1.2k citations · 3k across the 74 of their papers we have counts for
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Vertical Layering of Quantized Neural Networks for Heterogeneous Inference
Hai Wu, Ruifei He, Haoru Tan +2
Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated…
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
Yingxian Chen, Zhengzhe Liu, Baoheng Zhang +3
Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long vi…
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
Runyu Ding, Jihan Yang, Chuhui Xue +3
Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary perception is l…
Parametric Classification for Generalized Category Discovery: A Baseline Study
Xin Wen, Bingchen Zhao, Xiaojuan Qi
Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. Previous studies argued that parametric…
SL3D: Self-supervised-Self-labeled 3D Recognition
Fernando Julio Cendra, Lan Ma, Jiajun Shen +1
Deep learning has attained remarkable success in many 3D visual recognition tasks, including shape classification, object detection, and semantic segmentation. However, many of the…
Is synthetic data from generative models ready for image recognition?
Ruifei He, Shuyang Sun, Xin Yu +5
Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applic…
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
Shizhen Zhao, Xiaojuan Qi
Most existing 3D point cloud object detection approaches heavily rely on large amounts of labeled training data. However, the labeling process is costly and time-consuming. This pa…
In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks
Kaibin Huang, Hai Wu, Zhiyan Liu +1
The sixth-generation (6G) mobile networks are expected to feature the ubiquitous deployment of machine learning and AI algorithms at the network edge. With rapid advancements in ed…
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection
Jianhui Liu, Yukang Chen, Xiaoqing Ye +3
3D scenes are dominated by a large number of background points, which is redundant for the detection task that mainly needs to focus on foreground objects. In this paper, we analyz…
Rethinking Resolution in the Context of Efficient Video Recognition
Chuofan Ma, Qiushan Guo, Yi Jiang +3
In this paper, we empirically study how to make the most of low-resolution frames for efficient video recognition. Existing methods mainly focus on developing compact networks or a…
Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing
Xin Yu, Peng Dai, Wenbo Li +4
With the rapid development of mobile devices, modern widely-used mobile phones typically allow users to capture 4K resolution (i.e., ultra-high-definition) images. However, for ima…
Multimodal Transformer for Automatic 3D Annotation and Object Detection
Chang Liu, Xiaoyan Qian, Binxiao Huang +4
Despite a growing number of datasets being collected for training 3D object detection models, significant human effort is still required to annotate 3D boxes on LiDAR scans. To aut…
LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs
Yukang Chen, Jianhui Liu, Xiangyu Zhang +2
Recent advance in 2D CNNs has revealed that large kernels are important. However, when directly applying large convolutional kernels in 3D CNNs, severe difficulties are met, where…
Unifying Voxel-based Representation with Transformer for 3D Object Detection
Yanwei Li, Yilun Chen, Xiaojuan Qi +3
In this work, we present a unified framework for multi-modality 3D object detection, named UVTR. The proposed method aims to unify multi-modality representations in the voxel space…
Voxel Field Fusion for 3D Object Detection
Yanwei Li, Xiaojuan Qi, Yukang Chen +4
In this work, we present a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion. The proposed approach aims to maintain cros…
Self-Supervised Visual Representation Learning with Semantic Grouping
Xin Wen, Bingchen Zhao, Anlin Zheng +2
In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlyin…
Towards Efficient 3D Object Detection with Knowledge Distillation
Jihan Yang, Shaoshuai Shi, Runyu Ding +2
Despite substantial progress in 3D object detection, advanced 3D detectors often suffer from heavy computation overheads. To this end, we explore the potential of knowledge distill…
Video Demoireing with Relation-Based Temporal Consistency
Peng Dai, Xin Yu, Lan Ma +5
Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras. Considering the increasing demands for captur…
DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation
Runyu Ding, Jihan Yang, Li Jiang +1
Deep learning approaches achieve prominent success in 3D semantic segmentation. However, collecting densely annotated real-world 3D datasets is extremely time-consuming and expensi…
MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator
Chang Liu, Xiaoyan Qian, Xiaojuan Qi +3
Manually annotating 3D point clouds is laborious and costly, limiting the training data preparation for deep learning in real-world object detection. While a few previous studies t…
Towards Implicit Text-Guided 3D Shape Generation
Zhengzhe Liu, Yi Wang, Xiaojuan Qi +1
In this work, we explore the challenging task of generating 3D shapes from text. Beyond the existing works, we propose a new approach for text-guided 3D shape generation, capable o…
Stratified Transformer for 3D Point Cloud Segmentation
Xin Lai, Jianhui Liu, Li Jiang +5
3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependenci…
HINT: Hierarchical Neuron Concept Explainer
Andong Wang, Wei-Ning Lee, Xiaojuan Qi
To interpret deep networks, one main approach is to associate neurons with human-understandable concepts. However, existing methods often ignore the inherent relationships of diffe…
Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability
Ruifei He, Shuyang Sun, Jihan Yang +2
Large-scale pre-training has been proven to be crucial for various computer vision tasks. However, with the increase of pre-training data amount, model architecture amount, and the…