activity
20202022
most citedGeneralized Few-Shot Object Detection without Forgetting

21 citations · 33 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20222 cited

Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment

Yuchen Ma, Yanbei Chen, Zeynep Akata

Recent advances have indicated the strengths of self-supervised pre-training for improving representation learning on downstream tasks. Existing works often utilize self-supervised…

cs.CV202121 cited

Generalized Few-Shot Object Detection without Forgetting

Zhibo Fan, Yuchen Ma, Zeming Li +1

Recently few-shot object detection is widely adopted to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we clai…

cs.CV20213 cited

IQDet: Instance-wise Quality Distribution Sampling for Object Detection

Yuchen Ma, Songtao Liu, Zeming Li +1

We propose a dense object detector with an instance-wise sampling strategy, named IQDet. Instead of using human prior sampling strategies, we first extract the regional feature of…

cs.CV20207 cited

Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track

Zeming Li, Yuchen Ma, Yukang Chen +2

In this report, we present our object detection/instance segmentation system, MegDetV2, which works in a two-pass fashion, first to detect instances then to obtain segmentation. Ou…

cs.CV2020

BorderDet: Border Feature for Dense Object Detection

Han Qiu, Yuchen Ma, Zeming Li +2

Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted t…