This paper proposes a combined bottom-up and top-down attention mechanism for image captioning and visual question answering (VQA). The bottom-up mechanism uses Faster R-CNN to identify salient image regions, while the top-down mechanism uses task-specific context to determine attention weights. The attention mechanism is applied to image regions at the level of objects and other salient image regions, enabling more natural and effective attention for both tasks. In image captioning, the proposed model achieves state-of-the-art results on the MSCOCO test server, with CIDEr, SPICE, and BLEU-4 scores of 117.9, 21.5, and 36.9, respectively. In VQA, the model achieves first place in the 2017 VQA Challenge with 70.3% overall accuracy on the VQA v2.0 test-standard server. The model uses a soft top-down attention mechanism for captioning, where each feature is weighted based on the existing partial output sequence. The model also incorporates bottom-up attention, which significantly improves performance. The model uses two LSTM layers, with the first layer for top-down attention and the second for language modeling. The model is evaluated on the Visual Genome and Microsoft COCO datasets. The results show that the combined bottom-up and top-down attention mechanism outperforms previous approaches in both image captioning and VQA tasks. The model is also able to handle complex visual and linguistic tasks, such as identifying objects, object attributes, and relationships between objects. The model's attention mechanism is able to focus on both fine details and large image regions, avoiding the conventional trade-off between coarse and fine levels of detail. The model's attention weights are more interpretable, allowing for better understanding of the visual content. The model is also able to handle visual question answering tasks, where it correctly identifies the answer to questions based on the image content. The model's performance is evaluated using standard metrics such as CIDEr, SPICE, and BLEU-4 for image captioning, and accuracy for VQA. The results show that the model outperforms previous approaches in both tasks. The model is also able to handle complex visual and linguistic tasks, such as identifying objects, object attributes, and relationships between objects. The model's attention mechanism is able to focus on both fine details and large image regions, avoiding the conventional trade-off between coarse and fine levels of detail. The model's attention weights are more interpretable, allowing for better understanding of the visual content.This paper proposes a combined bottom-up and top-down attention mechanism for image captioning and visual question answering (VQA). The bottom-up mechanism uses Faster R-CNN to identify salient image regions, while the top-down mechanism uses task-specific context to determine attention weights. The attention mechanism is applied to image regions at the level of objects and other salient image regions, enabling more natural and effective attention for both tasks. In image captioning, the proposed model achieves state-of-the-art results on the MSCOCO test server, with CIDEr, SPICE, and BLEU-4 scores of 117.9, 21.5, and 36.9, respectively. In VQA, the model achieves first place in the 2017 VQA Challenge with 70.3% overall accuracy on the VQA v2.0 test-standard server. The model uses a soft top-down attention mechanism for captioning, where each feature is weighted based on the existing partial output sequence. The model also incorporates bottom-up attention, which significantly improves performance. The model uses two LSTM layers, with the first layer for top-down attention and the second for language modeling. The model is evaluated on the Visual Genome and Microsoft COCO datasets. The results show that the combined bottom-up and top-down attention mechanism outperforms previous approaches in both image captioning and VQA tasks. The model is also able to handle complex visual and linguistic tasks, such as identifying objects, object attributes, and relationships between objects. The model's attention mechanism is able to focus on both fine details and large image regions, avoiding the conventional trade-off between coarse and fine levels of detail. The model's attention weights are more interpretable, allowing for better understanding of the visual content. The model is also able to handle visual question answering tasks, where it correctly identifies the answer to questions based on the image content. The model's performance is evaluated using standard metrics such as CIDEr, SPICE, and BLEU-4 for image captioning, and accuracy for VQA. The results show that the model outperforms previous approaches in both tasks. The model is also able to handle complex visual and linguistic tasks, such as identifying objects, object attributes, and relationships between objects. The model's attention mechanism is able to focus on both fine details and large image regions, avoiding the conventional trade-off between coarse and fine levels of detail. The model's attention weights are more interpretable, allowing for better understanding of the visual content.