For the figure, the each step corresponds to a processed batch(16).
Part-level Dataset Available for Augmentation
Sources
Single Instance
Complicated Scene
- Real
- Synthetic
chair_dataset
- image_1~image_3000: kaggle furniture image dataset
- image_3001~image_4887: DeepFurniture
desk_dataset
- image_1~image_700: pix3d
home_appliance_dataset
- image_1~image_3000: kaggle furniture image dataset fridge only
- image_3001~image_3429: DeepFurniture home-appliance category
shelves_dataset
- image_1~image_3000: kaggle furniture image dataset
- image_3001~image_3243: pix3d wardrobe category
- image_3244~image_3604: pix3d bookcase category
sofa_dataset
- image_1~image_1947: pix3d
- image_1498~image_3888: DeepFurniture
table_dataset
- image_1~image_3000: kaggle furniture image dataset
- image_3001~image_4870: pix3d
- image_4871~image_7293: DeepFurniture
tool_dataset
- image_1~image_115: pix3d
- image_116~image_1441:kaggle mechanical tool dataset hammer
- image_1442~image_1812:kaggle mechanical tool dataset plier
- image_1813~image_3138:kaggle mechanical tool dataset screw driver
- image_3139~image_4469:kaggle mechanical tool dataset wrench
tv_dataset
- image_1~image_3000: kaggle furniture image dataset
PSR Dataset
Cabinet
from shelves_dataset 3000 images -> ? train & ? val samples
Desk (Processing)
from desk_dataset 699 images -> ? train & ? val samples
Tool (Processing)
from tool_dataset (simplified) 1335 images -> 2729 train & 911 val samples
Furniture (Processing)
from 130k Images/furniture 1983 images -> ? train & ? val samples
Coco (Processing)
from coco2017 2212 images -> ? train & ? val samples
current total:
- train: 12,415-686(bg)=11729
- val: 3,028-182(bg)=2846
Feature Pyramid Networks for Object Detection
识别不同尺寸的物体是目标检测中的一个基本挑战，而特征金字塔一直是多尺度目标检测中的一个基本的组成部分，但是由于特征金字塔计算量大，会拖慢整个检测速度，所以大多数方法为了检测速度而尽可能的去避免使用特征金字塔，而是只使用高层的特征来进行预测。高层的特征虽然包含了丰富的语义信息，但是由于低分辨率，很难准确地保存物体的位置信息。与之相反，低层的特征虽然语义信息较少，但是由于分辨率高，就可以准确地包含物体位置信息。所以如果可以将低层的特征和高层的特征融合起来，就能得到一个识别和定位都准确的目标检测系统。所以本文就旨在设计出这样的一个结构来使得检测准确且快速。
Deformable Convolutional Networks
Used in [[CenterNet]]
Associative Embedding= End-to-End Learning for Joint Detection and Grouping
Q&A
1
What is standard dense supervised learning? Mentioned in [[CenterNet]].
Standard dense supervised learning typically refers to a supervised learning setup where:
- Standard supervised learning means:
- You have input data X and corresponding ground truth labels Y.
- The goal is to train a model $f_\theta(X)$ that maps inputs to outputs by minimizing a loss function (e.g., cross-entropy, MSE) between the predicted labels and ground truth.
- The training dataset is fully labeled (i.e., each input has a corresponding label).
- Dense refers to:
- A per-pixel or per-element prediction task, where every element in the input gets a corresponding label.
- Common in vision tasks like:
- Semantic segmentation (each pixel is labeled with a class).
- Depth estimation (each pixel has a depth value).
- Optical flow (each pixel has a motion vector).
- Surface normal estimation (each pixel has a 3D orientation vector).
In contrast to sparse supervision, where only a subset of the input (e.g., bounding boxes, keypoints) is labeled, dense supervision provides full annotations for every relevant part of the input.
Example
In semantic segmentation:
- Input: an RGB image (e.g., 512×512 pixels).
- Output: a label map of the same size (512×512), where each pixel has a class label like “road”, “car”, “sky”, etc.
- Model: often a Fully Convolutional Network (FCN) or encoder-decoder like U-Net or DeepLab.
- Loss: usually pixel-wise cross-entropy.
