activity
20192023
most citedBridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

108 citations · 143 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2023

SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency

Yan Wang, Yuhang Li, Ruihao Gong +8

Extensive studies have shown that deep learning models are vulnerable to adversarial and natural noises, yet little is known about model robustness on noises caused by different sy…

cs.CV2022★ 1 cited

Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative Learning

Bo Li, Yongqiang Yao, Jingru Tan +4

Data in real-world object detection often exhibits the long-tailed distribution. Existing solutions tackle this problem by mitigating the competition between the head and tail cate…

cs.CV2022★ 2 cited

The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition

Jingru Tan, Bo Li, Xin Lu +4

Long-tail distribution is widely spread in real-world applications. Due to the extremely small ratio of instances, tail categories often show inferior accuracy. In this paper, we f…

cs.CV2022★ 12 cited

Towards Frame Rate Agnostic Multi-Object Tracking

Weitao Feng, Lei Bai, Yongqiang Yao +2

Multi-Object Tracking (MOT) is one of the most fundamental computer vision tasks that contributes to various video analysis applications. Despite the recent promising progress, cur…

cs.CV2022★ 4 cited

Equalized Focal Loss for Dense Long-Tailed Object Detection

Bo Li, Yongqiang Yao, Jingru Tan +4

Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. In practice, one-stage detectors…

cs.CV2020★ 16 cited

Cross-dataset Training for Class Increasing Object Detection

Yongqiang Yao, Yan Wang, Yu Guo +3

We present a conceptually simple, flexible and general framework for cross-dataset training in object detection. Given two or more already labeled datasets that target for differen…

cs.CV2019★ 108 cited

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

Shifeng Zhang, Cheng Chi, Yongqiang Yao +2

Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In t…