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Found 19 papers, 9 papers with code
MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft
In evaluation, we propose new metrics to assess not only visual quality but also the action following capacity when generating new scenes, which is crucial for a world model.
Fast Autoregressive Video Generation with Diagonal Decoding
Autoregressive Transformer models have demonstrated impressive performance in video generation, but their sequential token-by-token decoding process poses a major bottleneck, particularly for long videos represented by tens of thousands of tokens.
Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks
The framework simulates realistic force-based interactions, enhancing the training data's quality for precise robotic manipulation in construction tasks.
Open-Sora Plan: Open-Source Large Video Generation Model
We introduce Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on various user inputs.
WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model
However, as the resolution and duration of generated videos increase, the encoding cost of Video VAEs becomes a limiting bottleneck in training LVDMs.
Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
A Hybrid Generative and Discriminative PointNet on Unordered Point Sets
This paper proposes GDPNet, the first hybrid Generative and Discriminative PointNet that extends JEM for point cloud classification and generation.
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
In this work, we propose a simple yet effective training strategy MoE-Tuning for LVLMs.
Ranked #150 on
Visual Question Answering
on MM-Vet
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
Ranked #9 on
Zero-Shot Video Question Answer
on TGIF-QA
Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
Drones are vital for urban emergency search and rescue (SAR) due to the challenges of navigating dynamic environments with obstacles like buildings and wind.
W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition
Contrastive learning has become a popular solution for few-shot Name Entity Recognization (NER).
Improved Trust in Human-Robot Collaboration with ChatGPT
A human-subject experiment showed that incorporating ChatGPT in robots significantly increased trust in human-robot collaboration, which can be attributed to the robot's ability to communicate more effectively with humans.
Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT
To expand the ability of the current robot system in sequential understanding, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks.
APSNet: Attention Based Point Cloud Sampling
Traditional task-agnostic sampling methods, such as farthest point sampling (FPS), do not consider downstream tasks when sampling point clouds, and thus non-informative points to the tasks are often sampled.
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction
Thus, in this paper, we propose a Hierarchical N-Gram framework for Zero-Shot Link Prediction (HNZSLP), which considers the dependencies among character n-grams of the relation surface name for ZSLP.
Impacts of export restrictions on the global personal protective equipment trade network during COVID-19
The threshold model captures the shortage contagion on the global PPE trade network.
Physics and Society
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
We show that our Generative MMC (GMMC) can be trained discriminatively, generatively, or jointly for image classification and generation.
Adversarial Privacy Preserving Graph Embedding against Inference Attack
In this paper, we propose Adversarial Privacy Graph Embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations.
Sparse Graph Attention Networks
Among the variants of GNNs, Graph Attention Networks (GATs) learn to assign dense attention coefficients over all neighbors of a node for feature aggregation, and improve the performance of many graph learning tasks.
