34 citations · 69 across the 18 of their papers we have counts for
18 papers
C-MOP: Integrating Momentum and Boundary-Aware Clustering for Enhanced Prompt Evolution
Binwei Yan, Yifei Fu, Mingjian Zhu +4
Automatic prompt optimization is a promising direction to boost the performance of Large Language Models (LLMs). However, existing methods often suffer from noisy and conflicting u…
Towards Lossless Ultimate Vision Token Compression for VLMs
Dehua Zheng, Mouxiao Huang, Borui Jiang +2
Visual language models encounter challenges in computational efficiency and latency, primarily due to the substantial redundancy in the token representations of high-resolution ima…
PPE: Positional Preservation Embedding for Token Compression in Multimodal Large Language Models
Mouxiao Huang, Borui Jiang, Dehua Zheng +3
Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks, yet often suffer from inefficiencies due to redundant visual tokens. Existing to…
Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition
Hanting Chen, Yasheng Wang, Kai Han +21
This work presents Pangu Embedded, an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs), featuring flexible fast and slow thinking ca…
Single Domain Generalization for Few-Shot Counting via Universal Representation Matching
Xianing Chen, Si Huo, Borui Jiang +2
Few-shot counting estimates the number of target objects in an image using only a few annotated exemplars. However, domain shift severely hinders existing methods to generalize to…
Transferable text data distillation by trajectory matching
Rong Yao, Hailin Hu, Yifei Fu +5
In the realm of large language model (LLM), as the size of large models increases, it also brings higher training costs. There is a urgent need to minimize the data size in LLM tra…
Saliency-driven Dynamic Token Pruning for Large Language Models
Yao Tao, Yehui Tang, Yun Wang +3
Despite the recent success of large language models (LLMs), LLMs are particularly challenging in long-sequence inference scenarios due to the quadratic computational complexity of…
GenVidBench: A 6-Million Benchmark for AI-Generated Video Detection
Zhenliang Ni, Qiangyu Yan, Mouxiao Huang +5
The rapid advancement of video generation models has made it increasingly challenging to distinguish AI-generated videos from real ones. This issue underscores the urgent need for…
Omni-Dimensional Frequency Learner for General Time Series Analysis
Xianing Chen, Hanting Chen, Hailin Hu
Frequency domain representation of time series feature offers a concise representation for handling real-world time series data with inherent complexity and dynamic nature. However…
GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
Yirui Chen, Xudong Huang, Quan Zhang +9
The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia…
A Survey on Transformer Compression
Yehui Tang, Yunhe Wang, Jianyuan Guo +4
Transformer plays a vital role in the realms of natural language processing (NLP) and computer vision (CV), specially for constructing large language models (LLM) and large vision…
PanGu-: Enhancing Language Model Architectures via Nonlinearity Compensation
Yunhe Wang, Hanting Chen, Yehui Tang +17
The recent trend of large language models (LLMs) is to increase the scale of both model size (\aka the number of parameters) and dataset to achieve better generative ability, which…
GenDet: Towards Good Generalizations for AI-Generated Image Detection
Mingjian Zhu, Hanting Chen, Mouxiao Huang +4
The misuse of AI imagery can have harmful societal effects, prompting the creation of detectors to combat issues like the spread of fake news. Existing methods can effectively dete…
Data-Free Distillation of Language Model by Text-to-Text Transfer
Zheyuan Bai, Xinduo Liu, Hailin Hu +3
Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the model when original training data is unavailable. Previous works for DFKD in NLP mainly focus on disti…
Less is More: Focus Attention for Efficient DETR
Dehua Zheng, Wenhui Dong, Hailin Hu +2
DETR-like models have significantly boosted the performance of detectors and even outperformed classical convolutional models. However, all tokens are treated equally without discr…
GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
Mingjian Zhu, Hanting Chen, Qiangyu Yan +7
The extraordinary ability of generative models to generate photographic images has intensified concerns about the spread of disinformation, thereby leading to the demand for detect…
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Dapeng Hu, Shipeng Yan, Qizhengqiu Lu +6
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a lear…
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy
Haonan Lu, Hailin Hu, Xiaodong Lin
Capturing the composition patterns of relations is a vital task in knowledge graph completion. It also serves as a fundamental step towards multi-hop reasoning over learned knowled…