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MultiPoseNet: Fast Multi-Person Pose Estimation Using Pose Residual Network

Muhammed Kocabas and Salih Karagoz and Emre Akbas

Computer Vision – ECCV 2018 - 2018 via Local CrossRef

Keywords:

Muhammed Kocabas and Salih Karagoz and Emre Akbas

Computer Vision – ECCV 2018 - 2018 via Local CrossRef

Keywords:

[link]
The method is a multi-task learning model performing person detection, keypoint detection, person segmentation, and pose estimation. It is a bottom-up approach as it first localizes identity-free semantics and then group them into instances. https://i.imgur.com/kRs9687.png Model structure: - **Backbone**. A feature extractor is presented by ResNet-(50 or 101) with one [Feature Pyramid Network](https://arxiv.org/pdf/1612.03144.pdf) (FPN) for keypoint branch and one for person detection branch. FPN enhances extracted features through multi-level representation. - **Keypoint detection** detects keypoints as well as produces a pixel-level segmentation mask. https://i.imgur.com/XFAi3ga.png FPN features $K_i$ are processed with multiple $3\times3$ convolutions followed by concatenation and final $1\times1$ convolution to obtain predictions for each keypoint, as well as segmentation mask (see Figure for details). This results in #keypoints_in_dataset_per_person + 1 output layers. Additionally, intermediate supervision (i.e. loss) is applied at the FPN outputs. $L_2$ loss between predictions and Gaussian peaks at the keypoint locations is used. Similarly, $L_2$ loss is applied for segmentation predictions and corresponding ground truth masks. - **Person detection** is essentially a [RetinaNet](https://arxiv.org/pdf/1708.02002.pdf), a one-stage object detector, modified to only handle *person* class. - **Pose estimation**. Given initial keypoint predictions, Pose Estimation Network (PRN) selects a single keypoint for each class. https://i.imgur.com/k8wNP5p.png During inference, PRN takes cropped outputs from keypoint detection branch defined by the predicted bounding boxes from the person detection branch, resizes it to a fixed size, and forwards it through a multilayer perceptron with residual connection. During the training, the same process is performed, except the cropped keypoints come from the ground truth annotation defined by a labeled bounding box. This model is not an end-to-end trainable model. While keypoint and person detection branches can, in theory, be trained simultaneously, PRN network requires separate training. **Personal note**. Interestingly, PRN training with ground truth inputs (i.e. "perfect" inputs) only reaches 89.4 mAP validation score which is surprisingly quite far from the max possible score. This presumably means that even if preceding networks or branches perform god-like, the PRN might become a bottleneck in the performance. Therefore, more efforts should be directed to PRN itself. Moreover, modifying the network to support end-to-end training might help in boosting the performance. Open-source implementations used to make sure the paper apprehension is correct: [link1](https://github.com/LiMeng95/MultiPoseNet.pytorch), [link2](https://github.com/IcewineChen/pytorch-MultiPoseNet). |

Videos as Space-Time Region Graphs

Xiaolong Wang and Abhinav Gupta

Computer Vision – ECCV 2018 - 2018 via Local CrossRef

Keywords:

Xiaolong Wang and Abhinav Gupta

Computer Vision – ECCV 2018 - 2018 via Local CrossRef

Keywords:

[link]
This paper tackles the challenge of action recognition by representing a video as space-time graphs: **similarity graph** captures the relationship between correlated objects in the video while the **spatial-temporal graph** captures the interaction between objects. The algorithm is composed of several modules: https://i.imgur.com/DGacPVo.png 1. **Inflated 3D (I3D) network**. In essence, it is usual 2D CNN (e.g. ResNet-50) converted to 3D CNN by copying 2D weights along an additional dimension and subsequent renormalization. The network takes *batch x 3 x 32 x 224 x 224* tensor input and outputs *batch x 16 x 14 x 14*. 2. **Region Proposal Network (RPN)**. This is the same RPN used to predict initial bounding boxes in two-stage detectors like Faster R-CNN. Specifically, it predicts a predefined number of bounding boxes on every other frame of the input (initially input is 32 frames, thus 16 frames are used) to match the temporal dimension of I3D network's output. Then, I3D network output features and projected on them bounding boxes are passed to ROIAlign to obtain temporal features for each object proposal. Fortunately, PyTorch comes with a [pretrained Faster R-CNN on MSCOCO](https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html) which can be easily cut to have only RPN functionality. 3. **Similarity Graph**. This graph represents a feature similarity between different objects in a video. Having features $x_i$ extracted by RPN+ROIAlign for every bounding box predictions in a video, the similarity between any pair of objects is computed as $F(x_i, x_j) = (wx_i)^T * (w'x_j)$, where $w$ and $w'$ are learnable transformation weights. Softmax normalization is performed on each edge on the graph connected to a current node $i$. Graph convolutional network is represented as several graph convolutional layers with ReLU activation in between. Graph construction and convolutions can be conveniently implemented using [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric). 4. **Spatial-Temporal Graph**. This graph captures a spatial and temporal relationship between objects in neighboring frames. To construct a graph $G_{i,j}^{front}$, we need to iterate through every bounding box in frame $t$ and compute Intersection over Union (IoU) with every object in frame $t+1$. The IoU value serves as the weight of the edge connecting nodes (ROI aligned features from RPN) $i$ and $j$. The edge values are normalized so that the sum of edge values connected to proposal $i$ will be 1. In a similar manner, the backward graph $G_{i,j}^{back}$ is defined by analyzing frames $t$ and $t-1$. 5. **Classification Head**. The classification head takes two inputs. One is coming from average pooled features from I3D model resulting in *1 x 512* tensor. The other one is from pooled sum of features (i.e. *1 x 512* tensor) from the graph convolutional networks defined above. Both inputs are concatenated and fed to Fully-Connected (FC) layer to perform final multi-label (or multi-class) classification. **Dataset**. The authors have tested the proposed algorithm on [Something-Something](https://20bn.com/datasets/something-something) and [Charades](https://allenai.org/plato/charades/) datasets. For the first dataset, a softmax loss function is used, while the second one utilizes binary sigmoid loss to handle a multi-label property. The input data is sampled at 6fps, covering about 5 seconds of a video input. **My take**. I think this paper is a great engineering effort. While the paper is easy to understand at the high-level, implementing it is much harder partially due to unclear/misleading writing/description. I have challenged myself with [reproducing this paper](https://github.com/BAILOOL/Videos-as-Space-Time-Region-Graphs). It is work in progress, so be careful not to damage your PC and eyes :-) |

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