Search Results for author:
Found 8 papers, 4 papers with code
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression
Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint.
Enhanced Sparsification via Stimulative Training
To alleviate this issue, we first study and reveal the relative sparsity effect in emerging stimulative training and then propose a structured pruning framework, named STP, based on an enhanced sparsification paradigm which maintains the magnitude of dropped weights and enhances the expressivity of kept weights by self-distillation.
ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged continuously.
Rethinking of Feature Interaction for Multi-task Learning on Dense Prediction
In this work, we propose to learn a comprehensive intermediate feature globally from both task-generic and task-specific features, we reveal an important fact that this intermediate feature, namely the bridge feature, is a good solution to the above issues.
Efficient Architecture Search via Bi-level Data Pruning
Improving the efficiency of Neural Architecture Search (NAS) is a challenging but significant task that has received much attention.
StructChart: Perception, Structuring, Reasoning for Visual Chart Understanding
Charts are common in literature across different scientific fields, conveying rich information easily accessible to readers.
Ranked #19 on
Chart Question Answering
on ChartQA
(using extra training data)
Performance-aware Approximation of Global Channel Pruning for Multitask CNNs
Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance.
Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation
To enhance the model, adaptability between domains and reduce the computational cost when deploying the ensemble model, we propose a novel framework, namely Instance aware Model Ensemble With Distillation, IMED, which fuses multiple UDA component models adaptively according to different instances and distills these components into a small model.
