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Found 19 papers, 1 papers with code
MeshCraft: Exploring Efficient and Controllable Mesh Generation with Flow-based DiTs
Specifically, MeshCraft consists of two core components: 1) a transformer-based VAE that encodes raw meshes into continuous face-level tokens and decodes them back to the original meshes, and 2) a flow-based diffusion transformer conditioned on the number of faces, enabling the generation of high-quality 3D meshes with a predefined number of faces.
Aether: Geometric-Aware Unified World Modeling
The integration of geometric reconstruction and generative modeling remains a critical challenge in developing AI systems capable of human-like spatial reasoning.
Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems
Testing and evaluation is an important step before the large-scale application of the autonomous driving systems (ADSs).
Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue
The degraded performance and group unfairness caused by confounding sensitive attributes in rumor detection remains relatively unexplored.
CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction
3D Gaussian Splatting (3DGS) has demonstrated impressive performance in scene reconstruction.
LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization
In fact, all the subspaces representing danger in the logical scenario space, rather than only the most critical concrete scenario, play a more significant role for the safety evaluation.
Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction
Our model simultaneously estimates the camera pose from a single image and predicts the view from a new camera pose, effectively bridging the gap between spatial awareness and visual prediction.
A Prompting-Based Representation Learning Method for Recommendation with Large Language Models
The extensive information pre-trained by these LLMs allows for the potential to capture a more profound semantic representation from different contextual information of users and items.
HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling
Large Language Models (LLMs) have achieved remarkable success in various fields, prompting several studies to explore their potential in recommendation systems.
GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction
To this end, we propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS.
GVGEN: Text-to-3D Generation with Volumetric Representation
To better optimize the representation of these details, we propose a unique pruning and densifying method named the Candidate Pool Strategy, enhancing detail fidelity through selective optimization.
A Survey on Large Language Models for Personalized and Explainable Recommendations
In recent years, Recommender Systems(RS) have witnessed a transformative shift with the advent of Large Language Models(LLMs) in the field of Natural Language Processing(NLP).
TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices
To the best of our knowledge, SparseEngine is the first deployment framework capable of performing inference of sparse models with transformer on MCUs.
Hierarchical-level rain image generative model based on GAN
RCCycleGAN is based on the generative adversarial network (GAN) and can generate images of light, medium, and heavy rain.
EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE
Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed.
Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles
Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs.
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals
We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily.
An Ontology-based Method to Identify Triggering Conditions for Perception Insufficiency of Autonomous Vehicles
The autonomous vehicle (AV) is a safety-critical system relying on complex sensors and algorithms.
LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization
Black-box functions are broadly used to model complex problems that provide no explicit information but the input and output.
