1 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
VACoT: Rethinking Visual Data Augmentation with VLMs
Zhengzhuo Xu, Chong Sun, SiNan Du +3
While visual data augmentation remains a cornerstone for training robust vision models, it has received limited attention in visual language models (VLMs), which predominantly rely…
Visual Generation Tuning
Jiahao Guo, Sinan Du, Jingfeng Yao +7
Large Vision Language Models (VLMs) effectively bridge the modality gap through extensive pretraining, acquiring sophisticated visual representations aligned with language. However…
VQRAE: Representation Quantization Autoencoders for Multimodal Understanding, Generation and Reconstruction
Sinan Du, Jiahao Guo, Bo Li +8
Unifying multimodal understanding, generation and reconstruction representation in a single tokenizer remains a key challenge in building unified models. Previous research predomin…
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts
Sinan Du, Guosheng Zhang, Keyao Wang +7
Parameter-efficient transfer learning (PETL) has become a promising paradigm for adapting large-scale vision foundation models to downstream tasks. Typical methods primarily levera…
ChartBench: A Benchmark for Complex Visual Reasoning in Charts
Zhengzhuo Xu, Sinan Du, Yiyan Qi +3
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in image understanding and generation. However, current benchmarks fail to accurately evaluate the chart…
HHF: Hashing-guided Hinge Function for Deep Hashing Retrieval
Chengyin Xu, Zenghao Chai, Zhengzhuo Xu +4
Deep hashing has shown promising performance in large-scale image retrieval. However, latent codes extracted by Deep Neural Networks (DNNs) will inevitably lose semantic informatio…