Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection

Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training. However, major challenges remain: (1) differentiation of object instances can be ambiguous; (2) detectors tend to focus on discriminative parts rather than entire objects; (3) without ground truth, object proposals have to be redundant for high recalls, causing significant memory consumption... (read more)

PDF Abstract CVPR 2020 PDF CVPR 2020 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
RESULT BENCHMARK
Weakly Supervised Object Detection COCO test-dev wetectron(single-model, VGG16) AP50 24.8 # 1
Weakly Supervised Object Detection PASCAL VOC 2007 wetectron(single-model) MAP 54.9 # 3
Weakly Supervised Object Detection PASCAL VOC 2007 wetectron (single mode, 07+12) MAP 58.1 # 1
Weakly Supervised Object Detection PASCAL VOC 2012 test wetectron(single-model) MAP 52.1 # 2

Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
ReLU
Activation Functions
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Bottleneck Residual Block
Skip Connection Blocks
Global Average Pooling
Pooling Operations
Residual Block
Skip Connection Blocks
Kaiming Initialization
Initialization
Max Pooling
Pooling Operations
DropBlock
Regularization
Residual Connection
Skip Connections
Convolution
Convolutions
ResNet
Convolutional Neural Networks