I am implementing a published paper, where cellular region needs to be segmented from non-cellular region in a microscopic image of human cells. In the paper LFT coefficients of each pixel are selected as the features. And K-NN is used for segmentation. I am looking for reason, why RGB values are not selected directly as features, and applied K-NN on them? I have tested the same procedure using RGB values, but the segmentation quality was better using LFT features than …

I am building a model for reading receipts from their mobile snapshots. After the receipt is OCR'd, I plan to use a variation on LayoutLM for entity extraction. Entities are: "quantity", "price-per-unit", "product-name", "items-price", etc. What is the best model to consider to link all these entities into a single receipt item, so the final result looks like: "items": [ {"product": ..., "unit_price": ..., "price_paid": ..., "quantity": ..., }, ... ]

In transformers, there is a phase for rasidual connection, where the queries and the output from the attention are add and normalize. Can one please give some advise to the motivation of it? Or maybe I get it wrong? It seems to me that the values shouldn't come from the encoder, the values are the vector that we want to have attention on. And if so. We should have add and normalize the values from the previous state and not …

Let's say we are trying to classify cars into five different categories. For this, we have a lot of samples described by color, brand, model, year of manufacture and so on. For instance, imagine something like this: cars +-----+-------+---------+-------------+--+---+---------------------+ | id | color | brand | model | | year of manufacture | +-----+-------+---------+-------------+--+---+---------------------+ | ... | ... | ... | ... | ... | ... | | 319 | Black | Ferrari | Dino 246 GT | ... | …

In the paper Spatial Transformer Networks, the localization network's output, theta, is differentiable, given the current input feature map. How is this theta differentiable?