1 citations · 2 across the 14 of their papers we have counts for
14 papers
Generative World Renderer at the Speed of Play
Guixu Lin, Zheng-Hui Huang, Siqi Yang +3
Generative world renderer AlayaRenderer receives structured world states exported from physics engines and synthesizes RGB frames. Unlike models that generate frames from text/cont…
H2HMem: A Multimodal Memory Benchmark for Agents in Human-Human Interactions
Shiping Zhu, Yibo Yang, Zhengyang Wang +3
Large language model agents are increasingly deployed in human-human interaction settings, such as meeting assistants and clinical documentation systems, where they must observe co…
ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development
Pengrui Lu, Shiqi Zhang, Yunzhong Hou +8
Recent coding agents can generate complete codebases from simple prompts, yet existing evaluations focus on issue-level bug fixing and lag behind end-to-end development. We introdu…
PAS: A Training-Free Stabilizer for Temporal Encoding in Video LLMs
Bowen Sun, Yujun Cai, Ming-Hsuan Yang +2
Video LLMs suffer from temporal inconsistency: small shifts in frame timing can flip attention and suppress relevant frames. We trace this instability to the common extension of Ro…
RefineShot: Rethinking Cinematography Understanding with Foundational Skill Evaluation
Hang Wu, Yujun Cai, Haonan Ge +3
Cinematography understanding refers to the ability to recognize not only the visual content of a scene but also the cinematic techniques that shape narrative meaning. This capabili…
Blockwise SFT for Diffusion Language Models: Reconciling Bidirectional Attention and Autoregressive Decoding
Bowen Sun, Yujun Cai, Ming-Hsuan Yang +1
Discrete diffusion language models have shown strong potential for text generation, yet standard supervised fine-tuning (SFT) misaligns with their semi-autoregressive inference: tr…
MRFD: Multi-Region Fusion Decoding with Self-Consistency for Mitigating Hallucinations in LVLMs
Haonan Ge, Yiwei Wang, Ming-Hsuan Yang +1
Large Vision-Language Models (LVLMs) have shown strong performance across multimodal tasks. However, they often produce hallucinations -- text that is inconsistent with visual inpu…
DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning
Hang Wu, Hongkai Chen, Yujun Cai +4
Grounding natural language queries in graphical user interfaces (GUIs) poses unique challenges due to the diversity of visual elements, spatial clutter, and the ambiguity of langua…
Structured Attention Matters to Multimodal LLMs in Document Understanding
Chang Liu, Hongkai Chen, Yujun Cai +4
Document understanding remains a significant challenge for multimodal large language models (MLLMs). While previous research has primarily focused on locating evidence pages throug…
Text Speaks Louder than Vision: ASCII Art Reveals Textual Biases in Vision-Language Models
Zhaochen Wang, Bryan Hooi, Yiwei Wang +3
Vision-language models (VLMs) have advanced rapidly in processing multimodal information, but their ability to reconcile conflicting signals across modalities remains underexplored…
MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for Red-Team Jailbreak Attacks
Wenhao You, Bryan Hooi, Yiwei Wang +5
While safety mechanisms have significantly progressed in filtering harmful text inputs, MLLMs remain vulnerable to multimodal jailbreaks that exploit their cross-modal reasoning ca…
Tit-for-Tat: Safeguarding Large Vision-Language Models Against Jailbreak Attacks via Adversarial Defense
Shuyang Hao, Yiwei Wang, Bryan Hooi +5
Deploying large vision-language models (LVLMs) introduces a unique vulnerability: susceptibility to malicious attacks via visual inputs. However, existing defense methods suffer fr…
Lost in Edits? A -Compass for AIGC Provenance
Wenhao You, Bryan Hooi, Yiwei Wang +5
Recent advancements in diffusion models have driven the growth of text-guided image editing tools, enabling precise and iterative modifications of synthesized content. However, as…
Efficiently Disentangling CLIP for Multi-Object Perception
Samyak Rawlekar, Yujun Cai, Yiwei Wang +2
Vision-language models like CLIP excel at recognizing the single, prominent object in a scene. However, they struggle in complex scenes containing multiple objects. We identify a f…