https://github.com/wangjk666/M2TR-Multi-modal-Multi-scale-Transformers-for-Deepfake-Detection
공식 코드M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection
Papers with Code 아카이브
Image Compression
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 11
- 보존된 평가 기록
- 22
Image Compression is an application of data compression for digital images to lower their storage and/or transmission requirements.
Source: Variable Rate Deep Image Compression With a Conditional Autoencoder
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/whuyyx/magc
공식 코드Map-Assisted Remote-Sensing Image Compression at Extremely Low Bitrates
https://github.com/wookyounghan/losslessinr
공식 코드Towards Lossless Implicit Neural Representation via Bit Plane Decomposition
https://github.com/worldlife123/cbench_basic
공식 코드ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
https://github.com/ws-syx/transvfc
공식 코드TransVFC: A Transformable Video Feature Compression Framework for Machines
https://github.com/wut-ai/hypersound
공식 코드Hypernetworks build Implicit Neural Representations of Sounds
https://github.com/wyf0912/contextgs
공식 코드ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model
https://github.com/wyf0912/r2lcm
공식 코드https://github.com/wyq2021/s2lic
공식 코드https://github.com/xiaosu-zhu/McQuic
공식 코드Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression
https://github.com/xinjie-q/camsic
공식 코드CAMSIC: Content-aware Masked Image Modeling Transformer for Stereo Image Compression
https://github.com/xinjie-q/ldmic
공식 코드https://github.com/xinyue-li3/hybrid-lifting-structure
공식 코드https://github.com/xpixelgroup/hat
공식 코드https://github.com/xylimeng/CARP
공식 코드CARP: Compression through Adaptive Recursive Partitioning for Multi-dimensional Images
https://github.com/xyq7/invcompress
공식 코드https://github.com/yang-jin-hai/sain
공식 코드Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling
https://github.com/ychfan/nsr
공식 코드https://github.com/ychfan/scn
공식 코드https://github.com/yfzhang114/slime
공식 코드Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models
https://github.com/yhbcode000/Eloss-Interpretability
공식 코드Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving
https://github.com/yingyichen-cyy/Nested-Co-teaching
공식 코드https://github.com/yinkangsheng/ug-icm
공식 코드Unified Coding for Both Human Perception and Generalized Machine Analytics with CLIP Supervision
https://github.com/yoshitomo-matsubara/sc2-benchmark
공식 코드https://github.com/yoshitomo-matsubara/supervised-compression
공식 코드Supervised Compression for Resource-Constrained Edge Computing Systems
https://github.com/yulunzhang/RDN
공식 코드https://github.com/yunfeilu0112/fcnr
공식 코드FCNR: Fast Compressive Neural Representation of Visualization Images
https://github.com/zahragolpa/PatchSVD
공식 코드PatchSVD: A Non-uniform SVD-based Image Compression Algorithm
https://github.com/zeldam1/all-in-one
공식 코드https://github.com/zhangmozhe/microshift_compression
공식 코드Microshift: An Efficient Image Compression Algorithm for Hardware
https://github.com/zhentao-liu/klt-jnd
공식 코드Towards Top-Down Just Noticeable Difference Estimation of Natural Images
https://github.com/zmtomorrow/parallelnelloc
공식 코드https://github.com/zzs1994/CVQN
공식 코드Channel-Level Variable Quantization Network for Deep Image Compression
https://gitlab.com/Queuecumber/quantization-guided-ac
공식 코드https://gitlab.com/viper-purdue/balanced-rd
공식 코드Balanced Rate-Distortion Optimization in Learned Image Compression
https://gitlab.com/viper-purdue/continual-compression
공식 코드Towards Backward-Compatible Continual Learning of Image Compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.