10 citations · 20 across the 16 of their papers we have counts for
5 papers · 1 filter
EmoCaliber: Advancing Reliable Visual Emotion Comprehension via Confidence Verbalization and Calibration
Daiqing Wu, Dongbao Yang, Can Ma +1
Visual Emotion Comprehension (VEC) aims to infer sentiment polarities or emotion categories from affective cues embedded in images. In recent years, Multimodal Large Language Model…
Bridging Visual Affective Gap: Borrowing Textual Knowledge by Learning from Noisy Image-Text Pairs
Daiqing Wu, Dongbao Yang, Yu Zhou +1
Visual emotion recognition (VER) is a longstanding field that has garnered increasing attention with the advancement of deep neural networks. Although recent studies have achieved…
Customizing Visual Emotion Evaluation for MLLMs: An Open-vocabulary, Multifaceted, and Scalable Approach
Daiqing Wu, Dongbao Yang, Sicheng Zhao +2
Recently, Multimodal Large Language Models (MLLMs) have achieved exceptional performance across diverse tasks, continually surpassing previous expectations regarding their capabili…
Gather and Trace: Rethinking Video TextVQA from an Instance-oriented Perspective
Yan Zhang, Gangyan Zeng, Daiqing Wu +5
Video text-based visual question answering (Video TextVQA) aims to answer questions by explicitly reading and reasoning about the text involved in a video. Most works in this field…
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability
Daiqing Wu, Dongbao Yang, Sicheng Zhao +2
The advancements in Multimodal Large Language Models (MLLMs) have enabled various multimodal tasks to be addressed under a zero-shot paradigm. This paradigm sidesteps the cost of m…