[Paper Review] Transformers
Published:
Transformers is a deep learning architecture that enhances natural language processing by using self-attention mechanisms to capture long-range dependencies and contextual relationships in text.
Posts by Tags
Attention
Autoencoder
[Paper Review] VAE (Variational AutoEncoder)
Published:
Variational Autoencoders (VAEs) employ a probabilistic approach to latent variable modeling, optimizing a variational lower bound to perform efficient approximate posterior inference and learning of generative models with continuous latent variables.
Computer Vision
[Paper Review] Segment Anything Model 2 (SAM2)
Published:
SAM2 generalizes promptable visual segmentation to video by integrating spatio-temporal memory, interactive prompting, and a data engine for fine-grained, efficient, and class-agnostic object segmentation across frames.
Decoder
[Paper Review] Transformers
Published:
Transformers is a deep learning architecture that enhances natural language processing by using self-attention mechanisms to capture long-range dependencies and contextual relationships in text.
Diffusion
[Paper Review] Stable Diffusion & SDXL
Published:
SDXL extends Stable Diffusion with a larger U-Net backbone, multi-scale generation, and flexible text conditioning, enabling high-resolution, semantically rich image synthesis across diverse prompts and resolutions.
[Paper Review] VAE (Variational AutoEncoder)
Published:
DDIM (Denoising Diffusion Implicit Models) is a generative model for efficient image generation through a refined diffusion denoising process.
Encoder
[Paper Review] Transformers
Published:
Transformers is a deep learning architecture that enhances natural language processing by using self-attention mechanisms to capture long-range dependencies and contextual relationships in text.
[Paper Review] BERT
Published:
BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model that improves natural language understanding by pre-training on vast amounts of text to capture context from both directions.
Generative Models
[Paper Review] Stable Diffusion & SDXL
Published:
SDXL extends Stable Diffusion with a larger U-Net backbone, multi-scale generation, and flexible text conditioning, enabling high-resolution, semantically rich image synthesis across diverse prompts and resolutions.
[Paper Review] VAE (Variational AutoEncoder)
Published:
Variational Autoencoders (VAEs) employ a probabilistic approach to latent variable modeling, optimizing a variational lower bound to perform efficient approximate posterior inference and learning of generative models with continuous latent variables.
[Paper Review] VAE (Variational AutoEncoder)
Published:
DDIM (Denoising Diffusion Implicit Models) is a generative model for efficient image generation through a refined diffusion denoising process.
Image Segmentation
[Paper Review] Segment Anything Model 2 (SAM2)
Published:
SAM2 generalizes promptable visual segmentation to video by integrating spatio-temporal memory, interactive prompting, and a data engine for fine-grained, efficient, and class-agnostic object segmentation across frames.
NLP
[Paper Review] Transformers
Published:
Transformers is a deep learning architecture that enhances natural language processing by using self-attention mechanisms to capture long-range dependencies and contextual relationships in text.
[Paper Review] BERT
Published:
BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model that improves natural language understanding by pre-training on vast amounts of text to capture context from both directions.
Transformers
[Paper Review] UMT (Unified Multimodal Transformers)
Published:
UMT is a unified framework for video highlight detection and moment retrieval that flexibly integrates visual, audio, and optional text modalities to identify key moments in both query-based and query-free scenarios.
[Paper Review] Transformers
Published:
Transformers is a deep learning architecture that enhances natural language processing by using self-attention mechanisms to capture long-range dependencies and contextual relationships in text.
[Paper Review] BERT
Published:
BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model that improves natural language understanding by pre-training on vast amounts of text to capture context from both directions.
Video Highlight Detection
[Paper Review] UMT (Unified Multimodal Transformers)
Published:
UMT is a unified framework for video highlight detection and moment retrieval that flexibly integrates visual, audio, and optional text modalities to identify key moments in both query-based and query-free scenarios.
Video Segmentation
[Paper Review] Segment Anything Model 2 (SAM2)
Published:
SAM2 generalizes promptable visual segmentation to video by integrating spatio-temporal memory, interactive prompting, and a data engine for fine-grained, efficient, and class-agnostic object segmentation across frames.
Video Understanding
[Paper Review] SlowFast Networks for Video Recognition
Published:
The SlowFast network employs dual pathways, with the Slow Pathway capturing high-resolution spatial details and the Fast Pathway capturing rapid temporal changes, to achieve advanced video recognition.
[Paper Review] End-to-End Object Detection with Transformers (DETR)
Published:
DETR revolutionizes object detection by integrating the Transformer architecture’s global attention mechanism with CNN-extracted image features, utilizing a novel bipartite matching algorithm to enhance detection accuracy and efficiency across varied object scales.