41 citations · 81 across the 17 of their papers we have counts for
12 papers · 1 filter
Deep3DSketch: 3D modeling from Free-hand Sketches with View- and Structural-Aware Adversarial Training
Tianrun Chen, Chenglong Fu, Lanyun Zhu +4
This work aims to investigate the problem of 3D modeling using single free-hand sketches, which is one of the most natural ways we humans express ideas. Although sketch-based 3D mo…
Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches
Tianrun Chen, Chenglong Fu, Ying Zang +4
The rapid development of AR/VR brings tremendous demands for 3D content. While the widely-used Computer-Aided Design (CAD) method requires a time-consuming and labor-intensive mode…
PanopticNeRF-360: Panoramic 3D-to-2D Label Transfer in Urban Scenes
Xiao Fu, Shangzhan Zhang, Tianrun Chen +4
Training perception systems for self-driving cars requires substantial 2D annotations that are labor-intensive to manual label. While existing datasets provide rich annotations on…
Learning Gabor Texture Features for Fine-Grained Recognition
Lanyun Zhu, Tianrun Chen, Jianxiong Yin +2
Extracting and using class-discriminative features is critical for fine-grained recognition. Existing works have demonstrated the possibility of applying deep CNNs to exploit featu…
Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
Shangzan Zhang, Sida Peng, Yinji ShenTu +5
Recently, the editing of neural radiance fields (NeRFs) has gained considerable attention, but most prior works focus on static scenes while research on the appearance editing of d…
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
Jintao Rong, Hao Chen, Linlin Ou +3
The Contrastive Language-Image Pretraining (CLIP) model has been widely used in various downstream vision tasks. The few-shot learning paradigm has been widely adopted to augment i…
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
Tianrun Chen, Lanyun Zhu, Chaotao Ding +6
The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for…
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel Inference
Jinghan Yao, Nawras Alnaasan, Tian Chen +4
Autoregressive models, despite their commendable performance in a myriad of generative tasks, face challenges stemming from their inherently sequential structure. Inference on thes…
Learning imaging mechanism directly from optical microscopy observations
Ze-Hao Wang, Long-Kun Shan, Tong-Tian Weng +6
Optical microscopy image plays an important role in scientific research through the direct visualization of the nanoworld, where the imaging mechanism is described as the convoluti…
Continual Semantic Segmentation with Automatic Memory Sample Selection
Lanyun Zhu, Tianrun Chen, Jianxiong Yin +2
Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training. To alleviate the catastrophic forgetting issue in…
Performance Characterization of using Quantization for DNN Inference on Edge Devices: Extended Version
Hyunho Ahn, Tian Chen, Nawras Alnaasan +5
Quantization is a popular technique used in Deep Neural Networks (DNN) inference to reduce the size of models and improve the overall numerical performance by exploiting native har…
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
Shangzan Zhang, Sida Peng, Tianrun Chen +5
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single ima…