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# Review: IEF — Iterative Error Feedback (Human Pose Estimation)

Last Updated on July 20, 2023 by Editorial Team

#### Author(s): Sik-Ho Tsang

Originally published on Towards AI.

## Outperforms Tompson NIPS’14, and Tompson CVPR’15

In this story, IEF (Iterative Error Feedback), by UC Berkeley, is briefly reviewed. Instead of directly predicting the outputs in one go, a self-correcting model is used to progressively change an initial solution by feeding back error predictions. This is a 2016 CVPR paper with more than 300 citations. (

Sik-Ho Tsang @ Medium)

## Outline

1. IEF Architecture Overview
2. Training
3. Ablation Study
4. Comparison with State-Of-The-Art Approaches

## 1. IEF Architecture Overview

• xt, the concatenation of input image I with a visual representation g, input to the model f. With 3-channel RGB images and K heatmaps for K key points, there are K+3 channels for xt.
• Then model f output/predict a “correction” et.
• yt+1 = et + yt to obtain a new y.
• yt+1 is converted into a visual representation of g. g is 2D Gaussian having a fixed standard deviation and centered on the keypoint location.
• This procedure is initialized with a guess of the output (y0) and is repeated until a predetermined termination criterion is met.
• y0 is the median of ground truth 2D keypoint locations on training images

## 2.1. Loss Function

• et and e(y, yt) are the predicted and target bounded corrections, respectively.
• h is a measure of distance, such as a quadratic loss.
• T is the number of correction steps taken by the model. T=4.

## 2.2. Fixed Path Consolidation (FPC)

• The above is the iterative process, but we only got the final ground-truth. We do not have the intermediate ground-truth.
• The simplest strategy is to predefine yt for every iteration using a set of fixed corrections e(y, yt) starting from y0, obtaining (y0, y1, …, y).
• The target bounded corrections for every iteration are computed using a function:
• where k is the k-th keypoint. L denotes the maximum displacement for each keypoint location. L = 20 pixels. u^ is the unit vector of u.

## 2.3. ConvNet

• ImageNet pre-trained GoogLeNet is used.
• The conv-1 filters are modified to be operated on 20 channel inputs. The weights of the first three conv-1 channels were initialized using the weights learned by pre-training on Imagenet. The weights corresponding to the remaining 17 channels were randomly initialized with the Gaussian noise of variance 0.1.
• The last layer of 1000 units that predicted the Imagenet classes is discarded. It is replaced with a layer containing 32 units, encoding the continuous 2D correction expressed in Cartesian coordinates (the 17th ”keypoint” is the location of one point anywhere inside a person used in both training and testing).

## 3.1. Iterative v/s Direct Prediction

• IEF that additively regresses to keypoint locations achieves PCKh-0.5 of 81.0 as compared to PCKh of 74.8 achieved by directly regressing to the keypoints.

## 3.2. Iterative Error Feedback v/s Iterative Direct Prediction

• IEF achieves PCKh-0.5 of 81.0 as compared to PCKh of 73.4 by iterative direct prediction.

## 3.3. Importance of Fixed Path Consolidation (FPC)

• Without FPC, the performance drops by almost 10 PCKh points on the validation set.

## 3.4. Learning Structure Outputs

• As a baseline, regression gets 64.6.
• The IEF model with a single additional input channel for the left knee gets PCKh of 69.2.
• Furthermore, the IEF model over both the left knee and left hip gets PCKh of 72.8.
• Finally, modeling all joints together with the image obtains a PCKh of 73.8.

## 4.2. LSP

• There is also no marking point on the torsos, so the 17th keypoints are initialized to be the center of the image.
• IEF outperforms SOTA such as Tompson NIPS’14.

## Reference

[2016 CVPR] [IEF]
Human Pose Estimation with Iterative Error Feedback

## My Previous Reviews

Image Classification [LeNet] [AlexNet] [Maxout] [NIN] [ZFNet] [VGGNet] [Highway] [SPPNet] [PReLU-Net] [STN] [DeepImage] [SqueezeNet] [GoogLeNet / Inception-v1] [BN-Inception / Inception-v2] [Inception-v3] [Inception-v4] [Xception] [MobileNetV1] [ResNet] [Pre-Activation ResNet] [RiR] [RoR] [Stochastic Depth] [WRN] [ResNet-38] [Shake-Shake] [FractalNet] [Trimps-Soushen] [PolyNet] [ResNeXt] [DenseNet] [PyramidNet] [DRN] [DPN] [Residual Attention Network] [DMRNet / DFN-MR] [IGCNet / IGCV1] [MSDNet] [ShuffleNet V1] [SENet] [NASNet] [MobileNetV2]

Object Detection [OverFeat] [R-CNN] [Fast R-CNN] [Faster R-CNN] [MR-CNN & S-CNN] [DeepID-Net] [CRAFT] [R-FCN] [ION] [MultiPathNet] [NoC] [Hikvision] [GBD-Net / GBD-v1 & GBD-v2] [G-RMI] [TDM] [SSD] [DSSD] [YOLOv1] [YOLOv2 / YOLO9000] [YOLOv3] [FPN] [RetinaNet] [DCN]

Semantic Segmentation [FCN] [DeconvNet] [DeepLabv1 & DeepLabv2] [CRF-RNN] [SegNet] [ParseNet] [DilatedNet] [DRN] [RefineNet] [GCN] [PSPNet] [DeepLabv3] [ResNet-38] [ResNet-DUC-HDC] [LC] [FC-DenseNet] [IDW-CNN] [DIS] [SDN] [DeepLabv3+]

Biomedical Image Segmentation [CUMedVision1] [CUMedVision2 / DCAN] [U-Net] [CFS-FCN] [U-Net+ResNet] [MultiChannel] [V-Net] [3D U-Net] [M²FCN] [SA] [QSA+QNT] [3D U-Net+ResNet] [Cascaded 3D U-Net] [Attention U-Net] [RU-Net & R2U-Net] [VoxResNet] [DenseVoxNet][UNet++] [H-DenseUNet] [DUNet]

Super Resolution [SRCNN] [FSRCNN] [VDSR] [ESPCN] [RED-Net] [DRCN] [DRRN] [LapSRN & MS-LapSRN] [SRDenseNet] [SR+STN]

Human Pose Estimation [DeepPose] [Tompson NIPS’14] [Tompson CVPR’15] [CPM] [FCGN] [IEF]

Codec Post-Processing [ARCNN] [Lin DCC’16] [IFCNN] [Li ICME’17] [VRCNN] [DCAD] [DS-CNN]

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Published via Towards AI