Papers with Code 아카이브

NLP based Person Retrival

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/microsoft/RelationNet2

공식 코드

RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder

https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification

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Semantics-Aligned Representation Learning for Person Re-identification

https://github.com/microsoft/UniVL

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UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation

https://github.com/microsoft/X-Decoder

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Generalized Decoding for Pixel, Image, and Language

https://github.com/microsoft/archai

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LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models

https://github.com/microsoft/computervision-recipes/tree/master/contrib/document_cleanup/light_weight_document_cleanup_ICDAR2021

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Light-weight Document Image Cleanup using Perceptual Loss

https://github.com/microsoft/dcvc

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EVC: Towards Real-Time Neural Image Compression with Mask Decay

https://github.com/microsoft/efficientlongsequencemodeling

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Efficient Long Sequence Modeling via State Space Augmented Transformer

https://github.com/microsoft/encoder-decoder-slm

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Return of the Encoder: Maximizing Parameter Efficiency for SLMs

https://github.com/microsoft/interactive-summarization

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Interactive Editing for Text Summarization

https://github.com/microsoft/lavender

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LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling

https://github.com/microsoft/maro

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Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations

https://github.com/microsoft/noaudiocaptioning

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Training Audio Captioning Models without Audio

https://github.com/microsoft/seed-encoder

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Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder

https://github.com/microsoft/semantic_parsing_with_constrained_lm

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BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing

https://github.com/microsoft/simxns

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MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers

https://github.com/microsoft/speecht5

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SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing

https://github.com/microsoft/unilm

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UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

https://github.com/microsoft/unilm/blob/master/YOCO/README.md

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You Only Cache Once: Decoder-Decoder Architectures for Language Models

https://github.com/microsoft/unilm/tree/master/deltalm

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DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders

https://github.com/microsoft/unilm/tree/master/kosmos-g

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Kosmos-G: Generating Images in Context with Multimodal Large Language Models

https://github.com/microsoft/vidtok

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VidTwin: Video VAE with Decoupled Structure and Dynamics

https://github.com/middlekisser/GMNMT

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A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation

https://github.com/mihir3009/longbox

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LongBoX: Evaluating Transformers on Long-Sequence Clinical Tasks

https://github.com/mikelitu/dafoes

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DaFoEs: Mixing Datasets towards the generalization of vision-state deep-learning Force Estimation in Minimally Invasive Robotic Surgery

https://github.com/mikelu-shanghai/CrackIdentificationEvaluation-via-DeepLearning

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Evaluation and Comparison of Deep Learning Methods for Pavement Crack Identification with Visual Images

https://github.com/miliadis/DeepVideoCS

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DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing

https://github.com/milindmrao/nlp_comm

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Deep Learning for Joint Source-Channel Coding of Text

https://github.com/mindflow-institue/waveformer

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Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers

https://github.com/mindsetvision/mindset-vision

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MindSet: Vision. A toolbox for testing DNNs on key psychological experiments

https://github.com/mindslab-ai/assem-vc

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Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques

https://github.com/mindspore-lab/models

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Efficient Machine Translation with a BiLSTM-Attention Approach

https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/DECO

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DECO: Query-Based End-to-End Object Detection with ConvNets

https://github.com/ming053l/RTCS

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Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat

https://github.com/minghanli/univs

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UniVS: Unified and Universal Video Segmentation with Prompts as Queries

https://github.com/mingsun-tse/collaborative-distillation

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Collaborative Distillation for Ultra-Resolution Universal Style Transfer

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.