476 citations · 1.2k across the 57 of their papers we have counts for
59 papers
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection
Yifan Zhang, Zhen Dong, Huanrui Yang +6
Multi-view 3D detection based on BEV (bird-eye-view) has recently achieved significant improvements. However, the huge memory consumption of state-of-the-art models makes it hard t…
PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers
Peidong Jia, Jiaming Liu, Senqiao Yang +3
The Transformer-based detectors (i.e., DETR) have demonstrated impressive performance on end-to-end object detection. However, transferring DETR to different data distributions may…
DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images
Mingjie Pan, Yulu Gan, Fangxu Zhou +4
Three-dimensional microscopy is often limited by anisotropic spatial resolution, resulting in lower axial resolution than lateral resolution. Current State-of-The-Art (SoTA) isotro…
UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering
Mingjie Pan, Li Liu, Jiaming Liu +6
In this technical report, we present our solution, named UniOCC, for the Vision-Centric 3D occupancy prediction track in the nuScenes Open Dataset Challenge at CVPR 2023. Existing…
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation
Jiaming Liu, Senqiao Yang, Peidong Jia +5
Since real-world machine systems are running in non-stationary environments, Continual Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually ch…
HUB: Guiding Learned Optimizers with Continuous Prompt Tuning
Gaole Dai, Wei Wu, Ziyu Wang +3
Learned optimizers are a crucial component of meta-learning. Recent advancements in scalable learned optimizers have demonstrated their superior performance over hand-designed opti…
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models
Yijia Zhang, Lingran Zhao, Shijie Cao +6
Efficient deployment of large language models (LLMs) necessitates low-bit quantization to minimize model size and inference cost. While low-bit integer formats (e.g., INT8/INT4) ha…
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation
Yuheng Lu, Chenfeng Xu, Xiaobao Wei +4
The goal of open-vocabulary detection is to identify novel objects based on arbitrary textual descriptions. In this paper, we address open-vocabulary 3D point-cloud detection by a…
Chain of Thought Prompt Tuning in Vision Language Models
Jiaxin Ge, Hongyin Luo, Siyuan Qian +3
Language-Image Pre-training has demonstrated promising results on zero-shot and few-shot downstream tasks by prompting visual models with natural language prompts. However, most re…
PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection
Anthony Chen, Kevin Zhang, Renrui Zhang +4
Masked Autoencoders learn strong visual representations and achieve state-of-the-art results in several independent modalities, yet very few works have addressed their capabilities…
WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal
Yulin Luo, Rui Zhao, Xiaobao Wei +7
Adverse weather removal tasks like deraining, desnowing, and dehazing are usually treated as separate tasks. However, in practical autonomous driving scenarios, the type, intensity…
Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
Senqiao Yang, Jiarui Wu, Jiaming Liu +6
The visual prompts have provided an efficient manner in addressing visual cross-domain problems. In previous works, Visual Domain Prompt (VDP) first introduces domain prompts to ta…
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks
Xiaowei Chi, Jiaming Liu, Ming Lu +4
Bird's-Eye-View (BEV) 3D Object Detection is a crucial multi-view technique for autonomous driving systems. Recently, plenty of works are proposed, following a similar paradigm con…
Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world
Yulu Gan, Mingjie Pan, Rongyu Zhang +4
When facing changing environments in the real world, the lightweight model on client devices suffers from severe performance drops under distribution shifts. The main limitations o…
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification
Lirui Xiao, Huanrui Yang, Zhen Dong +3
Mixed-precision quantization has been widely applied on deep neural networks (DNNs) as it leads to significantly better efficiency-accuracy tradeoffs compared to uniform quantizati…
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection
Jianing Li, Ming Lu, Jiaming Liu +3
Recently, Bird's-Eye-View (BEV) representation has gained increasing attention in multi-view 3D object detection, which has demonstrated promising applications in autonomous drivin…
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection
Jiaming Liu, Rongyu Zhang, Xiaoqi Li +5
Vision-centric bird-eye-view (BEV) perception has shown promising potential in autonomous driving. Recent works mainly focus on improving efficiency or accuracy but neglect the cha…
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
Yijiang Liu, Huanrui Yang, Zhen Dong +3
The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision tran…
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
Xiangyang Zhu, Renrui Zhang, Bowei He +5
Large-scale pre-trained models have shown promising open-world performance for both vision and language tasks. However, their transferred capacity on 3D point clouds is still limit…
Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation
Yixiong Zou, Shanghang Zhang, Yuhua Li +1
Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes with suffic…
BadRes: Reveal the Backdoors through Residual Connection
Mingrui He, Tianyu Chen, Haoyi Zhou +2
Generally, residual connections are indispensable network components in building CNNs and Transformers for various downstream tasks in CV and VL, which encourages skip shortcuts be…
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Xiuying Wei, Yunchen Zhang, Xiangguo Zhang +5
Transformer architecture has become the fundamental element of the widespread natural language processing~(NLP) models. With the trends of large NLP models, the increasing memory a…
Uncertainty Guided Depth Fusion for Spike Camera
Jianing Li, Jiaming Liu, Xiaobao Wei +6
Depth estimation is essential for various important real-world applications such as autonomous driving. However, it suffers from severe performance degradation in high-velocity sce…
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
Jiaming Liu, Qizhe Zhang, Xiaoqi Li +5
Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in autonomous driving by mitigating the challenges posed by high-velocity…