355 citations · 2.9k across the 115 of their papers we have counts for
113 papers · 1 filter
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
Yilun Chen, Zhiding Yu, Yukang Chen +4
False negatives (FN) in 3D object detection, {\em e.g.}, missing predictions of pedestrians, vehicles, or other obstacles, can lead to potentially dangerous situations in autonomou…
LISA: Reasoning Segmentation via Large Language Model
Xin Lai, Zhuotao Tian, Yukang Chen +4
Although perception systems have made remarkable advancements in recent years, they still rely on explicit human instruction or pre-defined categories to identify the target object…
DiffComplete: Diffusion-based Generative 3D Shape Completion
Ruihang Chu, Enze Xie, Shentong Mo +4
We introduce a new diffusion-based approach for shape completion on 3D range scans. Compared with prior deterministic and probabilistic methods, we strike a balance between realism…
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract
Bohao Peng, Zhuotao Tian, Xiaoyang Wu +4
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic featu…
Real-World Image Variation by Aligning Diffusion Inversion Chain
Yuechen Zhang, Jinbo Xing, Eric Lo +1
Recent diffusion model advancements have enabled high-fidelity images to be generated using text prompts. However, a domain gap exists between generated images and real-world image…
Self-supervised Learning by View Synthesis
Shaoteng Liu, Xiangyu Zhang, Tao Hu +1
We present view-synthesis autoencoders (VSA) in this paper, which is a self-supervised learning framework designed for vision transformers. Different from traditional 2D pretrainin…
TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation
Jingyao Li, Pengguang Chen, Shengju Qian +2
Contrastive Language-Image Pre-training (CLIP) has recently shown great promise in pixel-level zero-shot learning tasks. However, existing approaches utilizing CLIP's text and patc…
TriVol: Point Cloud Rendering via Triple Volumes
Tao Hu, Xiaogang Xu, Ruihang Chu +1
Existing learning-based methods for point cloud rendering adopt various 3D representations and feature querying mechanisms to alleviate the sparsity problem of point clouds. Howeve…
Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields
Tao Hu, Xiaogang Xu, Shu Liu +1
Synthesizing photo-realistic images from a point cloud is challenging because of the sparsity of point cloud representation. Recent Neural Radiance Fields and extensions are propos…
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation
Bohao Peng, Zhuotao Tian, Xiaoyang Wu +4
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic featu…
Spherical Transformer for LiDAR-based 3D Recognition
Xin Lai, Yukang Chen, Fanbin Lu +2
LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information…
Learning Context-aware Classifier for Semantic Segmentation
Zhuotao Tian, Jiequan Cui, Li Jiang +5
Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature dist…
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
Yukang Chen, Jianhui Liu, Xiangyu Zhang +2
3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified…
Video-P2P: Video Editing with Cross-attention Control
Shaoteng Liu, Yuechen Zhang, Wenbo Li +2
This paper presents Video-P2P, a novel framework for real-world video editing with cross-attention control. While attention control has proven effective for image editing with pre-…
StraIT: Non-autoregressive Generation with Stratified Image Transformer
Shengju Qian, Huiwen Chang, Yuanzhen Li +3
We propose Stratified Image Transformer(StraIT), a pure non-autoregressive(NAR) generative model that demonstrates superiority in high-quality image synthesis over existing autoreg…
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
Jingyao Li, Pengguang Chen, Shaozuo Yu +3
The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples. Previous work applied recognition-ba…
Understanding Imbalanced Semantic Segmentation Through Neural Collapse
Zhisheng Zhong, Jiequan Cui, Yibo Yang +4
A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equi…
What Makes for Good Tokenizers in Vision Transformer?
Shengju Qian, Yi Zhu, Wenbo Li +2
The architecture of transformers, which recently witness booming applications in vision tasks, has pivoted against the widespread convolutional paradigm. Relying on the tokenizatio…
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments
Xiaogang Xu, Hengshuang Zhao, Philip Torr +1
Deep Neural Networks (DNNs) are vulnerable to the black-box adversarial attack that is highly transferable. This threat comes from the distribution gap between adversarial and clea…
Image Inpainting via Iteratively Decoupled Probabilistic Modeling
Wenbo Li, Xin Yu, Kun Zhou +3
Generative adversarial networks (GANs) have made great success in image inpainting yet still have difficulties tackling large missing regions. In contrast, iterative probabilistic…
Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization
Yuechen Zhang, Zexin He, Jinbo Xing +2
Current 3D scene stylization methods transfer textures and colors as styles using arbitrary style references, lacking meaningful semantic correspondences. We introduce Reference-Ba…
High-Quality Entity Segmentation
Lu Qi, Jason Kuen, Weidong Guo +5
Dense image segmentation tasks e.g., semantic, panoptic) are useful for image editing, but existing methods can hardly generalize well in an in-the-wild setting where there are unr…
Generalized Parametric Contrastive Learning
Jiequan Cui, Zhisheng Zhong, Zhuotao Tian +3
In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we obse…
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Xin Lai, Zhuotao Tian, Xiaogang Xu +5
Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. It leverages a labeled source domain dataset…