🔥 Trending AI Research: Let’s learn something new from the trending papers.

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🔥Trending AI Research

1️⃣ UC Berkeley Researchers Introduce Nerfstudio: A Python Framework for Neural Radiance Field (NeRF) Development  [Paper]

Rapid development in NeRF has led to many research papers being published but tracking the progress of this is difficult due to a lack of code consolidation. Many papers implement features in their own siloed repository which complicates the process of transferring features and research contributions across different implementations. To resolve this issue, researchers at Berkley present consolidated NeRF innovatives as Nerfstudios. The major goals of Nerfstudios are to consolidate various NeRF techniques into reusable, modular components and enable real-time visualization of NeRF scenes with a rich suite of controls. This will provide an easy-to-use workflow for creating NeRFs from user-captured data.

2️⃣ Meet Med-PaLM Multimodal (Med-PaLM M): A Large Multimodal Generative Model that Flexibly Encodes and Interprets Biomedical Data [Paper]

Researchers have been putting in efforts towards creating a general-purpose biomedical AI system. To facilitate the development of these generalist biomedical AI systems, a team of researchers from Google Research and Google DeepMind have introduced MultiMedBench, a unique benchmark made up of 14 different biomedical activities, to aid in the development of these biomedical AI systems. These activities cover a range of difficulties, including answering medical questions, analyzing dermatological and mammography images, creating and summarising radiology reports, and identifying genomic variations.

3️⃣ This AI Paper from China Proposes HQTrack: An AI Framework for High-Quality Tracking Anything in Videos [Paper]

A new study by the Dalian University of Technology, China, and DAMO Academy, Alibaba Group, presents a system called HQTrack, which stands for High-Quality Tracking. It comprises a video multi-object segmenter (VMOS) and a mask refiner (MR) primarily. To perceive tiny objects in intricate setups, the researchers employ VMOS, an enhanced variation of DeAOT, and cascade a gated propagation module (GPM) at 1/8 scale. In addition, they use Intern-T as their feature extractor to improve the ability to distinguish between different types of objects. In VMOS, the researchers only keep the most recently used frame in the long-term memory, discarding the older ones to make room. However, applying a large segmentation model to improve the tracking masks could be useful. Objects with complicated structures are especially challenging for SAM to predict, and they appear frequently in the VOTS challenge.

4️⃣ ETH Zurich Researchers Introduce LMQL: A Programming Language For Language Model Interaction [Paper]

Researchers from Switzerland introduced the cutting-edge concept of language model programming (LMP). By expanding the scope of language model prompting beyond simple text prompts, LMP provides a natural hybrid of the two methods. In addition, LMP lets you restrict the results the language model produces. This allows for a high level of abstraction in the language model, making it readily adaptable to various activities. Researchers implement LMQL (for Language Model Query Language) to allow for LMP. LMQL uses the constraints and control flow from an LMP prompt to generate an efficient inference technique that reduces the number of costly calls to the underlying language model. They demonstrate the ease with which LMQL may capture a variety of state-of-the-art prompting mechanisms, notably those that facilitate interactive flows that are difficult to implement with preexisting high-level APIs. The examination demonstrates that they maintain or improve accuracy on various downstream activities while drastically reducing computation time or financial outlay (in the case of pay-to-use APIs).

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