Byra, M.[Michal],
Skibbe, H.[Henrik],
Generating Visual Explanations from Deep Networks Using Implicit
Neural Representations,
WACV25(3310-3319)
IEEE DOI
2505
Deep learning, Visualization, Perturbation methods,
Neural networks, Image decomposition, Iterative methods
BibRef
Rhee, H.[Hochang],
Chung, H.S.[Hae Soo],
Jo, J.H.[Jun Ho],
Lee, E.J.[Eun Ji],
Cho, N.I.[Nam Ik],
SANERV: Scene-Adaptive Neural Representation for Videos,
ICIP24(1274-1280)
IEEE DOI
2411
Representation learning, Adaptation models, Image coding, Tensors,
Redundancy, Neural networks, Network architecture, Scene Adaptive
BibRef
Yan, H.[Hao],
Ke, Z.H.[Zhi-Hui],
Zhou, X.B.[Xiao-Bo],
Qiu, T.[Tie],
Shi, X.[Xidong],
Jiang, D.D.[Da-Dong],
DS-NeRV: Implicit Neural Video Representation with Decomposed Static
and Dynamic Codes,
CVPR24(23019-23029)
IEEE DOI Code:
WWW Link.
2410
Interpolation, Codes, Fuses, Computational modeling,
Termination of employment, Sampling methods,
Decomposing of static and dynamic
BibRef
Kim, J.[Jina],
Lee, J.[Jihoo],
Kang, J.W.[Je-Won],
SNeRV: Spectra-Preserving Neural Representation for Video,
ECCV24(LV: 332-348).
Springer DOI
2412
Code:
WWW Link.
BibRef
Tarchouli, M.[Marwa],
Guionnet, T.[Thomas],
Riviere, M.[Marc],
Hamidouche, W.[Wassim],
Outtas, M.[Meriem],
Deforges, O.[Olivier],
Res-NeRV: Residual Blocks for a Practical Implicit Neural Video
Decoder,
ICIP24(3751-3757)
IEEE DOI
2411
For reconstructing details. NeRV.
Image coding, Quantization (signal), Pipelines, Rate-distortion,
Streaming media, Decoding, Video Compression
BibRef
Zhao, Q.[Qi],
Asif, M.S.[M. Salman],
Ma, Z.[Zhan],
PNeRV: Enhancing Spatial Consistency via Pyramidal Neural
Representation for Videos,
CVPR24(19103-19112)
IEEE DOI
2410
Measurement, Adaptation models, Humanities, Correlation, Tensors,
Spatiotemporal phenomena, INR, video coding, video reconstruction
BibRef
Bai, Y.P.[Yun-Peng],
Dong, C.[Chao],
Wang, C.R.[Cai-Rong],
Yuan, C.[Chun],
PS-NeRV: Patch-Wise Stylized Neural Representations for Videos,
ICIP23(41-45)
IEEE DOI
2312
BibRef
Chen, H.[Hao],
Gwilliam, M.[Matthew],
Lim, S.N.[Ser-Nam],
Shrivastava, A.[Abhinav],
HNeRV: A Hybrid Neural Representation for Videos,
CVPR23(10270-10279)
IEEE DOI
2309
BibRef
Zhao, Q.[Qi],
Asif, M.S.[M. Salman],
Ma, Z.[Zhan],
DNeRV: Modeling Inherent Dynamics via Difference Neural
Representation for Videos,
CVPR23(2031-2040)
IEEE DOI
2309
BibRef
Li, Z.Z.[Zi-Zhang],
Wang, M.M.[Meng-Meng],
Pi, H.J.[Huai-Jin],
Xu, K.[Kechun],
Mei, J.B.[Jian-Biao],
Liu, Y.[Yong],
E-NeRV: Expedite Neural Video Representation with Disentangled
Spatial-Temporal Context,
ECCV22(XXXV:267-284).
Springer DOI
2211
BibRef
Srinivasan, P.P.[Pratul P.],
Deng, B.Y.[Bo-Yang],
Zhang, X.M.[Xiu-Ming],
Tancik, M.[Matthew],
Mildenhall, B.[Ben],
Barron, J.T.[Jonathan T.],
NeRV: Neural Reflectance and Visibility Fields for Relighting and
View Synthesis,
CVPR21(7491-7500)
IEEE DOI
2111
Training, Reflectivity,
Lighting, Predictive models, Rendering (computer graphics)
BibRef
Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Novel View Systhesis Using Neural Radiance Fields, NeRF .