1 code implementation • 15 Apr 2023 • Zhi Cai, Songtao Liu, Guodong Wang, Zheng Ge, Xiangyu Zhang, Di Huang
We propose a metric, recall of best-regressed samples, to quantitively evaluate the misalignment problem.
1 code implementation • NeurIPS 2021 • Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He, Hongbin Sun, Jian Sun, Nanning Zheng
Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.
no code implementations • 3 Dec 2022 • En Yu, Songtao Liu, Zhuoling Li, Jinrong Yang, Zeming Li, Shoudong Han, Wenbing Tao
VLM joints the information in the generated visual prompts and the textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description, which is domain invariant to different tracking scenes.
1 code implementation • 30 Sep 2022 • Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, Dinghao Wu
Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.
no code implementations • 29 Sep 2022 • Songtao Liu, Rex Ying, Hanze Dong, Lu Lin, Jinghui Chen, Dinghao Wu
However, the analysis of implicit denoising effect in graph neural networks remains open.
1 code implementation • 22 Jul 2022 • Jinrong Yang, Lin Song, Songtao Liu, Weixin Mao, Zeming Li, Xiaoping Li, Hongbin Sun, Jian Sun, Nanning Zheng
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference.
no code implementations • 21 Jul 2022 • Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Jian Sun
In this paper, we explore the performance of real time models on this metric and endow the models with the capacity of predicting the future, significantly improving the results for streaming perception.
2 code implementations • 6 Jul 2022 • HongYu Zhou, Zheng Ge, Songtao Liu, Weixin Mao, Zeming Li, Haiyan Yu, Jian Sun
To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters.
1 code implementation • CVPR 2022 • Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Jian Sun
In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem.
Ranked #1 on Real-Time Object Detection on Argoverse-HD (Full-Stack, Val) (sAP metric, using extra training data)
1 code implementation • 8 Sep 2021 • Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu
To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.
1 code implementation • 27 Jul 2021 • Songyang Zhang, Lin Song, Songtao Liu, Zheng Ge, Zeming Li, Xuming He, Jian Sun
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario.
41 code implementations • 18 Jul 2021 • Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.
Ranked #1 on Real-Time Object Detection on Argoverse-HD (Detection-Only, Val) (using extra training data)
no code implementations • CVPR 2021 • Yuchen Ma, Songtao Liu, Zeming Li, Jian Sun
We propose a dense object detector with an instance-wise sampling strategy, named IQDet.
2 code implementations • CVPR 2021 • Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, Jian Sun
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.
Ranked #73 on Object Detection on COCO test-dev
1 code implementation • 19 Jan 2021 • Zeming Li, Songtao Liu, Jian Sun
The teacher's weight is a momentum update of the student, and the teacher's BN statistics is a momentum update of those in history.
1 code implementation • 12 Jan 2021 • Zheng Ge, JianFeng Wang, Xin Huang, Songtao Liu, Osamu Yoshie
A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator.
no code implementations • 27 Nov 2020 • Songtao Liu, Zeming Li, Jian Sun
Our Faster R-CNN (ResNet50-FPN) baseline achieves 39. 8% mAP on COCO, which is on par with the state of the art self-supervised methods pre-trained on ImageNet.
2 code implementations • ECCV 2020 • Han Qiu, Yuchen Ma, Zeming Li, Songtao Liu, Jian Sun
In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature.
4 code implementations • ECCV 2020 • Jiaxi Wu, Songtao Liu, Di Huang, Yunhong Wang
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.
Ranked #16 on Few-Shot Object Detection on MS-COCO (30-shot)
2 code implementations • 7 Jul 2020 • Benjin Zhu, Jian-Feng Wang, Zhengkai Jiang, Fuhang Zong, Songtao Liu, Zeming Li, Jian Sun
During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions.
no code implementations • CVPR 2020 • Yangtao Zheng, Di Huang, Songtao Liu, Yunhong Wang
Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred.
1 code implementation • 21 Nov 2019 • Songtao Liu, Di Huang, Yunhong Wang
Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection.
Ranked #147 on Object Detection on COCO test-dev
no code implementations • 11 Nov 2019 • Songtao Liu, Lingwei Chen, Hanze Dong, ZiHao Wang, Dinghao Wu, Zengfeng Huang
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.
no code implementations • CVPR 2019 • Songtao Liu, Di Huang, Yunhong Wang
Pedestrian detection in a crowd is a very challenging issue.
Ranked #17 on Object Detection on CrowdHuman (full body)
7 code implementations • ECCV 2018 • Songtao Liu, Di Huang, Yunhong Wang
Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs.