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Found 14 papers, 12 papers with code
Video-R1: Reinforcing Video Reasoning in MLLMs
However, directly applying RL training with the GRPO algorithm to video reasoning presents two primary challenges: (i) a lack of temporal modeling for video reasoning, and (ii) the scarcity of high-quality video-reasoning data.
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1. 5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities.
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
In conclusion, VidEgoThink reflects a research trend towards employing MLLMs for egocentric vision, akin to human capabilities, enabling active observation and interaction in the complex real-world environments.
SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
We hope that our work can serve as a valuable addition to existing MLLM benchmarks, providing insightful observations and inspiring further research in the area of text-rich visual comprehension with MLLMs.
SEED-Bench: Benchmarking Multimodal Large Language Models
Multimodal large language models (MLLMs) building upon the foundation of powerful large language models (LLMs) have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3).
EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning
The pursuit of artificial general intelligence (AGI) has been accelerated by Multimodal Large Language Models (MLLMs), which exhibit superior reasoning, generalization capabilities, and proficiency in processing multimodal inputs.
SEED-Bench-2: Benchmarking Multimodal Large Language Models
Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3).
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation.
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners
Our CaFo incorporates CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, DALL-E's vision-generative knowledge, and GPT-3's language-generative knowledge.
Proposal Distribution Calibration for Few-Shot Object Detection
Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging.
Collaboration of Pre-trained Models Makes Better Few-shot Learner
In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior knowledge from various pre-training paradigms for better few-shot learning.
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects.
Ranked #16 on
Few-Shot Object Detection
on MS-COCO (10-shot)
Prototype Mixture Models for Few-shot Semantic Segmentation
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose.
Method of diagnosing heart disease based on deep learning ECG signal
Some have tried to combine expertise and signal processing to classify ECG signal by heart disease type.
