https://github.com/lwaekfjlk/mmoe
공식 코드MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts
Papers with Code 아카이브
Image Retrieval with Multi-Modal Query
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 3
- 보존된 평가 기록
- 15
The problem of retrieving images from a database based on a multi-modal (image- text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/m2man/LGSGM
공식 코드A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval
https://github.com/magic-ai4med/kep
공식 코드Knowledge-enhanced Visual-Language Pretraining for Computational Pathology
https://github.com/mahmoodlab/titan
공식 코드https://github.com/mariyahendriksen/ecir23-object-centric-vs-scene-centric-cmr
공식 코드Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
https://github.com/mcahny/rovit
공식 코드Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers
https://github.com/mcgill-nlp/diffusion-itm
공식 코드https://github.com/mesnico/TERAN
공식 코드Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders
https://github.com/mesnico/TERN
공식 코드Transformer Reasoning Network for Image-Text Matching and Retrieval
https://github.com/mesnico/aladin
공식 코드ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval
https://github.com/mesnico/idclip
공식 코드Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline
https://github.com/mhamilton723/DenseAV
공식 코드Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language
https://github.com/michi-3000/eyeclip
공식 코드EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
https://github.com/microsoft/Oscar
공식 코드Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
https://github.com/microsoft/llava-rad
공식 코드https://github.com/microsoft/unilm/tree/master/beit
공식 코드Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
https://github.com/minzwon/tag-based-music-retrieval
공식 코드https://github.com/minzwon/text2music-emotion-embedding
공식 코드https://github.com/mitjanikolaus/multimodal-predicate-noun-dependencies
공식 코드Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
https://github.com/mitsuichen14/lrsclip
공식 코드LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer Text
https://github.com/msaadsaeed/FOP
공식 코드Fusion and Orthogonal Projection for Improved Face-Voice Association
https://github.com/msaadsaeed/sbnet
공식 코드https://github.com/msarawut/HCI
공식 코드Effective and Efficient Indexing in Cross-Modal Hashing-Based Datasets
https://github.com/mshukor/tfood
공식 코드Transformer Decoders with MultiModal Regularization for Cross-Modal Food Retrieval
https://github.com/mshukor/vlpcook
공식 코드Vision and Structured-Language Pretraining for Cross-Modal Food Retrieval
https://github.com/multimodal-interpretability/nnn
공식 코드Nearest Neighbor Normalization Improves Multimodal Retrieval
https://github.com/mvrl/birdsat
공식 코드BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
https://github.com/mvrl/geoclap
공식 코드Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping
https://github.com/mvrl/taxabind
공식 코드TaxaBind: A Unified Embedding Space for Ecological Applications
https://github.com/mwray/Multi-Verb-Labels
공식 코드https://github.com/myfoodchoice/malm_mask_augmentation_based_local_matching-_for-_food_recipe_retrieval
공식 코드MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval
https://github.com/naver-ai/eccv-caption
공식 코드https://github.com/naver-ai/pcme
공식 코드https://github.com/naver-ai/pcmepp
공식 코드https://github.com/naver/posescript
공식 코드https://github.com/noah888/dar
공식 코드Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation Perspective
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.