activity
20192025
most citedWhat Do Deep Saliency Models Learn about Visual Attention?

5 citations · 8 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2025

Detecting Defective Wafers Via Modular Networks

Yifeng Zhang, Bryan Baker, Shi Chen +4

The growing availability of sensors within semiconductor manufacturing processes makes it feasible to detect defective wafers with data-driven models. Without directly measuring th…

cs.CV2024

Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs

Peng Ding, Jingyu Wu, Jun Kuang +6

Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on various visual-language understanding and generation tasks. However, MLLMs occasionally genera…

cs.CV2023★ 5 cited

What Do Deep Saliency Models Learn about Visual Attention?

Shi Chen, Ming Jiang, Qi Zhao

In recent years, deep saliency models have made significant progress in predicting human visual attention. However, the mechanisms behind their success remain largely unexplained d…

cs.CV2023

Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning

Shi Chen, Qi Zhao

Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and deco…

cs.CV2022

Attention in Reasoning: Dataset, Analysis, and Modeling

Shi Chen, Ming Jiang, Jinhui Yang +1

While attention has been an increasingly popular component in deep neural networks to both interpret and boost the performance of models, little work has examined how attention pro…

cs.CV2022

REX: Reasoning-aware and Grounded Explanation

Shi Chen, Qi Zhao

Effectiveness and interpretability are two essential properties for trustworthy AI systems. Most recent studies in visual reasoning are dedicated to improving the accuracy of predi…

cs.CV2020★ 2 cited

AiR: Attention with Reasoning Capability

Shi Chen, Ming Jiang, Jinhui Yang +1

While attention has been an increasingly popular component in deep neural networks to both interpret and boost performance of models, little work has examined how attention progres…

cs.CV2019★ 1 cited

Boosted Attention: Leveraging Human Attention for Image Captioning

Shi Chen, Qi Zhao

Visual attention has shown usefulness in image captioning, with the goal of enabling a caption model to selectively focus on regions of interest. Existing models typically rely on…