Self-Supervised Learning via

Conditional Motion Propagation

Xiaohang Zhan1      Xingang Pan1      Ziwei Liu1      Dahua Lin1      Chen Change Loy2
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019


Intelligent agent naturally learns from motion. Various self-supervised algorithms have leveraged motion cues to learn effective visual representations. The hurdle here is that motion is both ambiguous and complex, rendering previous works either suffer from degraded learning efficacy, or resort to strong assumptions on object motions. In this work, we design a new learning-from-motion paradigm to bridge these gaps. Instead of explicitly modeling the motion probabilities, we design the pretext task as a conditional motion propagation problem. Given an input image and several sparse flow guidance vectors on it, our framework seeks to recover the full-image motion. Compared to other alternatives, our framework has several appealing properties: (1) Using sparse flow guidance during training resolves the inherent motion ambiguity, and thus easing feature learning. (2) Solving the pretext task of conditional motion propagation encourages the emergence of kinematically-sound representations that poss greater expressive power. Extensive experiments demonstrate that our framework learns structural and coherent features; and achieves state-of-the-art self-supervision performance on several downstream tasks including semantic segmentation, instance segmentation, and human parsing. Furthermore, our framework is successfully extended to several useful applications such as semi-automatic pixel-level annotation.


Code and Models

Representation Learning

1. Pascal VOC 2012 Semantic Segmentation (AlexNet)

Method (AlexNet)Supervision (data amount)% mIoU
Krizhevsky et al. [1]ImageNet labels (1.3M)48.0
Random- (0)19.8
Pathak et al. [2]In-painting (1.2M)29.7
Zhang et al. [3]Colorization (1.3M)35.6
Zhang et al. [4]Split-Brain (1.3M)36.0
Noroozi et al. [5]Counting (1.3M)36.6
Noroozi et al. [6]Jigsaw (1.3M)37.6
Noroozi et al. [7]Jigsaw++ (1.3M)38.1
Jenni et al. [8]Spot-Artifacts (1.3M)38.1
Larsson et al. [9]Colorization (3.7M)38.4
Gidaris et al. [10]Rotation (1.3M)39.1
Pathak et al. [11]*Motion Segmentation (1.6M)39.7
Walker et al. [12]*Flow Prediction (3.22M)40.4
Mundhenk et al. [13]Context (1.3M)40.6
Mahendran et al. [14]Flow Similarity (1.6M)41.4
OursCMP (1.26M)42.9
OursCMP (3.22M)44.5
Caron et al. [15]Clustering (1.3M)45.1
Feng et al. [16]Feature Decoupling (1.3M)45.3

2. Pascal VOC 2012 Semantic Segmentation (ResNet-50)

Method (ResNet-50)Supervision (data amount)% mIoU
Krizhevsky et al. [1]ImageNet labels (1.2M)69.0
Random- (0)42.4
Walker et al. [12]*Flow Prediction (1.26M)54.5
Pathak et al. [11]*Motion Segmentation (1.6M)54.6
OursCMP (1.26M)59.0

3. COCO 2017 Instance Segmentation (ResNet-50)

Method (ResNet-50)Supervision (data amount)Det. (% mAP)Seg. (% mAP)
Krizhevsky et al. [1]ImageNet labels (1.2M)37.234.1
Random- (0)19.718.8
Pathak et al. [11]*Motion Segmentation (1.6M)27.725.8
Walker et al. [12]*Flow Prediction (1.26M)31.529.2
OursCMP (1.26M)32.329.8

4. LIP Human Parsing (ResNet-50)

Method (ResNet-50)Supervision (data amount)Single-Person (% mIoU)Multi-Person (% mIoU)
Krizhevsky et al. [1]ImageNet labels (1.2M)42.555.4
Random- (0)32.535.0
Pathak et al. [11]*Motion Segmentation (1.6M)36.650.9
Walker et al. [12]*Flow Prediction (1.26M)36.752.5
OursCMP (1.26M)36.951.8
OursCMP (4.57M)40.252.9
Note: Methods marked * have not reported the results in their paper, hence we reimplemented them to obtain the results.


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CMP testing results. In each group, the first row includes the original image and the guidance arrows given by users, the second row shows the predicted motion. The results demonstrate three characteristics of CMP: 1. CMP propagates motion on the whole rigid part. 2. CMP can infer whether a part is shifting or rotating (motion uniform if shifting, fading if rotating) as shown in the first group. 3. The results are physically feasible. For example, in the second group, given a single guidance vector on the left thigh, there are also responses on left shank and foot. It is due to the observation that the left leg is hovering. However, in the last column, although given a guidance vector on the right leg, the right foot keeps still because it is on the ground.


 author = {Zhan, Xiaohang and Pan, Xingang and Liu, Ziwei and Lin, Dahua and Loy, Chen Change},
 title = {Self-Supervised Learning via Conditional Motion Propagation},
 booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)},
 month = {June},
 year = {2019}