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Found 8 papers, 3 papers with code
Few-shot Adaptation of Multi-modal Foundation Models: A Survey
Therefore, in this survey, we introduce and analyze the research advancements in few-shot adaptation methods for multi-modal models, summarizing commonly used datasets and experimental setups, and comparing the results of different methods.
TableLlama: Towards Open Large Generalist Models for Tables
Towards that end, we construct TableInstruct, a new dataset with a variety of realistic tables and tasks, for instruction tuning and evaluating LLMs.
Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System
We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps.
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
By leveraging data from multiple clients, the FL paradigm can be especially beneficial for clients that have little training data to develop a data-hungry neural semantic parser on their own.
Exploring Chain-of-Thought Style Prompting for Text-to-SQL
Thus, we systematically study how to enhance LLMs' reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023).
Bootstrapping a User-Centered Task-Oriented Dialogue System
We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks.
Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras
Dynamic multi-person mesh recovery has been a hot topic in 3D vision recently.
Object-Occluded Human Shape and Pose Estimation From a Single Color Image
We propose a novel two-branch network architecture to train an end-to-end regressor via the latent feature supervision, which also includes a novel saliency map sub-net to extract the human information from object-occluded color images.
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