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1. Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines

2. NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines

3. Bridging the gap between image coding for machines and humans

4. Leveraging progressive model and overfitting for efficient learned image compression

5. Adaptation and Attention for Neural Video Coding

6. Lossless Image Compression Using a Multi-Scale Progressive Statistical Model

7. Learned Image Coding for Machines: A Content-Adaptive Approach

8. Image coding for machines: an end-to-end learned approach

9. Learning to Learn to Compress

10. Efficient Adaptation of Neural Network Filter for Video Compression

11. End-to-End Learning for Video Frame Compression with Self-Attention

12. A Compression Objective and a Cycle Loss for Neural Image Compression

13. Compressing Weight-updates for Image Artifacts Removal Neural Networks

14. Compressibility Loss for Neural Network Weights

15. Block-optimized Variable Bit Rate Neural Image Compression

16. Object Detection in Equirectangular Panorama

17. Depth Masked Discriminative Correlation Filter

18. Saliency-Enhanced Robust Visual Tracking

19. Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations

20. A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure

21. Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels

22. NN-VVC: A Hybrid Learned-Conventional Video Codec Targeting Humans and Machines.

23. Video Ladder Networks

27. Sensor-Based Analysis of User Generated Video for Multi-camera Video Remixing

29. Low-precision post-filtering in video coding

37. Overview of the Neural Network Compression and Representation (NNR) Standard

43. Adaptation and Attention for Neural Video Coding

44. Hybrid Pruning And Sparsification

50. L2C – Learning to Learn to Compress

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