2 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
SubRot: Signed Gradient Subspace Calibration for VLM Rotation Quantization
Zhenhao Shang, Haizhao Jing, Haokui Zhang +4
Post-training quantization reduces the deployment cost of vision-language models (VLMs), but preserving multimodal capabilities at low bit widths remains challenging. Existing meth…
P4Q: Co-designing Token Pruning and Quantization for Vision-Language Model Acceleration
Haizhao Jing, Zhenhao Shang, Haokui Zhang +2
Vision language models have achieved strong performance across a wide range of multimodal applications, yet their substantial computational and memory costs hinder efficient deploy…
Rethinking Practical and Efficient Quantization Calibration for Vision-Language Models
Zhenhao Shang, Haizhao Jing, Guoting Wei +4
Post-training quantization (PTQ) is a primary approach for deploying large language models without fine-tuning, and the quantized performance is often strongly affected by the cali…
Open-Text Aerial Detection: A Unified Framework For Aerial Visual Grounding And Detection
Guoting Wei, Xia Yuan, Yang Zhou +6
Open-Vocabulary Aerial Detection (OVAD) and Remote Sensing Visual Grounding (RSVG) have emerged as two key paradigms for aerial scene understanding. However, each paradigm suffers…
3D-RCNet: Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image Classification
Haizhao Jing, Liuwei Wan, Xizhe Xue +2
Recently, the Vision Transformer (ViT) model has replaced the classical Convolutional Neural Network (ConvNet) in various computer vision tasks due to its superior performance. Eve…
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image Classification
Xizhe Xue, Haokui Zhang, Haizhao Jing +3
Data-hungry HSI classification methods require high-quality labeled HSIs, which are often costly to obtain. This characteristic limits the performance potential of data-driven meth…