• GenHFi: Generating high fidelity images with subscale pixel networks and multidimensional upscaling. (ICLR2019)
• paper2: Scaling autoregressive video models (ICLR2020)
• Video pixel networks. (CoRR2016)
• Parallel: Parallel multiscale autoregressive density estimation
• VQGAN: Taming Transformers for High-Resolution Image Synthesis
• TeCoGAN: Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
• ImaGINator: Conditional Spatio-Temporal GAN for Video Generation
• Temporal Shift GAN for Large Scale Video Generation
• MoCoGAN: Decomposing Motion and Content for Video Generation
• Playable Video Generation (CVPR2021)

• VQ-VAE: Neural Discrete Representation Learning (NIPS2017)
• VQ-VAE2: Generating Diverse High-Resolution Images with VQ-VAE-2
• DALL-E: Zero-Shot Text-to-Image Generation
• VideoGPT: Video Generation using VQ-VAE and Transformers
• LVT: Latent Video Transformer
• Feature Quantization Improves GAN Training (ICML2020)
• DVT-NAT: Fast Decoding in Sequence Models Using Discrete Latent Variables (ICML2018)
• NWT: Towards natural audio-to-video generation with representation learning
• NUWA: Visual Synthesis Pre-training for Neural visUal World creAtion

paper list:

• CLIP: Learning Transferable Visual Models From Natural Language Supervision
• ActionCLIP: A New Paradigm for Video Action Recognition
• CLIP-It! Language-Guided Video Summarization

paper list to read:

• Everybody Sign Now: Translating Spoken Language to Photo Realistic Sign Language Video

• Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers andMixture Density Networks

• Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives

• Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production

• Ben Saunders, Necati Cihan Camgoz, and Richard Bowden

• Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses

• BABEL: Bodies, Action and Behavior with English Labels, CVPR2021

• Fingerspelling Detection in American Sign Language, CVPR2021

• American Sign Language fingerspelling recognition in the wild. SLT2018

• Fingerspelling recognition in the wild with iterative visual attention. ICCV2019

paper list:

• An efficient framework for learning sentence representations.
• CLEAR: Contrastive Learning for Sentence Representation
• Declutr: Deep contrastive learn- ing for unsupervised textual representations. arXiv
• SimCSE: Simple Contrastive Learning of Sentence Embeddings
• R-Drop: R-Drop: Regularized Dropout for Neural Networks
• Coco-lm: Correcting and contrasting text sequences for language model pretraining.
• Learning dense representations of phrases at scale.
• An unsupervised sentence embedding method by mutual information maximization.
• Representation degeneration problem in training natural language generation models
• CosReg: Improving neural language generation with spectrum control.
• CLAPS: Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
• LINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding
• Adversarial PerturbationInterpretable Adversarial Perturbation in Input Embedding Space for Text
• Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
• Unsupervised Data Augmentation for Consistency Training