Hey Folks!

This newsletter will discuss some cool AI research papers and AI tools. Happy learning!

👉 What is Trending in AI/ML Research? 

➡️ Can Video Segmentation Be More Cost-Effective? Meet DEVA: A Decoupled Video Segmentation Approach that Saves on Annotations and Generalizes Across Tasks

This paper introduces a Decoupled Video Segmentation Approach (DEVA) to tackle this challenge. DEVA is composed of task-specific image-level segmentation and a class/task-agnostic bi-directional temporal propagation. This design allows for the use of an image-level model for the target task, which is cheaper to train, alongside a universal temporal propagation model that generalizes across tasks. By using bi-directional propagation, the system fuses segmentation hypotheses from different frames to generate a coherent segmentation. The paper demonstrates that DEVA performs favorably compared to end-to-end approaches in various data-scarce tasks, such as large-vocabulary video panoptic and open-world video segmentation.

➡️  Meet Baichuan 2: A Series of Large-Scale Multilingual Language Models Containing 7B and 13B Parameters, Trained from Scratch, on 2.6T Tokens

How can we address the limitations of existing large language models, which are often closed-source and less capable in languages other than English? This technical report introduces Baichuan 2, a large-scale, multilingual language model with up to 13 billion parameters, trained on a massive dataset of 2.6 trillion tokens. Unlike other models that might be closed-source or English-centric, Baichuan 2 is designed to be multilingual and performs at par or better than comparable open-source models across various benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Notably, Baichuan 2 also demonstrates expertise in specialized fields such as medicine and law. The team plans to release all pre-training model checkpoints, thereby contributing to the broader research community.

➡️  Researchers from the University of Maryland and Meta AI Propose OmnimatteRF: A Novel Video Matting Method that Combines Dynamic 2D Foreground Layers and a 3D Background Model

How can video matting methods better represent complicated, real-world scenes, especially when traditional techniques are limited to 2D background layers? This paper proposes OmnimatteRF, an innovative video matting approach that combines dynamic 2D foreground layers with a 3D background model. Unlike existing methods, which primarily focus on 2D background representations, OmnimatteRF leverages the power of 3D modeling to reconstruct complex scenes. The 2D layers are dedicated to capturing detailed information of foreground objects, while the 3D background model handles the intricacies of real-world environments. Through extensive experiments on various videos, the paper demonstrates that OmnimatteRF outperforms existing methods in terms of scene reconstruction quality.

➡️ Meet AudioSR: A Plug & Play and One-for-All AI Solution for Upsampling Audio to Incredible 48kHz Quality

How can we improve the quality of audio signals across a wide range of types and bandwidth settings? This paper introduces AudioSR, a diffusion-based generative model designed for robust audio super-resolution. Unlike previous methods that are limited to specific audio types and bandwidths, AudioSR is versatile and capable of handling sound effects, music, and speech. It can upsample audio signals within a bandwidth range of 2kHz to 16kHz to a high-resolution 24kHz bandwidth signal with a sampling rate of 48kHz. Objective evaluations show that AudioSR outperforms benchmarks, and subjective tests confirm its utility as a plug-and-play module to enhance other audio generative models like AudioLDM, Fastspeech2, and MusicGen.

👉 What is Trending in AI Tools? 

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