JiJingYu / ComputerVisionSummarization

The summary of computer vision

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1. CVPR Paper

All the paper is available at official website.

The offline list of paper is available at this

1.1. All CVPR2018 Paper

1.1.1. Tracking

Paper ID Type Title
122 Poster Detect-and-Track: Efficient Pose Estimation in Videos
255 Poster Multi-Cue Correlation Filters for Robust Visual Tracking
281 Spotlight Tracking Multiple Objects Outside the Line of Sight using Speckle Imaging
281 Poster Tracking Multiple Objects Outside the Line of Sight using Speckle Imaging
369 Oral Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies
369 Poster Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies
423 Spotlight Fast and Accurate Online Video Object Segmentation via Tracking Parts
423 Poster Fast and Accurate Online Video Object Segmentation via Tracking Parts
678 Poster Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
736 Spotlight GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB
736 Poster GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB
890 Poster CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles
892 Poster Context-aware Deep Feature Compression for High-speed Visual Tracking
1022 Poster A Benchmark for Articulated Human Pose Estimation and Tracking
1194 Poster Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning
1264 Poster End-to-end Flow Correlation Tracking with Spatial-temporal Attention
1280 Spotlight VITAL: VIsual Tracking via Adversarial Learning
1280 Poster VITAL: VIsual Tracking via Adversarial Learning
1304 Poster SINT++: Robust Visual Tracking via Adversarial Hard Positive Generation
1353 Poster Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
1439 Poster Efficient Diverse Ensemble for Discriminative Co-Tracking
1494 Poster Correlation Tracking via Joint Discrimination and Reliability Learning
1676 Spotlight Learning Spatial-Aware Regressions for Visual Tracking
1676 Poster Learning Spatial-Aware Regressions for Visual Tracking
1679 Poster Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes
1949 Poster Rolling Shutter and Radial Distortion are Features for High Frame Rate Multi-camera Tracking
2129 Poster High-speed Tracking with Multi-kernel Correlation Filters
2628 Poster A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
2951 Spotlight High Performance Visual Tracking with Siamese Region Proposal Network
2951 Poster High Performance Visual Tracking with Siamese Region Proposal Network
3013 Oral Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
3013 Poster Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
3292 Spotlight MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
3292 Poster MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
3502 Poster A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos
3583 Poster Towards dense object tracking in a 2D honeybee hive
3817 Spotlight Good Appearance Features for Multi-Target Multi-Camera Tracking
3817 Poster Good Appearance Features for Multi-Target Multi-Camera Tracking
3980 Poster A Twofold Siamese Network for Real-Time Object Tracking

2. Video collection

2.1. Link

https://pan.baidu.com/s/1eSIVG90

2.2. video rank

  • holoportation_ virtual 3D teleportation in real-time (Microsoft Research).mp4
  • Realtime Multi-Person 2D Human Pose Estimation using Part Affinity Fields, CVPR 2017 Oral
  • Full-Resolution Residual Networks (FRRNs) for Semantic Image Segmentation in Street Scenes
  • YOLO v2
  • DeepGlint CVPR2016

3. Detection

4. Sementation

4.1. mask rcnn

The mask rcnn is proposed by KaiMing, and implied in github repostory

  • mask rcnn extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.

  • output:

    • a class label
    • a bounding-box offset
    • object mask
  • It can run at 5 fps and training on COCO takes one to two days on a single 8-GPU machine.

  • It has another application: human pose estimation, instance segementation, bounding-box object detection, and person keypoint detection, camera calibration.

    • By viewing each keypoint as a one-hot binary mask, it can estimate human pose.
  • It belongs to the instance segmentation field.

I have the mask rcnn in bus scene.

It performs well. This is all the result

5. Lip language

It's an amazing thing that training lip language recogition

5.1. Learning Lip Sync from Audio

  • given the audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync. see video

6. Some Interesting

6.1. Aging photo prediction

  • takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination. see video

6.2. D panorama

7. Tracking

7.1. MOT

method name title paper author rate
CDA_DDALv2 Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking TPAMI MLA Bae, Seung-Hwan, and Kuk-Jin Yoon. reading now
FWT Fusion of Head and Full-Body Detectors for Multi-Object Tracking CVPR18 Roberto Henschel, Laura Leal-Taixe, Daniel Cremers, Bodo Rosenhahn reading now
LMP Multiple people tracking by lifted multicut and person re-identification CVPR17 Tang, Siyu, et al. reading now
NLLMPa Joint graph decomposition & node labeling: Problem, algorithms, applications. CVPR17 Levinkov, Evgeny, et al. reading now
QuadMOT16 Multi-Object Tracking with Quadruplet Convolutional Neural Networks CVPR17 Son, Jeany, et al. reading now
EDMT Enhancing Detection Model for Multiple Hypothesis Tracking CVPR17w Chen, Jiahui, et al. reading now
AMIR Tracking the untrackable: Learning to track multiple cues with long-term dependencies ICCV17 Sadeghian, Amir, Alexandre Alahi, and Silvio Savarese. reading now
STAM16 Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism. ICCV17 Chu, Qi, et al. reading now
LINF1 Improving Multi-Frame Data Association with Sparse Representations for Robust Near-Online Multi-Object Tracking ECCV16 L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle reading now
EAMTT Multi-target tracking with strong and weak detections ECCV16w R. Sanchez-Matilla, F. Poiesi, A. Cavallaro reading now
LTTSC-CRF Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection ECCV16w Le, Nam, Alexander Heili, and Jean-Marc Odobez. reading now

7.2. Correlation Filter

7.3. End-to-end representation learning for correlation filter based tracking

It is a tracking method based on deep learning. This author designed a network consisting of correlation filter layer, who solved the backpropagation program

  • I have tried this method. But it doesn't work well and have some test failure cases, as following

  • abstract We present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multidimensional features in the primal and dual domain.

  • video, paper, matlab code, python code

  • advantage:

    • It's an end-to-end tracking method, which can be trained directly.
    • It can run in real-time.
  • disadvantage:

    • It's will drift with the object occlusion
    • It's will scale wrongly with the object enlarge or being small.
  • My opion:

    • Tracking should be combined both the object feature itself and the context feature.

7.4. Attentional Correlation Filter Network for Adaptive Visual Tracking

  • paper, video, code
  • advantage:
  • disadvantage:
    • slow. Cannot run in real-time.

7.5. Context-Aware Correlation Filter Tracking

  • Bas

8. Action Recognition

9. Reconstruction

10. Detection

11. Sementation

12. Action Recognition

13. Point Cloud Representation

14. Summary

  • Mask RCNN is amazing, but it's not fast enough for real time detection.
  • There are lots of computer vision tasks need to be done, and only few tasks are finished. Obejct recognition is the simplest task, which is extremly handled and the rate of recognition is more than that of human beings. But, the majority tasks are still need to be done, such as: action recogition, action predict, 3D object recognition, 3D object representation, 3D action recognition, represention of speak, smell, feel and vision. Machine vision is the kernel task for robot intelligence. So don't worry about nothing to do in this field.

15. Reference

http://www.themtank.org/a-year-in-computer-vision

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The summary of computer vision


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