Image Comprehension
22 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Image Comprehension
Libraries
Use these libraries to find Image Comprehension models and implementations
Visual7W
Most implemented papers
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
internlm/internlm-xcomposer
•
•
We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition.
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
We try to narrow the gap by mining the potential of VLMs for better performance and any-to-any workflow from three aspects, i. e., high-resolution visual tokens, high-quality data, and VLM-guided generation.
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
In this paper, we propose SIMA, a framework that enhances visual and language modality alignment through self-improvement, eliminating the needs for external models or data.
ArtGPT-4: Towards Artistic-understanding Large Vision-Language Models with Enhanced Adapter
However, a grand challenge of exploiting LLMs for multimodal learning is the size of pre-trained LLMs which are always with billions of parameters.
JourneyDB: A Benchmark for Generative Image Understanding
shihaozhaozsh/lavi-bridge
•
•
NeurIPS 2023
On our dataset, we have devised four benchmarks to assess the performance of generated image comprehension in relation to both content and style interpretation.
Hierarchical Open-vocabulary Universal Image Segmentation
berkeley-hipie/hipie
•
•
NeurIPS 2023
Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions.
RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension
To this end, we propose to extract features corresponding to regional objects as soft prompts for LLM, which provides a straightforward and scalable approach and eliminates the need for LLM fine-tuning.
CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs
When exploring the development of Artificial General Intelligence (AGI), a critical task for these models involves interpreting and processing information from multiple image inputs.
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
Multi-modal large language models (MLLMs) have demonstrated remarkable success in vision and visual-language tasks within the natural image domain.
MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification
This highlights the challenging nature of our benchmark for existing models and the significant gap between the multimodal reasoning capabilities of current models and humans.
