2 citations · 2 across the 17 of their papers we have counts for
20 papers
Self-Supervised Representation-Guided Generative Dataset Distillation
Mingzhuo Li, Guang Li, Linfeng Ye +4
Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility. Most existing methods target randomly initialized networks…
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
Di Wu, Huan Liu, Zhixiang Chi +3
The rapid advancements in using neural networks as implicit data representations have attracted significant interest in developing machine learning methods that analyze and process…
X-Palm: Paired Multispectral-to-Smartphone Dataset for Cross-Domain Palmprint Authentication
Jamal Seyedmohammadi, Pai Chet Ng, Angelo Genovese +3
Palmprint modality offers a privacy-preserving biometric solution, yet its deployment is hindered by the domain gap between controlled enrollment and unconstrained authentication.…
Privacy-Preserving Federated Action Recognition via Differentially Private Selective Tuning and Efficient Communication
Idris Zakariyya, Pai Chet Ng, Kaushik Bhargav Sivangi +3
Federated video action recognition enables collaborative model training without sharing raw video data, yet remains vulnerable to two key challenges: \textit{model exposure} and \t…
ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging
Linfeng Ye, Shayan Mohajer Hamidi, Zhixiang Chi +5
Attention-based multiple instance learning (MIL) has emerged as a powerful framework for whole slide image (WSI) diagnosis, leveraging attention to aggregate instance-level feature…
Normalized Conditional Mutual Information Surrogate Loss for Deep Neural Classifiers
Linfeng Ye, Zhixiang Chi, Konstantinos N. Plataniotis +1
In this paper, we propose a novel information theoretic surrogate loss; normalized conditional mutual information (NCMI); as a drop in alternative to the de facto cross-entropy (CE…
Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks
Mingzhuo Li, Guang Li, Linfeng Ye +4
In this paper, we propose difficulty-guided sampling (DGS) to bridge the target gap between the distillation objective and the downstream task, therefore improving the performance…
SparseVILA: Decoupling Visual Sparsity for Efficient VLM Inference
Samir Khaki, Junxian Guo, Jiaming Tang +6
Vision Language Models (VLMs) have rapidly advanced in integrating visual and textual reasoning, powering applications across high-resolution image understanding, long-video analys…
Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation
Zhixiang Chi, Yanan Wu, Li Gu +5
CLIP exhibits strong visual-textual alignment but struggle with open-vocabulary segmentation due to poor localization. Prior methods enhance spatial coherence by modifying intermed…
SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
Samir Khaki, Xiuyu Li, Junxian Guo +7
Fine-tuning LLMs is both computationally and memory-intensive. While parameter-efficient fine-tuning methods, such as QLoRA and DoRA, reduce the number of trainable parameters and…
Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation
Zhixiang Chi, Li Gu, Huan Liu +4
Few-shot Test-Time Domain Adaptation focuses on adapting a model at test time to a specific domain using only a few unlabeled examples, addressing domain shift. Prior methods lever…
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…
Dynamic Vision Mamba
Mengxuan Wu, Zekai Li, Zhiyuan Liang +9
Mamba-based vision models have gained extensive attention as a result of being computationally more efficient than attention-based models. However, spatial redundancy still exists…
Data-to-Model Distillation: Data-Efficient Learning Framework
Ahmad Sajedi, Samir Khaki, Lucy Z. Liu +3
Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a mod…
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
Kai Wang, Zekai Li, Zhi-Qi Cheng +6
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios.…
GSTAM: Efficient Graph Distillation with Structural Attention-Matching
Arash Rasti-Meymandi, Ahmad Sajedi, Zhaopan Xu +1
Graph distillation has emerged as a solution for reducing large graph datasets to smaller, more manageable, and informative ones. Existing methods primarily target node classificat…
Prioritize Alignment in Dataset Distillation
Zekai Li, Ziyao Guo, Wangbo Zhao +8
Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this,…
Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams
Ziqiang Wang, Zhixiang Chi, Yanan Wu +4
Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source. Current methods predomina…
Adapting to Distribution Shift by Visual Domain Prompt Generation
Zhixiang Chi, Li Gu, Tao Zhong +4
In this paper, we aim to adapt a model at test-time using a few unlabeled data to address distribution shifts. To tackle the challenges of extracting domain knowledge from a limite…
Test-Time Personalization with Meta Prompt for Gaze Estimation
Huan Liu, Julia Qi, Zhenhao Li +4
Despite the recent remarkable achievement in gaze estimation, efficient and accurate personalization of gaze estimation without labels is a practical problem but rarely touched on…