1 citations · 2 across the 16 of their papers we have counts for
18 papers
RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling
Zhenchen Tang, Yang Li, Songlin Yang +6
Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existing video reward models often…
An Evolutionary Agentic Approach for Open-ended Image Quality Perception
Zhenchen Tang, Bo Peng, Zichuan Wang +4
Generative models are rapidly expanding image quality assessment (IQA) beyond traditional fidelity factors to emerging dimensions such as physical plausibility and text-rendering c…
Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination
Zichuan Wang, Songlin Yang, Bo Peng +4
Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image. Prior work largely attributes this to insufficient v…
Endogenous Reprompting: Self-Evolving Cognitive Alignment for Unified Multimodal Models
Zhenchen Tang, Songlin Yang, Zichuan Wang +4
Unified Multimodal Models (UMMs) exhibit strong understanding, yet this capability often fails to effectively guide generation. We identify this as a Cognitive Gap: the model lacks…
Revisiting MLLM Based Image Quality Assessment: Errors and Remedy
Zhenchen Tang, Songlin Yang, Bo Peng +2
The rapid progress of multi-modal large language models (MLLMs) has boosted the task of image quality assessment (IQA). However, a key challenge arises from the inherent mismatch b…
HandEval: Taking the First Step Towards Hand Quality Evaluation in Generated Images
Zichuan Wang, Bo Peng, Songlin Yang +2
Although recent text-to-image (T2I) models have significantly improved the overall visual quality of generated images, they still struggle in the generation of accurate details in…
Towards Geometry-Grounded Dense Semantic Matching with VGGT Priors
Songlin Yang, Tianyi Wei, Yushi Lan +3
Semantic matching aims to establish pixel-level correspondences between instances of the same category and represents a fundamental task in computer vision. Existing approaches suf…
Instant Preference Alignment for Text-to-Image Diffusion Models
Yang Li, Songlin Yang, Xiaoxuan Han +4
Text-to-image (T2I) generation has greatly enhanced creative expression, yet achieving preference-aligned generation in a real-time and training-free manner remains challenging. Pr…
Textured 3D Regenerative Morphing with 3D Diffusion Prior
Songlin Yang, Yushi Lan, Honghua Chen +1
Textured 3D morphing creates smooth and plausible interpolation sequences between two 3D objects, focusing on transitions in both shape and texture. This is important for creative…
Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective
Xiaoxuan Han, Songlin Yang, Wei Wang +2
Advanced text-to-image diffusion models raise safety concerns regarding identity privacy violation, copyright infringement, and Not Safe For Work content generation. Towards this,…
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
Yang Li, Songlin Yang, Wei Wang +3
Highly realistic AI generated face forgeries known as deepfakes have raised serious social concerns. Although DNN-based face forgery detection models have achieved good performance…
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation
Yang Li, Songlin Yang, Wei Wang +1
Advanced diffusion-based Text-to-Image (T2I) models, such as the Stable Diffusion Model, have made significant progress in generating diverse and high-quality images using text pro…
Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?
Xiaoxuan Han, Songlin Yang, Wei Wang +2
Deep neural networks have significantly improved the performance of face forgery detection models in discriminating Artificial Intelligent Generated Content (AIGC). However, their…
Learning Dense Correspondence for NeRF-Based Face Reenactment
Songlin Yang, Wei Wang, Yushi Lan +4
Face reenactment is challenging due to the need to establish dense correspondence between various face representations for motion transfer. Recent studies have utilized Neural Radi…
Context-Aware Talking-Head Video Editing
Songlin Yang, Wei Wang, Jun Ling +3
Talking-head video editing aims to efficiently insert, delete, and substitute the word of a pre-recorded video through a text transcript editor. The key challenge for this task is…
Designing a 3D-Aware StyleNeRF Encoder for Face Editing
Songlin Yang, Wei Wang, Bo Peng +1
GAN inversion has been exploited in many face manipulation tasks, but 2D GANs often fail to generate multi-view 3D consistent images. The encoders designed for 2D GANs are not able…
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models
Songlin Yang, Wei Wang, Chenye Xu +3
Face anti-spoofing aims to discriminate the spoofing face images (e.g., printed photos) from live ones. However, adversarial examples greatly challenge its credibility, where addin…
A Systematical Solution for Face De-identification
Songlin Yang, Wei Wang, Yuehua Cheng +1
With the identity information in face data more closely related to personal credit and property security, people pay increasing attention to the protection of face data privacy. In…