33 citations · 129 across the 12 of their papers we have counts for
12 papers
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection
Weixin Mao, Jinrong Yang, Zheng Ge +5
Depth perception is a crucial component of monoc-ular 3D detection tasks that typically involve ill-posed problems. In light of the success of sample mining techniques in 2D object…
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
Rui Yang, Lin Song, Yanwei Li +4
This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for too…
BoxSnake: Polygonal Instance Segmentation with Box Supervision
Rui Yang, Lin Song, Yixiao Ge +1
Box-supervised instance segmentation has gained much attention as it requires only simple box annotations instead of costly mask or polygon annotations. However, existing box-super…
Dynamic Grained Encoder for Vision Transformers
Lin Song, Songyang Zhang, Songtao Liu +5
Transformers, the de-facto standard for language modeling, have been recently applied for vision tasks. This paper introduces sparse queries for vision transformers to exploit the…
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Jinrong Yang, Lin Song, Songtao Liu +6
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference. These strategies are typically based on fixed and handcrafted rul…
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
Songyang Zhang, Lin Song, Songtao Liu +4
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YO…
Fine-Grained Dynamic Head for Object Detection
Lin Song, Yanwei Li, Zhengkai Jiang +4
The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, th…
Rethinking Learnable Tree Filter for Generic Feature Transform
Lin Song, Yanwei Li, Zhengkai Jiang +5
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces…
End-to-End Object Detection with Fully Convolutional Network
Jianfeng Wang, Lin Song, Zeming Li +3
Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS…
Learning Dynamic Routing for Semantic Segmentation
Yanwei Li, Lin Song, Yukang Chen +4
Recently, numerous handcrafted and searched networks have been applied for semantic segmentation. However, previous works intend to handle inputs with various scales in pre-defined…
Learnable Tree Filter for Structure-preserving Feature Transform
Lin Song, Yanwei Li, Zeming Li +4
Learning discriminative global features plays a vital role in semantic segmentation. And most of the existing methods adopt stacks of local convolutions or non-local blocks to capt…
TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection
Lin Song, Shiwei Zhang, Gang Yu +1
Current state-of-the-art approaches for spatio-temporal action detection have achieved impressive results but remain unsatisfactory for temporal extent detection. The main reason c…