113 citations · 283 across the 14 of their papers we have counts for
14 papers
TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation
Rong Li, ShiJie Li, Xieyuanli Chen +3
LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist wi…
Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking
Teli Ma, Mengmeng Wang, Jimin Xiao +2
Siamese network has been a de facto benchmark framework for 3D LiDAR object tracking with a shared-parametric encoder extracting features from template and search region, respectiv…
An Examination of the Compositionality of Large Generative Vision-Language Models
Teli Ma, Rong Li, Junwei Liang
With the success of Large Language Models (LLMs), many Generative Vision-Language Models (GVLMs) have been constructed via multimodal instruction tuning. However, the performance o…
Correlation Pyramid Network for 3D Single Object Tracking
Mengmeng Wang, Teli Ma, Xingxing Zuo +2
3D LiDAR-based single object tracking (SOT) has gained increasing attention as it plays a crucial role in 3D applications such as autonomous driving. The central problem is how to…
Resilient Binary Neural Network
Sheng Xu, Yanjing Li, Teli Ma +5
Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is…
IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors
Sheng Xu, Yanjing Li, Bohan Zeng +5
Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and stu…
Recurrent Bilinear Optimization for Binary Neural Networks
Sheng Xu, Yanjing Li, Tiancheng Wang +6
Binary Neural Networks (BNNs) show great promise for real-world embedded devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation plays an esse…
ConvMAE: Masked Convolution Meets Masked Autoencoders
Peng Gao, Teli Ma, Hongsheng Li +3
Vision Transformers (ViT) become widely-adopted architectures for various vision tasks. Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer…
TerViT: An Efficient Ternary Vision Transformer
Sheng Xu, Yanjing Li, Teli Ma +4
Vision transformers (ViTs) have demonstrated great potential in various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-con…
A Simple Long-Tailed Recognition Baseline via Vision-Language Model
Teli Ma, Shijie Geng, Mengmeng Wang +5
The visual world naturally exhibits a long-tailed distribution of open classes, which poses great challenges to modern visual systems. Existing approaches either perform class re-b…
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Peng Gao, Shijie Geng, Renrui Zhang +5
Large-scale contrastive vision-language pre-training has shown significant progress in visual representation learning. Unlike traditional visual systems trained by a fixed set of d…
Oriented Object Detection with Transformer
Teli Ma, Mingyuan Mao, Honghui Zheng +6
Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN. However, the potential of DETR remains largely un…
Dual-stream Network for Visual Recognition
Mingyuan Mao, Renrui Zhang, Honghui Zheng +6
Transformers with remarkable global representation capacities achieve competitive results for visual tasks, but fail to consider high-level local pattern information in input image…
A Review of Recent Advances of Binary Neural Networks for Edge Computing
Wenyu Zhao, Teli Ma, Xuan Gong +2
Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial syste…