1 citations · 1 across the 16 of their papers we have counts for
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DMC-CF: Dynamic Multimodal CounterFactual QA benchmark for Causal Reasoning
Junzhe Zhang, Huixuan Zhang, Guirong Wang +5
With the rapid advancement of multimodal large language models (MLLMs), models have demonstrated increasingly powerful multimodal capabilities. However, whether MLLMs trained throu…
SpecSem-Net: Integrating Spectral and Semantic Features for Robust AI-generated Video Detection
Zixi Wei, Huixuaun Zhang, Xiaojun Wan
The remarkable visual fidelity of recent commercial video generative models, such as Sora and Veo, renders robust AI-generated video detection increasingly essential to prevent syn…
UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localization
Huixuan Zhang, Xiaojun Wan
With the rapid proliferation of image generative models, the authenticity of digital images has become a significant concern. While existing studies have proposed various methods f…
MT2IBench: A Large-Scale Multi-Category, Multi-Instance, Multi-Relation Text-to-Image Benchmark
Huixuan Zhang, Xiaojun Wan
Text-to-image models are known to struggle with generating images that perfectly align with textual prompts. Several previous studies have focused on evaluating image-text alignmen…
KBE-DME: Dynamic Multimodal Evaluation via Knowledge Enhanced Benchmark Evolution
Junzhe Zhang, Huixuan Zhang, Xiaojun Wan
The rapid progress of multimodal large language models (MLLMs) calls for more reliable evaluation protocols. Existing static benchmarks suffer from the potential risk of data conta…
How Much To Guide: Revisiting Adaptive Guidance in Classifier-Free Guidance Text-to-Vision Diffusion Models
Huixuan Zhang, Junzhe Zhang, Xiaojun Wan
With the rapid development of text-to-vision generation diffusion models, classifier-free guidance has emerged as the most prevalent method for conditioning. However, this approach…
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency
Junzhe Zhang, Huixuan Zhang, Xunjian Yin +4
Multimodal large language models (MLLMs) are prone to non-factual or outdated knowledge issues, which can manifest as misreading and misrecognition errors due to the complexity of…
EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning
Junzhe Zhang, Huixuan Zhang, Xunjian Yin +1
News image captioning requires model to generate an informative caption rich in entities, with the news image and the associated news article. Current MLLMs still bear limitations…