Tag: transformer
Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets.- Pix2seq A Language Modeling Framework for Object Detection (28 Sep 2022)
- DreamBooth Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (28 Sep 2022)
- CLIP Learning Transferable Visual Models From Natural Language Supervision (27 Sep 2022)
- MLP-Mixer An all-MLP Architecture for Vision (08 May 2021)
- Transformer Introduction (14 Apr 2021)
- Swin Transformer (11 Apr 2021)
- CVPR 2021 Transformer Paper (11 Apr 2021)
- ViT AN IMAGE IS WORTH 16X16 WORDS TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE (28 Mar 2021)
- End-to-End Object Detection with Transformers (07 Mar 2021)
- Transformer in Computer Vision (03 Feb 2021)