AI Technology & Industry Review
In the new paper 3D-FM GAN: Towards 3D-Controllable Face Manipulation, a team from Princeton University and Adobe Research presents 3D-FM GAN, a novel conditional GAN framework that enables precise 3D-controllable face manipulation with high photorealism and strong identity preservation without requiring any manual tuning or optimizations.
In the new paper Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks, a Microsoft research team presents BEiT-3, a general-purpose state-of-the-art multimodal foundation model for both vision and vision-language tasks that advances the big convergence of backbone architectures, pretraining tasks, and model scaling.
Carnegie Mellon University researchers provide background information and details on contributions to the DialPort project over the last six years in their new paper The DialPort Tools. These tools — such as the DialPort Portal and DialCrowd — will be demoed at the SIGDIAL 2022 conference next month in Edinburgh.
In the new paper Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization, a research team from Microsoft Azure AI and Microsoft Research presents Z-Code++, a novel encoder-decoder pretrained language model optimized for abstractive summarization that significantly improves performance on low-resource summarization tasks.
In the new paper Paint2Pix: Interactive Painting based Progressive Image Synthesis and Editing, a research team from Adobe Research and Australian National University presents paint2pix, a novel model that learns to predict users’ intentions and produce photorealistic images from primitive and coarse human brushstroke inputs.
Colossal-AI team and BioMap open-source their latest solution – xTrimo Multimer for protein monomer and multimer structure prediction. This new solution can predict both monomer and multimer structure simultaneously accelerating the process by up to 11 times!
Google Research and Carnegie Mellon University have open-sourced a library for constructing Python program graph representations used in machine learning for code research. Details are presented in the report A Library for Representing Python Programs as Graphs for Machine Learning.
In the new paper Interactive Code Generation via Test-Driven User-Intent Formalization, a team from Microsoft Research, the University of Pennsylvania, and the University of California, San Diego proposes a workflow for test-driven user-intent formalization that leverages user feedback to generate code that is 90.40 percent consistent with user intent.
In the new paper Semi-supervised Vision Transformers at Scale, a research team from AWS AI Labs proposes a semi-supervised learning pipeline for vision transformers that is stable, reduces hyperparameter tuning sensitivity, and outperforms conventional convolutional neural networks.
In the new paper Learning to Improve Code Efficiency, a research team from the Georgia Institute of Technology and Google Research presents a novel discrete generative latent-variable model designed to help programmers identify more computationally efficient code variants, taking a step toward automating the process of code performance optimization.
In the new paper Few-shot Learning With Retrieval Augmented Language Models, a research team from Meta AI, PSL University, Inria, and University College London presents Atlas, a pretrained retrieval augmented language model that effectively learns new knowledge-intensive tasks under few-shot settings. Atlas outperforms the 540B parameter PaLM model on QA tasks while using 50x fewer parameters.
In the new paper BlenderBot 3: A Deployed Conversational Agent That Continually Learns to Responsibly Engage, researchers from Meta AI and Mila/McGill University release BlenderBot 3, a 175B parameter state-of-the-art open-domain dialogue model deployed on a public website. BlenderBot 3 is designed for continual learning via its user interactions.
A Tencent AI Lab research team introduces Efficient and Intelligent Editing (Effidit), a digital writing assistant that leverages large-scale neural language models to provide high-quality assistance in text completion, error checking, text polishing, keywords to sentences (K2S) and cloud input methods (cloud IME).
In the new paper MinVIS: A Minimal Video Instance Segmentation Framework Without Video-based Training, an NVIDIA research team presents MinVIS, a minimal video instance segmentation framework that outperforms state-of-the-art VIS approaches without requiring video-based training.
In the new paper TextWorldExpress: Simulating Text Games at One Million Steps Per Second, a research team from the University of Arizona and Microsoft Research Montréal presents TextWorldExpress, a high-performance text-game simulator that boosts throughput by approximately three orders of magnitude, reaching one million steps per second.
In the new paper Efficient Training of Language Models to Fill in the Middle, an OpenAI research team shows that causal decoder-based autoregressive (AR) language models can learn to infill texts via a very simple and straightforward transformation to the training data and without any architectural modifications.
In the new paper Is Attention All NeRF Needs?, a research team from the Indian Institute of Technology Madras and the University of Texas at Austin proposes Generalizable NeRF Transformer (GNT), a pure and universal transformer-based architecture for efficient on-the-fly reconstruction of NeRFs. The work demonstrates that a pure attention mechanism suffices for learning a physically-grounded rendering process.
In the new paper Face Deblurring Using Dual Camera Fusion on Mobile Phones, a Google team proposes a novel dual camera fusion technique that achieves robust face deblurring in diverse motion and lighting conditions.