18 citations · 20 across the 19 of their papers we have counts for
7 papers · 1 filter
Beyond Atomic Layouts: Compositional Design Understanding with Vision-Language Models
Yiyang Huang, Zhaowen Wang, Simon Jenni +4
Layout understanding, or the interpretation of element organization, is essential for document analysis, user interface (UI) creation, and graphic design. While recent vision-langu…
Post-Training in End-to-End Autonomous Driving
Ruining Yang, Muxing Wang, Yixiao Chen +8
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class…
Distorted or Fabricated? A Survey on Hallucination in Video LLMs
Yiyang Huang, Yitian Zhang, Yizhou Wang +4
Despite significant progress in video-language modeling, hallucinations remain a persistent challenge in Video Large Language Models (Vid-LLMs), referring to outputs that appear pl…
The Indra Representation Hypothesis for Multimodal Alignment
Jianglin Lu, Hailing Wang, Kuo Yang +3
Recent studies have uncovered an interesting phenomenon: unimodal foundation models tend to learn convergent representations, regardless of differences in architecture, training ob…
Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks
Qihua Dong, Kuo Yang, Lin Ju +6
Referring Expression Comprehension (REC) links language to region level visual perception. Standard benchmarks (RefCOCO, RefCOCO+, RefCOCOg) have progressed rapidly with multimodal…
Fine-T2I: An Open, Large-Scale, and Diverse Dataset for High-Quality T2I Fine-Tuning
Xu Ma, Yitian Zhang, Qihua Dong +1
High-quality and open datasets remain a major bottleneck for text-to-image (T2I) fine-tuning. Despite rapid progress in model architectures and training pipelines, most publicly av…
Any Orthogonal Transform Will Do: Range Reduction Explains Rotation-Based Quantization for Super-Resolution
Dorsa Zeinali, Hailing Wang, Yitian Zhang +1
Rotation-based quantization -- multiplying weights and activations by a Hadamard matrix before quantizing -- is now standard, but why it works is explained informally, via incohere…