5 citations · 13 across the 12 of their papers we have counts for
12 papers
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training
Renqiu Xia, Mingsheng Li, Hancheng Ye +12
Despite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry Problem Solving (GPS), which demands understanding diagrams,…
Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy
Hancheng Ye, Jiakang Yuan, Renqiu Xia +5
Diffusion models have recently achieved great success in the synthesis of high-quality images and videos. However, the existing denoising techniques in diffusion models are commonl…
DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models
Renqiu Xia, Song Mao, Xiangchao Yan +19
Scientific documents record research findings and valuable human knowledge, comprising a vast corpus of high-quality data. Leveraging multi-modality data extracted from these docum…
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression
Hancheng Ye, Chong Yu, Peng Ye +5
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, fo…
Enhanced Sparsification via Stimulative Training
Shengji Tang, Weihao Lin, Hancheng Ye +4
Sparsification-based pruning has been an important category in model compression. Existing methods commonly set sparsity-inducing penalty terms to suppress the importance of droppe…
ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
Renqiu Xia, Bo Zhang, Hancheng Ye +9
Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged continuously. However, their capacity to query information depicted in visual charts and engage in r…
Efficient Architecture Search via Bi-level Data Pruning
Chongjun Tu, Peng Ye, Weihao Lin +5
Improving the efficiency of Neural Architecture Search (NAS) is a challenging but significant task that has received much attention. Previous works mainly adopted the Differentiabl…
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions
Jingdong Zhang, Jiayuan Fan, Peng Ye +5
Multi-task dense prediction aims at handling multiple pixel-wise prediction tasks within a unified network simultaneously for visual scene understanding. However, cross-task featur…
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
Renqiu Xia, Haoyang Peng, Hancheng Ye +7
Charts are common in literature across various scientific fields, conveying rich information easily accessible to readers. Current chart-related tasks focus on either chart percept…
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations
Xiangchao Yan, Runjian Chen, Bo Zhang +11
Annotating 3D LiDAR point clouds for perception tasks is fundamental for many applications e.g., autonomous driving, yet it still remains notoriously labor-intensive. Pretraining-f…
Performance-aware Approximation of Global Channel Pruning for Multitask CNNs
Hancheng Ye, Bo Zhang, Tao Chen +2
Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance. Previous works focus on either…