Corpus of resources for multimodal machine learning with physiological signals (mmps).
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Updated
Aug 19, 2024
Corpus of resources for multimodal machine learning with physiological signals (mmps).
Demo for Binding Text, Images, Graphs, and Audio for Music Representation Learning
[CVPR2021] SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
End-to-end Training for Multimodal Recommendation Systems
[CVPR 2023] Vote2Cap-DETR and [T-PAMI 2024] Vote2Cap-DETR++; A set-to-set perspective towards 3D Dense Captioning; State-of-the-Art 3D Dense Captioning methods
"When in doubt, use brute force." - Ken Thompson
LAVIS - A One-stop Library for Language-Vision Intelligence
[CVPR 2024] Official code for "Text-Driven Image Editing via Learnable Regions"
This repository is cloned from https://github.com/HLR/LatentAlignmentProcedural. This is a potential baseline explored for the textual_cloze task on the RecipeQA Dataset - https://hucvl.github.io/recipeqa/
Here we will track the latest AI Multimodal Models, including Multimodal Foundation Models, LLM, Agent, Audio, Image, Video, Music and 3D content. 🔥
Paper List of Pre-trained Foundation Recommender Models
VTC: Improving Video-Text Retrieval with User Comments
ACL 2024 (SRW), Official Codebase of our Paper: "MoExtend: Tuning New Experts for Modality and Task Extension"
[ACL2024] Progressively Modality Freezing for Multi-Modal Entity Alignment
This repository is maintained to release dataset and models for multimodal puzzle reasoning.
[CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"
Code for Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities
code and link to the dataset for Kenyan Food detection paper accepted as a paper in MADiMA 2019 Workshop as part of ACM MM 2019 conference.
SIREN Scalable, Isotropic Recursive Column Multimodal Neural Architecture with Device State Recognition Use-Case
CompBench evaluates the comparative reasoning of multimodal large language models (MLLMs) with 40K image pairs and questions across 8 dimensions of relative comparison: visual attribute, existence, state, emotion, temporality, spatiality, quantity, and quality. CompBench covers diverse visual domains, including animals, fashion, sports, and scenes.
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