539 citations · 843 across the 24 of their papers we have counts for
23 papers · 1 filter
PET-DINO: Unifying Visual Cues into Grounding DINO with Prompt-Enriched Training
Weifu Fu, Jinyang Li, Bin-Bin Gao +6
Open-Set Object Detection (OSOD) enables recognition of novel categories beyond fixed classes but faces challenges in aligning text representations with complex visual concepts and…
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang +6
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The ex…
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Donghao Zhou, Jialin Li, Jinpeng Li +7
Large-scale well-annotated datasets are of great importance for training an effective object detector. However, obtaining accurate bounding box annotations is laborious and demandi…
Clustered-patch Element Connection for Few-shot Learning
Jinxiang Lai, Siqian Yang, Junhong Zhou +5
Weak feature representation problem has influenced the performance of few-shot classification task for a long time. To alleviate this problem, recent researchers build connections…
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Wu +8
Recent Few-Shot Learning (FSL) methods put emphasis on generating a discriminative embedding features to precisely measure the similarity between support and query sets. Current CN…
HDNet: A Hierarchically Decoupled Network for Crowd Counting
Chenliang Gu, Changan Wang, Bin-Bin Gao +2
Recently, density map regression-based methods have dominated in crowd counting owing to their excellent fitting ability on density distribution. However, further improvement tends…
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
Jiawei Zhan, Jun Liu, Wei Tang +8
Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Al…
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
Jinxiang Lai, Siqian Yang, Guannan Jiang +9
Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the…
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Liu +6
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples. M…
How to Reduce Change Detection to Semantic Segmentation
Guo-Hua Wang, Bin-Bin Gao, Chengjie Wang
Change detection (CD) aims to identify changes that occur in an image pair taken different times. Prior methods devise specific networks from scratch to predict change masks in pix…
UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation
Jiashuo Fan, Bin Gao, Huan Jin +1
Deep neural networks (DNNs) have witnessed great successes in semantic segmentation, which requires a large number of labeled data for training. We present a novel learning framewo…
EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs
Guohao Ying, Xin He, Bin Gao +2
Generative adversarial networks (GANs) have proven successful in image generation tasks. However, GAN training is inherently unstable. Although many works try to stabilize it by ma…
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning…
A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation
Feng Luo, Bin-Bin Gao, Jiangpeng Yan +1
Boundary-based instance segmentation has drawn much attention since of its attractive efficiency. However, existing methods suffer from the difficulty in long-distance regression.…
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Most of advanced solutions exploit a metri…
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
Bin-Bin Gao, Xin-Xin Liu, Hong-Yu Zhou +2
Facial attributes (\eg, age and attractiveness) estimation performance has been greatly improved by using convolutional neural networks. However, existing methods have an inconsist…
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
Bin-Bin Gao, Hong-Yu Zhou
Multi-label image recognition is a practical and challenging task compared to single-label image classification. However, previous works may be suboptimal because of a great number…
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
Object detection aims at high speed and accuracy simultaneously. However, fast models are usually less accurate, while accurate models cannot satisfy our need for speed. A fast mod…
Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as t…
Deep Label Distribution Learning with Label Ambiguity
Bin-Bin Gao, Chao Xing, Chen-Wei Xie +2
Convolutional Neural Networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most import…
Visual Recognition Using Directional Distribution Distance
Jianxin Wu, Bin-Bin Gao, Guoqing Liu
In computer vision, an entity such as an image or video is often represented as a set of instance vectors, which can be SIFT, motion, or deep learning feature vectors extracted fro…
Deep Spatial Pyramid: The Devil is Once Again in the Details
Bin-Bin Gao, Xiu-Shen Wei, Jianxin Wu +1
In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achie…
Exploit Bounding Box Annotations for Multi-label Object Recognition
Hao Yang, Joey Tianyi Zhou, Yu Zhang +3
Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications. However, for multi-label images that conta…