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20202026
most citedGeneralized Few-Shot Object Detection without Forgetting

21 citations · 35 across the 6 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision

Ruiyan Han, Zhen Fang, XinYu Sun +9

While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledg…

cs.CV20232 cited

DiT: Efficient Vision Transformers with Dynamic Token Routing

Yuchen Ma, Zhengcong Fei, Junshi Huang

Recently, the tokens of images share the same static data flow in many dense networks. However, challenges arise from the variance among the objects in images, such as large variat…

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…