5 papers
Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs
Sanghwan Kim, Rui Xiao, Stephan Alaniz +2
Multimodal Large Language Models (MLLMs) often struggle with fine-grained perception, such as identifying small objects in high-resolution images or detecting key moments in long v…
FINER: MLLMs Hallucinate under Fine-grained Negative Queries
Rui Xiao, Sanghwan Kim, Yongqin Xian +2
Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coa…
COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
Sanghwan Kim, Rui Xiao, Mariana-Iuliana Georgescu +2
Vision-Language Models (VLMs) trained with contrastive loss have achieved significant advancements in various vision and language tasks. However, the global nature of the contrasti…
Accurate Explanation Model for Image Classifiers using Class Association Embedding
Ruitao Xie, Jingbang Chen, Limai Jiang +3
Image classification is a primary task in data analysis where explainable models are crucially demanded in various applications. Although amounts of methods have been proposed to o…
FLAIR: VLM with Fine-grained Language-informed Image Representations
Rui Xiao, Sanghwan Kim, Mariana-Iuliana Georgescu +2
CLIP has shown impressive results in aligning images and texts at scale. However, its ability to capture detailed visual features remains limited because CLIP matches images and te…