Tag: bert

BERT, which stands for Bidirectional Encoder Representations from Transformers, is a state-of-the-art natural language processing model in the field of deep learning. It's based on the Transformer architecture and is pre-trained on a large corpus of text data, enabling it to understand context and semantics in a bidirectional manner, which means it considers both the left and right context of a word when processing text. BERT has significantly improved the performance of various NLP tasks, such as text classification, question-answering, and sentiment analysis, and has become a foundational model for many NLP applications. Researchers and developers often fine-tune BERT for specific language understanding tasks, making it a versatile tool for a wide range of natural language processing challenges.

• PaLI-3 Vision Language Models Smaller, Faster, Stronger (15 Oct 2023)
  

  This is reading note for PaLI-3 Vision Language Models: Smaller, Faster, Stronger. This paper proposes to use image-text-matching to replace contrast loss. The experiment indicates this method is especially effective in relatively small models.

• Align before Fuse Vision and Language Representation Learning with Momentum Distillation (11 Oct 2023)
  

  This is my reading note for Align before Fuse: Vision and Language Representation Learning with Momentum Distillation. The paper proposes a multi modality model which is trained base on contrast loss, mask language modeling and image-text match. To handle noisy pairs of text and image, it track moving average of model and distill to the final model.

• RoBERTa A Robustly Optimized BERT Pretraining Approach (07 Oct 2023)
  

  This is my reading note for RoBERTa: A Robustly Optimized BERT Pretraining Approach. This paper revisits the design choice of BERT. It provides that 1) adding more data; 2) using larger batch size; 3) training for more iterations could significantly improves the performance. In addition, using longer sentence/context could also improve performance and next sentence prediction is no longer useful.

• MaMMUT A Simple Architecture for Joint Learning for MultiModal Tasks (24 Sep 2023)
  

  This is my reading note for MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks. The paper proposes an efficient multi modality model. it proposes to unify generative loss (masked language modeling) and contrast loss via a two pass training process. One pass is for generate loss which utilizes casual attention model in text decoder and the other pass is bidirectional text decoding. The order of two passes are shuffled during the training.

• Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone (22 Sep 2023)
  

  This is my reading note for Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone. This papers propose a two-stage pre-training strategy: (i) coarse-grained pre-training based on image-text data; followed by (ii) fine-grained pre-training based on image-text-box data.

• An Empirical Study of Training End-to-End Vision-and-Language Transformers (21 Sep 2023)
  

  This is my reading note for An Empirical Study of Training End-to-End Vision-and-Language Transformers. This paper provides a good review and comparison of multi modality (video and text) model’s design choice.

• SeamlessM4T-Massively Multilingual & Multimodal Machine Translation (05 Sep 2023)
  

  This is my reading note 2/2 on SeamlessM4T-Massively Multilingual & Multimodal Machine Translation. It is end to end multi language translation system supports multimodality (text and audio). This paper also provides a good review on machine translation. This note focus on data preparation part of the paper and please read SeamlessM4T-data for the other part.

• SeamlessM4T-Massively Multilingual & Multimodal Machine Translation (04 Sep 2023)
  

  This is my reading note 1/2 on SeamlessM4T-Massively Multilingual & Multimodal Machine Translation. It is end to end multi language translation system supports multimodality (text and audio). This paper also provides a good review on machine translation. This note focus on data preparation part of the paper and please read SeamlessM4T-model for the other part.

• Multimodal Learning with Transformers A Survey (02 Sep 2023)
  

  This is my reading note on Multimodal Learning with Transformers A Survey. This a paper provides a very nice overview of the transformer based multimodality learning techniques.

• CoCa Contrastive Captioners are Image-Text Foundation Models (31 Jul 2023)
  

  This is my reading note for CoCa: Contrastive Captioners are Image-Text Foundation Models. The paper proposes a multi modality model, especially it models the problem as image caption as well as text alignment problem. The model contains three component: a vision encoder, a text decoder (which generates text embedding ) and a multi modality decoder , which generate caption given image and text embedding.

• FLAVA A Foundational Language And Vision Alignment Model (30 Jul 2023)
  

  This is my reading note for FLAVA: A Foundational Language And Vision Alignment Model. This paper proposes a multi modality model. Especially, the model not only work across modality, but also on each modality and joint modality. To do that, it contains loss functions for both within modality but also across modality. It also proposes to use the same architecture for vision encoder, Text encoder as well as multi -modality encoder.

• AudioGen Textually Guided Audio Generation (24 Jul 2023)
  

  This is my reading note for AudioGen: Textually Guided Audio Generation. This paper propose to use auto regressive model to generate audio condition on text. The audio presentation is based on sound stream on neural sound.

• Make-An-Audio Text-To-Audio Generation with Prompt-Enhanced Diffusion Models (23 Jul 2023)
  

  This is my reading note for Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models. This paper proposes a diffusion model for audio, which uses an auto encoder to convert audio signal to a spectrum which could be natively handled by latent diffusion method.

• Qwen-VL A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond (09 Jul 2023)
  

  This is my reading note for Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond. This paper proposes a vision-language model capable of vision grounding and image text reading. To do that, it considers visual grounding and OCR tasks in pre-training. In architecture, the paper uses Qformer from BLIP2.

• ELECTRA Pre-training Text Encoders as Discriminators Rather Than Generators (09 Jul 2023)
  

  This is my reading note ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. This paper proposes to replace masked language modeling with the discriminator task of whether the token is from the authentic data distribution or fixed by the generator model. Especially the model contains a generator that’s trained with masked language modeling objects and discriminator to classify whether a token is filled by the generator or not.

• PaLI A Jointly-Scaled Multilingual Language-Image Model (08 Jul 2023)
  

  This is my reading note for PaLI: A Jointly-Scaled Multilingual Language-Image Model. This paper formulates all the image-text pretraining tasks as visual question answering. The major contributions of this paper includes 1) shows balanced size of vision model and language model improves performances; 2) training with mixture of 8 tasks is important.

• What Does BERT Look At? An Analysis of BERT's Attention (28 Jun 2023)
  

  This is my reading note for What Does BERT Look At? An Analysis of BERT’s Attention. This paper studies the attention map of Bert.it found that the attention map captures information such as syntax and co reference . It also found there is a lot of redundancy in heads of the same layer.

• UNITER UNiversal Image-TExt Representation Learning (24 Jun 2023)
  

  This is my reading note for UNITER: UNiversal Image-TExt Representation Learning. This paper proposes a vision language pre training model. The major innovation here is it studies the work region alignment loss as well as different mask region models task.

• Must-read AI Papers (16 Feb 2021)
  

  I will create a new reading note series based on Must-read AI Papers from Crossminds.ai.