6 citations · 10 across the 18 of their papers we have counts for
13 papers · 1 filter
Knowing Beyond the Known: Reinforced Knowledge Specification for Multi-Label Class-Incremental Learning
Aoting Zhang, Dongbao Yang, Chang Liu +3
Existing class-incremental learning methods struggle in multi-label scenarios (MLCIL) due to the inherent contradiction of learning objectives arising from co-occurring and incompl…
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…
Orthogonal Knowledge Refreshing for Domain-Incremental Object Detection
Aoting Zhang, Dongbao Yang, Chang Liu +3
Domain-incremental object detection (DIOD) requires models to continually adapt to new domains while preserving prior knowledge. Recently, parameter-efficient fine-tuning offers a…
UniTranslator: A Unified Multi-modal Framework for End-to-end In-Image Machine Translation
Jiahao Lyu, Pei Fu, Zhenhang Li +6
In-Image Machine Translation (IIMT) aims to translate scene text in an image and render the translated text back into the original regions while preserving the overall visual appea…
Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters
Gengluo Li, Shangpin Peng, Xingyu Wan +16
Vision Large Language Models (VLLMs) have achieved remarkable success in modern text-rich visual understanding. However, their perceptual robustness in the face of the continuous m…
Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training
Gengluo Li, Pengyuan Lyu, Chengquan Zhang +7
Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Traditional cascaded pipelines depend…
IMTBench: A Multi-Scenario Cross-Modal Collaborative Evaluation Benchmark for In-Image Machine Translation
Jiahao Lyu, Pei Fu, Zhenhang Li +7
End-to-end In-Image Machine Translation (IIMT) aims to convert text embedded within an image into a target language while preserving the original visual context, layout, and render…
MMTIT-Bench: A Multilingual and Multi-Scenario Benchmark with Cognition-Perception-Reasoning Guided Text-Image Machine Translation
Gengluo Li, Chengquan Zhang, Yupu Liang +9
End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world multilingual scene understanding.…
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…
Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition
Yifei Zhang, Chang Liu, Jin Wei +4
Text images are unique in their dual nature, encompassing both visual and linguistic information. The visual component encompasses structural and appearance-based features, while t…
Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal Clues
Yan Zhang, Gangyan Zeng, Huawen Shen +3
Video text-based visual question answering (Video TextVQA) is a practical task that aims to answer questions by jointly reasoning textual and visual information in a given video. I…
Resolving Sentiment Discrepancy for Multimodal Sentiment Detection via Semantics Completion and Decomposition
Daiqing Wu, Dongbao Yang, Huawen Shen +2
With the proliferation of social media posts in recent years, the need to detect sentiments in multimodal (image-text) content has grown rapidly. Since posts are user-generated, th…