4 citations · 13 across the 17 of their papers we have counts for
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What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and Biases
Anthony Meng Huat Tiong, Junqi Zhao, Boyang Li +3
Vision-language (VL) models, pretrained on colossal image-text datasets, have attained broad VL competence that is difficult to evaluate. A common belief is that a small number of…
Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions
David Junhao Zhang, Dongxu Li, Hung Le +3
Most existing video diffusion models (VDMs) are limited to mere text conditions. Thereby, they are usually lacking in control over visual appearance and geometry structure of the g…
Diffusion Model Alignment Using Direct Preference Optimization
Bram Wallace, Meihua Dang, Rafael Rafailov +7
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' prefe…
X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning
Artemis Panagopoulou, Le Xue, Ning Yu +7
Recent research has achieved significant advancements in visual reasoning tasks through learning image-to-language projections and leveraging the impressive reasoning abilities of…