Search Results for author:
Found 23 papers, 7 papers with code
MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets
Driven by powerful image diffusion models, recent research has achieved the automatic creation of 3D objects from textual or visual guidance.
CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians
The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS).
SceneX:Procedural Controllable Large-scale Scene Generation via Large-language Models
Due to its great application potential, large-scale scene generation has drawn extensive attention in academia and industry.
Segment Anything in 3D Gaussians
In this paper, we propose a novel approach to achieve object segmentation in 3D Gaussian via an interactive procedure without any training process and learned parameters.
Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis through Prompt Engineering
The text-to-image synthesis by diffusion models has recently shown remarkable performance in generating high-quality images.
LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection
Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps).
FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes
Indoor scene generation has attracted significant attention recently as it is crucial for applications of gaming, virtual reality, and interior design.
FG-MDM: Towards Zero-Shot Human Motion Generation via Fine-Grained Descriptions
By adopting a divide-and-conquer strategy, we propose a new framework named Fine-Grained Human Motion Diffusion Model (FG-MDM) for zero-shot human motion generation.
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
The advent of Large Language Models (LLMs) has paved the way for complex tasks such as role-playing, which enhances user interactions by enabling models to imitate various characters.
OWL: A Large Language Model for IT Operations
However, there is a lack of specialized LLMs for IT operations.
BAEFormer: Bi-Directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation
Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance.
Ranked #6 on
Bird's-Eye View Semantic Segmentation
on nuScenes
Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization
However, due to the tissue overlap, X-ray images are difficult to provide fine-grained features for early diagnosis.
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning
Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis.
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
The past year has witnessed a rapid development of masked image modeling (MIM).
DATA: Domain-Aware and Task-Aware Self-supervised Learning
However, due to the high training costs and the unconsciousness of downstream usages, most self-supervised learning methods lack the capability to correspond to the diversities of downstream scenarios, as there are various data domains, different vision tasks and latency constraints on models.
GAIA: A Transfer Learning System of Object Detection that Fits Your Needs
Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently.
Uncertainty-Aware Pseudo Label Refinery for Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation for semantic segmentation aims to assign the pixel-level labels for unlabeled target domain by transferring knowledge from the labeled source domain.
Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels
Training with more data has always been the most stable and effective way of improving performance in deep learning era.
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection
Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights.
Learning an Efficient Network for Large-Scale Hierarchical Object Detection with Data Imbalance: 3rd Place Solution to Open Images Challenge 2019
This report details our solution to the Google AI Open Images Challenge 2019 Object Detection Track.
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection
With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection.
POD: Practical Object Detection with Scale-Sensitive Network
Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust to scale variance.
Accelerating Deep Neural Networks with Spatial Bottleneck Modules
This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks.
