134 results on '"Wen-Hsiao Peng"'
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2. Learning-Based Conditional Image Compression.
3. Conditional Variational Autoencoders for Hierarchical B-frame Coding.
4. Transformer-Based Learned Image Compression for Joint Decoding and Denoising.
5. Complexity-Efficiency Control With ANN-Based CTU Partitioning for Video Encoding.
6. B-CANF: Adaptive B-Frame Coding With Conditional Augmented Normalizing Flows.
7. TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception.
8. MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution.
9. Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction.
10. Hierarchical B-Frame Video Coding Using Two-Layer CANF Without Motion Coding.
11. Learned Hierarchical B-frame Coding with Adaptive Feature Modulation for YUV 4: 2: 0 Content.
12. Transformer-Based Variable-Rate Image Compression with Region-of-Interest Control.
13. Continually-Adapted Margin and Multi-Anchor Distillation for Class-Incremental Learning.
14. Sparse Tensor-based point cloud attribute compression using Augmented Normalizing Flows.
15. Transformer-based Image Compression with Variable Image Quality Objectives.
16. Learning-Based Scalable Video Coding with Spatial and Temporal Prediction.
17. Rate Adaptation for Learned Two-layer B-frame Coding without Signaling Motion Information.
18. HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar.
19. Cross-Platform Neural Video Coding: A Case Study.
20. Fast-OMRA: Fast Online Motion Resolution Adaptation for Neural B-Frame Coding.
21. ComNeck: Bridging Compressed Image Latents and Multimodal LLMs via Universal Transform-Neck.
22. Analysis of the Limitations of Further Improvement of the Efficiency of VVC-CABAC.
23. Incoming Editorial.
24. Learned Video Compression for YUV 4: 2: 0 Content Using Flow-based Conditional Inter-frame Coding.
25. A Study of Motion Coding Schemes for Learned Video Compression.
26. Content-Adaptive Motion Rate Adaption For Learned Video Compression.
27. Deep Incremental Optical Flow Coding For Learned Video Compression.
28. Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265.
29. Augmented Normalizing Flow for Point Cloud Geometry Coding.
30. CANF-VC: Conditional Augmented Normalizing Flows for Video Compression.
31. Two-Layer Learning-Based P-Frame Coding with Super-Resolution and Content-Adaptive Conditional ANF.
32. Indirect: invertible and discrete noisy image rescaling with enhancement from case-dependent textures.
33. Object Rearrangement Through Planar Pushing: A Theoretical Analysis and Validation.
34. CANF-VC++: Enhancing Conditional Augmented Normalizing Flows for Video Compression with Advanced Techniques.
35. Content-Adaptive Motion Rate Adaption for Learned Video Compression.
36. Hierarchical B-frame Video Coding Using Two-Layer CANF without Motion Coding.
37. Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction.
38. Transformer-based Variable-rate Image Compression with Region-of-interest Control.
39. Transformer-based Image Compression with Variable Image Quality Objectives.
40. Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks.
41. GSVNET: Guided Spatially-Varying Convolution for Fast Semantic Segmentation on Video.
42. A Dual-Critic Reinforcement Learning Framework for Frame-Level Bit Allocation in HEVC/H.265.
43. End-to-End Learned Image Compression With Augmented Normalizing Flows.
44. Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling.
45. Deep Video Compression for Interframe Coding.
46. DIRECT: Discrete Image Rescaling with Enhancement from Case-specific Textures.
47. P-frame Coding Proposal by NCTU: Parametric Video Prediction through Backprop-based Motion Estimation.
48. Semantic Segmentation on Compressed Video using Block Motion Compensation and Guided Inpainting.
49. Deep Video Prediction Through Sparse Motion Regularization.
50. Class-Incremental Learning with Rectified Feature-Graph Preservation.
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