https://github.com/983632847/awesome-multimodal-object-tracking
공식 코드COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking
Papers with Code 아카이브
cross-modal alignment
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AI-14/micar-vl-moe
공식 코드https://github.com/AIGeeksGroup/3DCoCa
공식 코드https://github.com/AllenInstitute/coupledAE
공식 코드A coupled autoencoder approach for multi-modal analysis of cell types
https://github.com/AwellmanZha/M2ConceptBase
공식 코드M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge Base
https://github.com/CriliasMiller/ASAP
공식 코드ASAP: Advancing Semantic Alignment Promotes Multi-Modal Manipulation Detecting and Grounding
https://github.com/DeepLearnXMU/CMTMR
공식 코드Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
https://github.com/DylanOrange/geal
공식 코드GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency
https://github.com/EHZ9NIWI7/MSF-GZSSAR
공식 코드Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition
https://github.com/GradientSpaces/CrossOver
공식 코드https://github.com/ICEORY/PMF
공식 코드EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation
https://github.com/IVGSZ/Flash-VStream
공식 코드Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams
https://github.com/KaijingOfficial/sram_vtg
공식 코드Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding
https://github.com/RobertLuo1/NeurIPS2023_SOC
공식 코드SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
https://github.com/RomGai/BrainVis
공식 코드BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction
https://github.com/SkyworkAI/Skywork-R1V
공식 코드https://github.com/SliMM-X/CoMP-MM
공식 코드CoMP: Continual Multimodal Pre-training for Vision Foundation Models
https://github.com/WuXinglong-HIT/CLIPER
공식 코드Towards Bridging the Cross-modal Semantic Gap for Multi-modal Recommendation
https://github.com/airvln/airvln
공식 코드https://github.com/alibaba/AliceMind/tree/main/mPLUG
공식 코드mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/alibabaresearch/damo-convai
공식 코드Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment
https://github.com/binbinjiang/cvt-slr
공식 코드https://github.com/bronyayang/law_of_vision_representation_in_mllms
공식 코드https://github.com/bruno686/SEA
공식 코드It is Never Too Late to Mend: Separate Learning for Multimedia Recommendation
https://github.com/chenyilun95/dsgn2
공식 코드DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors
https://github.com/chenzcv7/motor
공식 코드Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining
https://github.com/csir1996/vln-gela
공식 코드Grounded Entity-Landmark Adaptive Pre-training for Vision-and-Language Navigation
https://github.com/cyh-0/CAVP
공식 코드Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation
https://github.com/decisionintelligence/RCRank
공식 코드RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems
https://github.com/delong-liu-bupt/sen
공식 코드Text-guided Image Restoration and Semantic Enhancement for Text-to-Image Person Retrieval
https://github.com/dmg-illc/didec-seq-gen
공식 코드Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze
https://github.com/edsonroteia/cav-mae-sync
공식 코드CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
https://github.com/fanmengwang/mmpolymer
공식 코드MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction
https://github.com/foundation-multimodal-models/cal
공식 코드Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment
https://github.com/fqhank/align-kd
공식 코드Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model
https://github.com/friedrichor/UNITE
공식 코드Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.