4 citations · 11 across the 18 of their papers we have counts for
18 papers
Mitigating Entangled Steering in Large Vision-Language Models for Hallucination Reduction
Yuanhong Zhang, Zhaoyang Wang, Xin Zhang +2
Large Vision-Language Models (LVLMs) have achieved remarkable success across cross-modal tasks but remain hindered by hallucinations, producing textual outputs inconsistent with vi…
Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition
Xuemei Jia, Jiawei Du, Hui Wei +3
High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory and copyright constraints. Thi…
IMS3: Breaking Distributional Aggregation in Diffusion-Based Dataset Distillation
Chenru Wang, Yunyi Chen, Zijun Yang +2
Dataset Distillation aims to synthesize compact datasets that can approximate the training efficacy of large-scale real datasets, offering an efficient solution to the increasing c…
SCOPE: Saliency-Coverage Oriented Token Pruning for Efficient Multimodel LLMs
Jinhong Deng, Wen Li, Joey Tianyi Zhou +1
Multimodal Large Language Models (MLLMs) typically process a large number of visual tokens, leading to considerable computational overhead, even though many of these tokens are red…
AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences
Jieyu Li, Xin Zhang, Joey Tianyi Zhou
Recent advances in AI-generated content have fueled the rise of highly realistic synthetic videos, posing severe risks to societal trust and digital integrity. Existing benchmarks…
Modelship Attribution: Tracing Multi-Stage Manipulations Across Generative Models
Zhiya Tan, Xin Zhang, Joey Tianyi Zhou
As generative techniques become increasingly accessible, authentic visuals are frequently subjected to iterative alterations by various individuals employing a variety of tools. Cu…
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
Zekai Li, Xinhao Zhong, Samir Khaki +49
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance co…
Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents
Tianmi Ma, Jiawei Du, Wenxin Huang +4
Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains undere…
PVP: Polar Representation Boost for 3D Semantic Occupancy Prediction
Yujing Xue, Jiaxiang Liu, Jiawei Du +1
Recently, polar coordinate-based representations have shown promise for 3D perceptual tasks. Compared to Cartesian methods, polar grids provide a viable alternative, offering bette…
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
Jiawei Du, Xin Zhang, Juncheng Hu +2
The sharp increase in data-related expenses has motivated research into condensing datasets while retaining the most informative features. Dataset distillation has thus recently co…
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
Xin Zhang, Jiawei Du, Ping Liu +1
Dataset distillation has emerged as a technique aiming to condense informative features from large, natural datasets into a compact and synthetic form. While recent advancements ha…
Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification
Yuxin Hong, Xiao Zhang, Xin Zhang +1
In the medical field, managing high-dimensional massive medical imaging data and performing reliable medical analysis from it is a critical challenge, especially in resource-limite…
Data-independent Module-aware Pruning for Hierarchical Vision Transformers
Yang He, Joey Tianyi Zhou
Hierarchical vision transformers (ViTs) have two advantages over conventional ViTs. First, hierarchical ViTs achieve linear computational complexity with respect to image size by l…
Multisize Dataset Condensation
Yang He, Lingao Xiao, Joey Tianyi Zhou +1
While dataset condensation effectively enhances training efficiency, its application in on-device scenarios brings unique challenges. 1) Due to the fluctuating computational resour…
Direct Distillation between Different Domains
Jialiang Tang, Shuo Chen, Gang Niu +4
Knowledge Distillation (KD) aims to learn a compact student network using knowledge from a large pre-trained teacher network, where both networks are trained on data from the same…
Sequential Subset Matching for Dataset Distillation
Jiawei Du, Qin Shi, Joey Tianyi Zhou
Dataset distillation is a newly emerging task that synthesizes a small-size dataset used in training deep neural networks (DNNs) for reducing data storage and model training costs.…
Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning
Xin Zhang, Jiawei Du, Yunsong Li +2
Dataset pruning aims to construct a coreset capable of achieving performance comparable to the original, full dataset. Most existing dataset pruning methods rely on snapshot-based…
You Only Condense Once: Two Rules for Pruning Condensed Datasets
Yang He, Lingao Xiao, Joey Tianyi Zhou
Dataset condensation is a crucial tool for enhancing training efficiency by reducing the size of the training dataset, particularly in on-device scenarios. However, these scenarios…