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Found 9 papers, 0 papers with code
GVGEN: Text-to-3D Generation with Volumetric Representation
To simplify the generation of GaussianVolume and empower the model to generate instances with detailed 3D geometry, we propose a coarse-to-fine pipeline.
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.
