312 citations · 1.5k across the 54 of their papers we have counts for
55 papers · 1 filter
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Senqiao Yang, Junyi Li, Xin Lai +3
Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens. How…
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models
Longxiang Tang, Zhuotao Tian, Kai Li +5
This study addresses the Domain-Class Incremental Learning problem, a realistic but challenging continual learning scenario where both the domain distribution and target classes va…
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
Bohao Peng, Xiaoyang Wu, Li Jiang +4
The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especia…
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding
Chengyao Wang, Li Jiang, Xiaoyang Wu +4
Self-supervised 3D representation learning aims to learn effective representations from large-scale unlabeled point clouds. Most existing approaches adopt point discrimination as t…
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
Lihe Yang, Zhen Zhao, Lei Qi +3
Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly…
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
Xiaoyang Wu, Zhuotao Tian, Xin Wen +4
The rapid advancement of deep learning models often attributes to their ability to leverage massive training data. In contrast, such privilege has not yet fully benefited 3D deep l…
InsMapper: Exploring Inner-instance Information for Vectorized HD Mapping
Zhenhua Xu, Kwan-Yee. K. Wong, Hengshuang Zhao
Vectorized high-definition (HD) maps contain detailed information about surrounding road elements, which are crucial for various downstream tasks in modern autonomous vehicles, suc…
GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping
Zhuoling Li, Chunrui Han, Zheng Ge +5
Efficiency is quite important for 3D lane detection due to practical deployment demand. In this work, we propose a simple, fast, and end-to-end detector that still maintains high d…
AnyDoor: Zero-shot Object-level Image Customization
Xi Chen, Lianghua Huang, Yu Liu +3
This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations in a harmonious way. Instead of tu…
SAM3D: Segment Anything in 3D Scenes
Yunhan Yang, Xiaoyang Wu, Tong He +2
In this work, we propose SAM3D, a novel framework that is able to predict masks in 3D point clouds by leveraging the Segment-Anything Model (SAM) in RGB images without further trai…
OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection
Zhangyang Qi, Jiaqi Wang, Xiaoyang Wu +1
Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query…
VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection
Zhuoling Li, Chuanrui Zhang, Wei-Chiu Ma +5
In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks. However, their performance in multi-view 3D object detection re…
Detecting Everything in the Open World: Towards Universal Object Detection
Zhenyu Wang, Yali Li, Xi Chen +4
In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual…
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning
Xiaoyang Wu, Xin Wen, Xihui Liu +1
As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on vario…
ScribbleSeg: Scribble-based Interactive Image Segmentation
Xi Chen, Yau Shing Jonathan Cheung, Ser-Nam Lim +1
Interactive segmentation enables users to extract masks by providing simple annotations to indicate the target, such as boxes, clicks, or scribbles. Among these interaction formats…
GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue
Zhening Huang, Xiaoyang Wu, Hengshuang Zhao +4
Current point cloud segmentation architectures suffer from limited long-range feature modeling, as they mostly rely on aggregating information with local neighborhoods. Furthermore…
Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation
Zhao Yang, Jiaqi Wang, Yansong Tang +3
Referring image segmentation segments an image from a language expression. With the aim of producing high-quality masks, existing methods often adopt iterative learning approaches…
Influencer Backdoor Attack on Semantic Segmentation
Haoheng Lan, Jindong Gu, Philip Torr +1
When a small number of poisoned samples are injected into the training dataset of a deep neural network, the network can be induced to exhibit malicious behavior during inferences,…
Open-vocabulary Panoptic Segmentation with Embedding Modulation
Xi Chen, Shuang Li, Ser-Nam Lim +2
Open-vocabulary image segmentation is attracting increasing attention due to its critical applications in the real world. Traditional closed-vocabulary segmentation methods are not…
PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer
Zitong Yu, Yuming Shen, Jingang Shi +5
Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applicatio…
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…
: Backward-compatible Training with Basis Transformation
Yifei Zhou, Zilu Li, Abhinav Shrivastava +4
Modern retrieval system often requires recomputing the representation of every piece of data in the gallery when updating to a better representation model. This process is known as…
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
Xiaoyang Wu, Yixing Lao, Li Jiang +2
As a pioneering work exploring transformer architecture for 3D point cloud understanding, Point Transformer achieves impressive results on multiple highly competitive benchmarks. I…
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…