93 citations · 149 across the 12 of their papers we have counts for
11 papers · 1 filter
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…
Vision Learners Meet Web Image-Text Pairs
Bingchen Zhao, Quan Cui, Hao Wu +3
Many self-supervised learning methods are pre-trained on the well-curated ImageNet-1K dataset. In this work, given the excellent scalability of web data, we consider self-supervise…
Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective
Quan Cui, Bingchen Zhao, Zhao-Min Chen +5
This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i.e., image classification.…
Contrastive Vision-Language Pre-training with Limited Resources
Quan Cui, Boyan Zhou, Yu Guo +4
Pioneering dual-encoder pre-training works (e.g., CLIP and ALIGN) have revealed the potential of aligning multi-modal representations with contrastive learning. However, these work…
PP-YOLOv2: A Practical Object Detector
Xin Huang, Xinxin Wang, Wenyu Lv +10
Being effective and efficient is essential to an object detector for practical use. To meet these two concerns, we comprehensively evaluate a collection of existing refinements to…
OTA: Optimal Transport Assignment for Object Detection
Zheng Ge, Songtao Liu, Zeming Li +2
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we…
LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
Zheng Ge, Jianfeng Wang, Xin Huang +2
Label assignment has been widely studied in general object detection because of its great impact on detectors' performance. However, none of these works focus on label assignment i…
ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval
Quan Cui, Qing-Yuan Jiang, Xiu-Shen Wei +2
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional…
Delving into the Imbalance of Positive Proposals in Two-stage Object Detection
Zheng Ge, Zequn Jie, Xin Huang +2
Imbalance issue is a major yet unsolved bottleneck for the current object detection models. In this work, we observe two crucial yet never discussed imbalance issues. The first imb…
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing
Xin Huang, Zheng Ge, Zequn Jie +1
Although significant progress has been made in pedestrian detection recently, pedestrian detection in crowded scenes is still challenging. The heavy occlusion between pedestrians i…
PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression
Zheng Ge, Zequn Jie, Xin Huang +2
Detecting human bodies in highly crowded scenes is a challenging problem. Two main reasons result in such a problem: 1). weak visual cues of heavily occluded instances can hardly p…