285 citations · 410 across the 10 of their papers we have counts for
10 papers
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference
Jiajun Zhou, Jiajun Wu, Yizhao Gao +7
To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched. A prominent challenge is to quantize the DNN models into…
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval
Haoyu Lu, Nanyi Fei, Yuqi Huo +3
Large-scale single-stream pre-training has shown dramatic performance in image-text retrieval. Regrettably, it faces low inference efficiency due to heavy attention layers. Recentl…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
Towards artificial general intelligence via a multimodal foundation model
Nanyi Fei, Zhiwu Lu, Yizhao Gao +9
The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods hav…
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference
Zhen Dong, Yizhao Gao, Qijing Huang +3
Automatic algorithm-hardware co-design for DNN has shown great success in improving the performance of DNNs on FPGAs. However, this process remains challenging due to the intractab…
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
Yuqi Huo, Manli Zhang, Guangzhen Liu +32
Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction bet…
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning
Yizhao Gao, Nanyi Fei, Guangzhen Liu +3
Most recent few-shot learning (FSL) methods are based on meta-learning with episodic training. In each meta-training episode, a discriminative feature embedding and/or classifier a…
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Zhen Dong, Dequan Wang, Qijing Huang +6
Deploying deep learning models on embedded systems has been challenging due to limited computing resources. The majority of existing work focuses on accelerating image classificati…
Algorithm-hardware Co-design for Deformable Convolution
Qijing Huang, Dequan Wang, Yizhao Gao +5
FPGAs provide a flexible and efficient platform to accelerate rapidly-changing algorithms for computer vision. The majority of existing work focuses on accelerating image classific…
Meta-Learning across Meta-Tasks for Few-Shot Learning
Nanyi Fei, Zhiwu Lu, Yizhao Gao +3
Existing meta-learning based few-shot learning (FSL) methods typically adopt an episodic training strategy whereby each episode contains a meta-task. Across episodes, these tasks a…