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Found 7 papers, 1 papers with code
RankCLIP: Ranking-Consistent Language-Image Pretraining
Among the ever-evolving development of vision-language models, contrastive language-image pretraining (CLIP) has set new benchmarks in many downstream tasks such as zero-shot classifications by leveraging self-supervised contrastive learning on large amounts of text-image pairs.
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
While large vision-language models (LVLMs) have demonstrated impressive capabilities in interpreting multi-modal contexts, they invariably suffer from object hallucinations (OH).
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback remains a significant impediment.
Direct Acquisition Optimization for Low-Budget Active Learning
Active Learning (AL) has gained prominence in integrating data-intensive machine learning (ML) models into domains with limited labeled data.
Multi-Modality Guidance Network For Missing Modality Inference
Multimodal models have gained significant success in recent years.
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits
NERO evaluation is consist of a task-agnostic interactive interface and a set of visualizations, called NERO plots, which reveals the equivariance property of the model.
Breaking the Curse of Quality Saturation with User-Centric Ranking
A key puzzle in search, ads, and recommendation is that the ranking model can only utilize a small portion of the vastly available user interaction data.
