Vision-Language Models

LLaVA-01: A novel visual language model achieves superior reasoning performance through structured, multi-stage processing and efficient inference-time scaling, surpassing even larger, closed-source m…

JanusFlow harmonizes autoregression and rectified flow for unified multimodal understanding and generation, achieving state-of-the-art results on standard benchmarks.

VideoGLaMM: a new large multimodal model achieves precise pixel-level visual grounding in videos by seamlessly integrating a dual vision encoder, a spatio-temporal decoder, and a large language model.

LLM2CLIP boosts CLIP’s performance by cleverly integrating LLMs, enabling it to understand longer, more complex image captions and achieving state-of-the-art results across various benchmarks.

MM-Detect: a novel framework detects contamination in multimodal LLMs, enhancing benchmark reliability by identifying training set leakage and improving performance evaluations.

TIP-I2V: A million-scale dataset provides 1.7 million real user text & image prompts for image-to-video generation, boosting model development and safety.

Inference-optimal Vision Language Models (VLMs) need only one visual token but larger models!

OS-Atlas: A new open-source toolkit and model dramatically improves GUI agent performance by providing a massive dataset and innovative training methods, enabling superior generalization to unseen int…

BenchX: A unified benchmark framework reveals surprising MedVLP performance, challenging existing conclusions and advancing research.