Unmatched Flexibility– Highly competitive pricing for matrices up to 32x32 ...
( model, image_size = 256, hidden_layer = 'to_latent', # hidden layer name or index, from which to extract the embedding projection_hidden_size = 256, # projector network hidden dimension projection_layers = 4, # number of layers in projection network num_classes_K = 65336, # output ...
( model, image_size = 256, hidden_layer = 'to_latent', # hidden layer name or index, from which to extract the embedding projection_hidden_size = 256, # projector network hidden dimension projection_layers = 4, # number of layers in projection network num_classes_K = 65336, # output ...