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

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🔥Trending AI Research

1️⃣ Can Large Language Models Help Long-term Action Anticipation from Videos? Meet AntGPT: An AI Framework to Incorporate Large Language Models for the Video-based Long-Term Action Anticipation Task [Paper] [Blog]

Researchers from Brown University and Honda Research Institute provide a two-stage system called AntGPT to do the quantitative and qualitative evaluations required to provide answers to these questions. AntGPT first identifies human activities using supervised action recognition algorithms. The OpenAI GPT models are fed the recognized actions by AntGPT as discretized video representations to determine the intended outcome of the actions or the actions to come, which may then optionally be post-processed into the final predictions. In bottom-up LTA, they explicitly ask the GPT model to predict future action sequences using autoregressive methods, fine-tuning, or in-context learning. They initially ask GPT to forecast the actor’s aim before producing the actor’s behaviors to accomplish top-down LTA. Continue reading……

2️⃣ UC Berkeley Researchers Introduce Dynalang: An AI Agent that Learns a Multimodal World Model to Predict Future Text and Image Representations and Learns to Act from Imagined Model Rollouts [Paper] [Blog]

Researchers from UC Berkeley introduce Dynalang, an agent that acquires a language and visual model of the world through online experience and utilizes the model to understand how to behave. Dynalang separates learning to behave using that model (reinforcement learning with task incentives) from learning to model the world with language (supervised learning with prediction targets). The world model receives visual and textual inputs as observation modalities, which are compressed into a latent space. With data gathered online as the agent interacts with its surroundings, it trains the world model to anticipate future latent representations. Using the latent representation of the world model as input, they train the policy to adopt decisions that maximize task reward. Continue reading……

3️⃣ Meet CT2Hair: A Fully Automatic Framework for Creating High-Fidelity 3D Hair Models that are Suitable for Use in Downstream Graphics Applications [Paper] [Blog]

Who doesn’t like gaming? The more natural and fashioned the characters in the game, the more we enjoy it. Is it possible to have graphics that look exactly like natural hair?

Apart from 3D hair authoring tools, the manual creation by artists is both time-consuming and difficult to scale and can also be biased by the limitations of current 3D authoring tools. Creating a large dataset that accurately represents a wide range of real-world hair variations like curly, silky, straight, and wavy is a big challenge. Researchers at State Key Labs and Meta Reality Labs succeeded in reconstructing various hairstyle graphics from real-world hair wigs as input. Continue reading……

4️⃣ Imagine Swapping OpenAI with any LLM and all in a Single Line! Meet Genoss GPT: An API that is Compatible with OpenAI SDK and Built on Top of Open-Source Models like GPT4ALL [GitHub link] [Blog]

Google Deepmind’s research aims to improve generalization and enable emergent semantic reasoning by directly incorporating vision-language models trained on Internet-scale data into end-to-end robotic control. With the help of web-based language and vision-language data, we aim to make a single, comprehensively trained model to learn to link robot observations to actions. They propose fine-tuning state-of-the-art vision-language models together using data from robot trajectories and large-scale visual question-answering exercises conducted over the Internet. In contrast to other methods, they propose a straightforward, all-purpose recipe: express robotic actions as text tokens and incorporate them directly into the model’s training set as natural language tokens would. Researchers study vision-language-action models (VLA), and RT-2 instantiates one such model. Through rigorous testing (6k assessment trials), they could ascertain that RT-2 acquired various emergent skills through Internet-scale training and that the technique led to performant robotic policies. Continue reading……

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