Existing solutions often resort to down-sampling operations, such as pooling, to reduce computational cost. Unfortunately, such operations are non-invertible and can result in information loss.

For instance, web images retrieved with queries of " tiger cat " (a cat species) and " drumstick " (a musical instrument) are almost dominated by images of tigers and

TBAL is emerging as a widely-used solution in practice.

We then annotated ten thousand WBC images with these attributes, resulting in 113k labels (11 attributes x 10.3k images). Annotating at this level of detail and scale is unprecedented, offering unique value to AI in pathology. Moreover, we conduct experiments to predict these attributes from cell images, and also demonstrate specific applications that can benefit from our detailed annotations.

The vast amount of data required to model wildfires at a larger scale presents difficulties in the collection and curation of the data.

Fig. S1 displays the full set of region labels, corresponding to Regions including significant voxels from Fig.3(a) in the main text are As detailed in Section 3.1 of the main text, our model underwent a three-stage training process. After this stage, we conducted a fine-tuning process using the learned fixations from the WhereCNN. In this stage, the WhereCNN, after the pre-training in Stage 1, was incorporated to guide the WhatCNN's fixations. The model samples fixations from the predicted saliency maps from WhereCNN. As indicated in Section 3.1 of the main text, we utilized For All Stages All training stages were conducted using four NVIDIA A40 GPUs. Figure S2: Process of determining the next fixation point given the current fixation.