53 citations · 95 across the 12 of their papers we have counts for
13 papers
Reading is not Reasoning: Bridging the Agentic Policy Gap in Vision-Text Compression
Cheng Fan, Junyi Zhou, Tingzhang Luo +5
Multi-step language-model agents repeatedly process growing interaction histories, leading to substantial context costs. Vision--text compression reduces these costs by rendering h…
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…
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…
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…
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…
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…
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…
Dynamic Resolution Network
Mingjian Zhu, Kai Han, Enhua Wu +4
Deep convolutional neural networks (CNNs) are often of sophisticated design with numerous learnable parameters for the accuracy reason. To alleviate the expensive costs of deployin…
Vision Transformer Pruning
Mingjian Zhu, Yehui Tang, Kai Han
Vision transformer has achieved competitive performance on a variety of computer vision applications. However, their storage, run-time memory, and computational demands are hinderi…
Video Captioning in Compressed Video
Mingjian Zhu, Chenrui Duan, Changbin Yu
Existing approaches in video captioning concentrate on exploring global frame features in the uncompressed videos, while the free of charge and critical saliency information alread…
Dynamic Feature Pyramid Networks for Object Detection
Mingjian Zhu, Kai Han, Changbin Yu +1
Feature pyramid network (FPN) is a critical component in modern object detection frameworks. The performance gain in most of the existing FPN variants is mainly attributed to the i…
Crowd Video Captioning
Liqi Yan, Mingjian Zhu, Changbin Yu
Describing a video automatically with natural language is a challenging task in the area of computer vision. In most cases, the on-site situation of great events is reported in new…
Attribute-Aware Attention Model for Fine-grained Representation Learning
Kai Han, Jianyuan Guo, Chao Zhang +1
How to learn a discriminative fine-grained representation is a key point in many computer vision applications, such as person re-identification, fine-grained classification, fine-g…