arxiv: https://arxiv.org/abs/2205.11916 key idea add “let’s think step by step” as zero-shot prompt to LLM input to get better results! Background and Proposed Method These days the paradigm for using large language models(LLM) are not transfer learning but rather using prompts. This is because due to its huge size its impractical to trying finetuning it to…

arxiv: https://arxiv.org/abs/2202.01197 key points presents VOS, a novel framework for OOD detection generate virutal outlier in latent space and use it in training, helping the model to discriminate between ID(in-distribution) and OOD(out-of-distribution) data adding a new loss that discriminates OOD / ID data during training, the model’s object detection performance can be…

arxiv: https://arxiv.org/abs/2111.15664 Key Points visual document understanding model which does OCR + downstream task in one step with a single end-to-end model outputs are generative, and formatted to be convertible to JSON, which makes this architecture highly compatible to various downstream tasks. present SynthDoG, a synthetic document image generator used in this…

arxiv: https://arxiv.org/abs/1910.13461 key points propose autoregressive model named BART, which is architecturally similar to standard transformer encoder + decoder Check out 5 pretraining tasks, and experiment which pretraining task is most helpful test BART performance with large scale pretraining on downstream tasks Model Architecture This work introduces BART, which is fundamentally nearly identical to…

arxiv: https://arxiv.org/abs/2106.11539 this work proposes a backbone for visual document understanding domain. It uses text, visual, spatial features. Key points use text, visual, spatial features at each encoding layer, keep feeding in visual and spatial features on the input side. This has the ‘residual’ connection effect. text and visual features are processed…

arxiv: https://arxiv.org/abs/2012.14740 LayoutLMv2 is a work improved from LayoutLM. This line of work focues on creating a model for document understanding. The main task of this genre is to extract useful information when a document is given. In this case, it tries to extract information when a document image is…

arxiv: https://arxiv.org/abs/2108.04539 key points use text and spatial information. doesn’t utilize image feature a better spatial information encoding method compared to LayoutLM propose new pretraining task: Area Masked Language Model spatial information encoding method For each text box, get four corner point x,y coordinates and normalize them all with image width/height. For each corner, calculate relative…

arxiv: https://arxiv.org/abs/2107.14795 Key points developing upon the Perceiver idea, Perceiver IO proposes a Perceiver like structure but where output size can be much larger and still keep overall complexity linear. (Checkout summary on Perceiver here) same with Perceiver, this work use latent array to save input information and run this through multiple…

arxiv: https://arxiv.org/abs/2103.14030 Key points multi scale feature extraction. Could think of as adoption of FPN idea. restrict transformer operation to within each window and not the entire feature map → allows to keep overall complexity linear instead of quadratic apply shifted window to allow inter-window interaction fuse relative position information in self…