("bert-base-cased", "bert-base-cased")and fine-tune the model. This means especially the decoder weights have to be adapted a lot, since in the EncoderDecoder framework the model has a causal mask and the cross attention layers are to be trained from scratch. The results so far are ...
Secondly, a cross-layer attention fusion (CAF) module is proposed to capture multiscale features by integrating channel information and spatial information from different layers of the feature maps. Lastly, a bidirectional attention gate (BAG) module is constructed within th...
To this end, we analyze a text-conditioned model in depth and observe that the cross-attention layers are the key to controlling the relation between the spatial layout of the image to each word in the prompt. With this observation, we present several applications which monitor the image ...
Motivated by this, we devise a cross-layer attention mechanism, called multi-head recurrent layer attention (MRLA), that sends a query representation of the current layer to all previous layers to retrieve query-related information from different levels of receptive fields. A light-weighted version...
We approach this problem by introducting a perceiver-resampler network with gated cross attention layers and a mapping network between the frozen encoder and the frozen generator. We provide more details on the model architecture as well as how to setup and run the project in the sections below...
Table 4:Ablation study by changing attention layers on all the datasets (accuracy in %) Table 4 denotes the performance in presence of various attention layers as described in the network, demonstrating the need for all three attention modules. ...
The baseline transformer decoder-based models use a standard transformer decoder [27] with cross-attention layers, while our ViTSTR-Transducer models use a modified transformer decoder without cross-attention layers. All models use a DeiT-Small backbone [30] as a 2D feature extractor since it is...
(MTL), which enables automatic feature fusing at every layer from different tasks. This is in contrast with the most widely used MTL CNN structures which empirically or heuristically share features on some specific layers (e.g., share all the features except the last convolutional layer). The ...
# 假设image_features和text_features分别是提取的图像和文本特征 def stacked_cross_attention(image_features, text_features, num_layers): for _ in range(num_layers): # 计算注意力权重 attention_weights = compute_attention_weights(image_features, text_features) # 加权求和得到新的特征 new_image_feature...
prompt_edit_token_weights=[]values to scale the importance of the tokens in cross attention layers, as a list of tuples representing(token id, strength), this is used to increase or decrease the importance of a word in the prompt, it is applied toprompt_editwhen possible (ifprompt_editis...