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Found 11 papers, 6 papers with code
GS2Pose: Two-stage 6D Object Pose Estimation Guided by Gaussian Splatting
This paper proposes a new method for accurate and robust 6D pose estimation of novel objects, named GS2Pose.
Crystal: Illuminating LLM Abilities on Language and Code
In this work, we propose a pretraining strategy to enhance the integration of natural language and coding capabilities within a single LLM.
On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored.
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs
To address this problem, we propose $\texttt{Web2Code}$, a benchmark consisting of a new large-scale webpage-to-code dataset for instruction tuning and an evaluation framework for the webpage understanding and HTML code translation abilities of MLLMs.
MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor and Connector
The Mini-ASPP and PSA are specifically designed for shadow feature enhancement, thereby enabling the expression of local details and small objects.
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs.
LLM360: Towards Fully Transparent Open-Source LLMs
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
Hedonic Prices and Quality Adjusted Price Indices Powered by AI
To accomplish this, we generate abstract product attributes, or ``features,'' from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function.
Benchmarking Robustness in Neural Radiance Fields
We find that NeRF-based models are significantly degraded in the presence of corruption, and are more sensitive to a different set of corruptions than image recognition models.
Exponential Family Model-Based Reinforcement Learning via Score Matching
We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family distributions with $d$ parameters and the reward is bounded and known.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
