12 citations · 12 across the 5 of their papers we have counts for
7 papers · 1 filter
Feature Competition and Its Representational Origins in Vision Language Models: Evidence from the Stroop Paradigm
Nurbüke Teker, Rui Xiao, Zeynep Akata +1
When you ask a vision-language model (VLM) a question with an image, it answers your question based on the image. But to act means to choose: saying one thing means choosing to res…
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…
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…
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…
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…
Teacher-Student Competition for Unsupervised Domain Adaptation
Ruixin Xiao, Zhilei Liu, Baoyuan Wu
With the supervision from source domain only in class-level, existing unsupervised domain adaptation (UDA) methods mainly learn the domain-invariant representations from a shared f…