742 citations · 2.5k across the 92 of their papers we have counts for
12 papers · 1 filter
Scene-Generalizable Interactive Segmentation of Radiance Fields
Songlin Tang, Wenjie Pei, Xin Tao +3
Existing methods for interactive segmentation in radiance fields entail scene-specific optimization and thus cannot generalize across different scenes, which greatly limits their a…
Feature Decoupling-Recycling Network for Fast Interactive Segmentation
Huimin Zeng, Weinong Wang, Xin Tao +3
Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input without considering the invariant nature of the sour…
Cascade-DETR: Delving into High-Quality Universal Object Detection
Mingqiao Ye, Lei Ke, Siyuan Li +4
Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not comp…
Segment Anything Meets Point Tracking
Frano Rajič, Lei Ke, Yu-Wing Tai +3
The Segment Anything Model (SAM) has established itself as a powerful zero-shot image segmentation model, enabled by efficient point-centric annotation and prompt-based models. Whi…
UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks
Yanan Sun, Zihan Zhong, Qi Fan +2
Large-scale joint training of multimodal models, e.g., CLIP, have demonstrated great performance in many vision-language tasks. However, image-text pairs for pre-training are restr…
Segment Anything in High Quality
Lei Ke, Mingqiao Ye, Martin Danelljan +4
The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being…
Registering Neural Radiance Fields as 3D Density Images
Han Jiang, Ruoxuan Li, Haosen Sun +2
No significant work has been done to directly merge two partially overlapping scenes using NeRF representations. Given pre-trained NeRF models of a 3D scene with partial overlappin…
Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation
Yue Xu, Yong-Lu Li, Kaitong Cui +4
Data-efficient learning has garnered significant attention, especially given the current trend of large multi-modal models. Recently, dataset distillation has become an effective a…
Deceptive-NeRF/3DGS: Diffusion-Generated Pseudo-Observations for High-Quality Sparse-View Reconstruction
Xinhang Liu, Jiaben Chen, Shiu-hong Kao +2
Novel view synthesis via Neural Radiance Fields (NeRFs) or 3D Gaussian Splatting (3DGS) typically necessitates dense observations with hundreds of input images to circumvent artifa…
Instance Neural Radiance Field
Yichen Liu, Benran Hu, Junkai Huang +2
This paper presents one of the first learning-based NeRF 3D instance segmentation pipelines, dubbed as Instance Neural Radiance Field, or Instance NeRF. Taking a NeRF pretrained fr…
Mask-Free Video Instance Segmentation
Lei Ke, Martin Danelljan, Henghui Ding +3
The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks…
Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations
Xinhang Liu, Yu-Wing Tai, Chi-Keung Tang
While Neural Radiance Fields (NeRFs) had achieved unprecedented novel view synthesis results, they have been struggling in dealing with large-scale cluttered scenes with sparse inp…