The field of multimodal learning and vision-language models is rapidly evolving, with a focus on improving the performance and efficiency of these models. Recent studies have explored the use of large language models, such as Multimodal Large Language Models (MLLMs), to enhance tasks like image captioning, document image machine translation, and phrase grounding. Notably, the development of novel training paradigms, like Synchronously Self-Reviewing (SSR) and MuGCP (Multi-modal Mutual-Guidance Conditional Prompt Learning), has led to significant improvements in these tasks. Additionally, research on efficient vision-language models, such as BlindSight and VisionThink, has resulted in substantial reductions in computational costs while maintaining performance. Overall, the field is moving towards more effective and efficient multimodal models, with potential applications in various areas, including medical imaging, object detection, and human-computer interaction. Noteworthy papers include Unveiling Effective In-Context Configurations for Image Captioning and PoseLLM: Enhancing Language-Guided Human Pose Estimation with MLP Alignment, which introduced innovative methods for analyzing and improving multimodal in-context learning and language-guided human pose estimation, respectively.
Advancements in Multimodal Learning and Vision-Language Models
Sources
Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis
Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency
Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models
Visual Semantic Description Generation with MLLMs for Image-Text Matching
L-CLIPScore: a Lightweight Embedding-based Captioning Metric for Evaluating and Training
BlindSight: Harnessing Sparsity for Efficient VLMs
PoseLLM: Enhancing Language-Guided Human Pose Estimation with MLP Alignment
CoSMo: A Multimodal Transformer for Page Stream Segmentation in Comic Books
FIX-CLIP: Dual-Branch Hierarchical Contrastive Learning via Synthetic Captions for Better Understanding of Long Text
A Training-Free, Task-Agnostic Framework for Enhancing MLLM Performance on High-Resolution Images
Fine-Grained Zero-Shot Object Detection
Text-Visual Semantic Constrained AI-Generated Image Quality Assessment
KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model
MSA at ImageCLEF 2025 Multimodal Reasoning: Multilingual Multimodal Reasoning With Ensemble Vision Language Models
Generate to Ground: Multimodal Text Conditioning Boosts Phrase Grounding in Medical Vision-Language Models
HRSeg: High-Resolution Visual Perception and Enhancement for Reasoning Segmentation
Leveraging Language Prior for Infrared Small Target Detection
Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning