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20242026
most citedBridging Visual Affective Gap: Borrowing Textual Knowledge by Learning from Noisy Image-Text Pairs

6 citations · 7 across the 13 of their papers we have counts for

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cs.CV2026

MVEI & EmObserver: Empowering MLLM-Oriented Visual Emotional Intelligence via Emotion Statement Judgement

Daiqing Wu, Dongbao Yang, Jiashu Yao +4

Affective Image Content Analysis (AICA) aims to recognize and understand emotions elicited by visual content, representing an indispensable step toward Artificial General Intellige…

cs.CV2026

Benchmarking Living-Screen-Native GUI Agents on Short-Video Platforms

Jiashu Yao, Heyan Huang, Daiqing Wu +5

GUI agents today assume a static screen, where the world is frozen between two actions. However, real interfaces such as short-video applications violate this assumption, as their…

cs.CV2026

Beyond Detection: A Structure-Aware Framework for Scene Text Tracking

Chenmin Yu, Liu Yu, Daiqing Wu +3

Modern visual object trackers show impressive results on general targets, yet their performance drops substantially when dealing with scene text. Although currently underexplored,…

cs.CV2025

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…

cs.CV20256 cited

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…

cs.CV2025

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…