most citedLoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

4 citations · 6 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV20241 cited

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach

Jiwei Guan, Tianyu Ding, Longbing Cao +3

Vision-language pretraining (VLP) with transformers has demonstrated exceptional performance across numerous multimodal tasks. However, the adversarial robustness of these models h…

cs.IR20241 cited

VERA: Validation and Evaluation of Retrieval-Augmented Systems

Tianyu Ding, Adi Banerjee, Laurent Mombaerts +3

The increasing use of Retrieval-Augmented Generation (RAG) systems in various applications necessitates stringent protocols to ensure RAG systems accuracy, safety, and alignment wi…

cs.CV2024

AdaContour: Adaptive Contour Descriptor with Hierarchical Representation

Tianyu Ding, Jinxin Zhou, Tianyi Chen +3

Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes. By and large, this is the result of the shape being registered with a single gl…

cs.CV2024

S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing

Guangzhi Wang, Tianyi Chen, Kamran Ghasedi +6

Face attribute editing plays a pivotal role in various applications. However, existing methods encounter challenges in achieving high-quality results while preserving identity, edi…

cs.LG2024

ONNXPruner: ONNX-Based General Model Pruning Adapter

Dongdong Ren, Wenbin Li, Tianyu Ding +5

Recent advancements in model pruning have focused on developing new algorithms and improving upon benchmarks. However, the practical application of these algorithms across various…

cs.CV2024

Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation

Wenxiao Deng, Wenbin Li, Tianyu Ding +5

Dataset distillation has emerged as a promising approach in deep learning, enabling efficient training with small synthetic datasets derived from larger real ones. Particularly, di…