9 papers
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…
GmNet: Revisiting Gating Mechanisms From A Frequency View
Yifan Wang, Xu Ma, Yitian Zhang +5
Gating mechanisms have emerged as an effective strategy integrated into model designs beyond recurrent neural networks for addressing long-range dependency problems. In a broad und…
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…
CompSRT: Quantization and Pruning for Image Super Resolution Transformers
Dorsa Zeinali, Hailing Wang, Yitian Zhang +1
Model compression has become an important tool for making image super resolution models more efficient. However, the gap between the best compressed models and the full precision m…
Outlier-Aware Post-Training Quantization for Image Super-Resolution
Hailing Wang, jianglin Lu, Yitian Zhang +1
Quantization techniques, including quantization-aware training (QAT) and post-training quantization (PTQ), have become essential for inference acceleration of image super-resolutio…