742 citations · 1.8k across the 57 of their papers we have counts for
56 papers · 1 filter
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…
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…
ONeRF: Unsupervised 3D Object Segmentation from Multiple Views
Shengnan Liang, Yichen Liu, Shangzhe Wu +2
We present ONeRF, a method that automatically segments and reconstructs object instances in 3D from multi-view RGB images without any additional manual annotations. The segmented 3…
H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions
Changlin Li, Guangyang Wu, Yanan Sun +3
Capitalizing on the rapid development of neural networks, recent video frame interpolation (VFI) methods have achieved notable improvements. However, they still fall short for real…
Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts
Qi Fan, Mattia Segu, Yu-Wing Tai +4
Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary subst…
NeRF-RPN: A general framework for object detection in NeRFs
Benran Hu, Junkai Huang, Yichen Liu +2
This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF. Given a pre-trained NeRF model, NeRF-RPN aims to detect all boundin…
FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fields
Hao Zhang, Tianyuan Dai, Yu-Wing Tai +1
This paper presents the first significant work on directly predicting 3D face landmarks on neural radiance fields (NeRFs). Our 3D coarse-to-fine Face Landmarks NeRF (FLNeRF) model…
Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation
Xinhang Liu, Jiaben Chen, Huai Yu +2
We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived fr…
Occlusion-Aware Instance Segmentation via BiLayer Network Architectures
Lei Ke, Yu-Wing Tai, Chi-Keung Tang
Segmenting highly-overlapping image objects is challenging, because there is typically no distinction between real object contours and occlusion boundaries on images. Unlike previo…
Video Mask Transfiner for High-Quality Video Instance Segmentation
Lei Ke, Henghui Ding, Martin Danelljan +3
While Video Instance Segmentation (VIS) has seen rapid progress, current approaches struggle to predict high-quality masks with accurate boundary details. Moreover, the predicted s…
Self-Support Few-Shot Semantic Segmentation
Qi Fan, Wenjie Pei, Yu-Wing Tai +1
Existing few-shot segmentation methods have achieved great progress based on the support-query matching framework. But they still heavily suffer from the limited coverage of intra-…
Human Instance Matting via Mutual Guidance and Multi-Instance Refinement
Yanan Sun, Chi-Keung Tang, Yu-Wing Tai
This paper introduces a new matting task called human instance matting (HIM), which requires the pertinent model to automatically predict a precise alpha matte for each human insta…
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
Peng Zheng, Huazhu Fu, Deng-Ping Fan +5
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in na…
Interactiveness Field in Human-Object Interactions
Xinpeng Liu, Yong-Lu Li, Xiaoqian Wu +3
Human-Object Interaction (HOI) detection plays a core role in activity understanding. Though recent two/one-stage methods have achieved impressive results, as an essential step, di…
HAA4D: Few-Shot Human Atomic Action Recognition via 3D Spatio-Temporal Skeletal Alignment
Mu-Ruei Tseng, Abhishek Gupta, Chi-Keung Tang +1
Human actions involve complex pose variations and their 2D projections can be highly ambiguous. Thus 3D spatio-temporal or 4D (i.e., 3D+T) human skeletons, which are photometric an…
Mask Transfiner for High-Quality Instance Segmentation
Lei Ke, Martin Danelljan, Xia Li +3
Two-stage and query-based instance segmentation methods have achieved remarkable results. However, their segmented masks are still very coarse. In this paper, we present Mask Trans…
Occlusion-Aware Video Object Inpainting
Lei Ke, Yu-Wing Tai, Chi-Keung Tang
Conventional video inpainting is neither object-oriented nor occlusion-aware, making it liable to obvious artifacts when large occluded object regions are inpainted. This paper pre…