15 citations · 36 across the 18 of their papers we have counts for
18 papers
Jacap: Robust KV Cache Eviction via Jacobian-Based Nonlinear Information Capacity Preservation
Jiaming Yang, Chenwei Tang, Liangli Zhen +2
Key-value (KV) cache eviction is essential for scaling long-context inference in Large Language Models. However, existing policies predominantly rely on empirical heuristics, lacki…
Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
Jiaming Yang, Chenwei Tang, Liangli Zhen +1
Key-Value (KV) caching is essential for large language model inference, yet its memory overhead poses a critical bottleneck for long-context generation. Existing eviction policies…
Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design
Haoxiang Sun, Tao Wang, Chenwei Tang +2
Following the success of Group Relative Policy Optimization (GRPO) in foundation LLMs, an increasing number of works have sought to adapt GRPO to Visual Large Language Models (VLLM…
Adaptive Image Zoom-in with Bounding Box Transformation for UAV Object Detection
Tao Wang, Chenyu Lin, Chenwei Tang +5
Detecting objects from UAV-captured images is challenging due to the small object size. In this work, a simple and efficient adaptive zoom-in framework is explored for object detec…
Rewarding the Journey, Not Just the Destination: A Composite Path and Answer Self-Scoring Reward Mechanism for Test-Time Reinforcement Learning
Jingyu Xing, Chenwei Tang, Xinyu Liu +5
Reinforcement Learning (RL) has emerged as a powerful paradigm for advancing Large Language Models (LLMs), achieving remarkable performance in complex reasoning domains such as mat…
SAG-Agent: Enabling Long-Horizon Reasoning in Strategy Games via Dynamic Knowledge Graphs
Chenwei Tang, Lin Long, Xinyu Liu +6
Most commodity software lacks accessible Application Programming Interfaces (APIs), requiring autonomous agents to interact solely through pixel-based Graphical User Interfaces (GU…
Dual Prompt Learning for Adapting Vision-Language Models to Downstream Image-Text Retrieval
Yifan Wang, Tao Wang, Chenwei Tang +5
Recently, prompt learning has demonstrated remarkable success in adapting pre-trained Vision-Language Models (VLMs) to various downstream tasks such as image classification. Howeve…
SaENeRF: Suppressing Artifacts in Event-based Neural Radiance Fields
Yuanjian Wang, Yufei Deng, Rong Xiao +4
Event cameras are neuromorphic vision sensors that asynchronously capture changes in logarithmic brightness changes, offering significant advantages such as low latency, low power…
Memory-Augmented Dual-Decoder Networks for Multi-Class Unsupervised Anomaly Detection
Jingyu Xing, Chenwei Tang, Tao Wang +5
Recent advances in unsupervised anomaly detection (UAD) have shifted from single-class to multi-class scenarios. In such complex contexts, the increasing pattern diversity has brou…
EBAD-Gaussian: Event-driven Bundle Adjusted Deblur Gaussian Splatting
Yufei Deng, Yuanjian Wang, Rong Xiao +6
While 3D Gaussian Splatting (3D-GS) achieves photorealistic novel view synthesis, its performance degrades with motion blur. In scenarios with rapid motion or low-light conditions,…
Zero-Shot Aerial Object Detection with Visual Description Regularization
Zhengqing Zang, Chenyu Lin, Chenwei Tang +2
Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consumin…
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
Chenwei Tang, Wenqiang Zhou, Dong Wang +8
The advent of Industry 4.0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, opera…
VCL Challenges 2023 at ICCV 2023 Technical Report: Bi-level Adaptation Method for Test-time Adaptive Object Detection
Chenyu Lin, Yusheng He, Zhengqing Zang +3
This report outlines our team's participation in VCL Challenges B Continual Test_time Adaptation, focusing on the technical details of our approach. Our primary focus is Testtime A…
Attribute Localization and Revision Network for Zero-Shot Learning
Junzhe Xu, Suling Duan, Chenwei Tang +2
Zero-shot learning enables the model to recognize unseen categories with the aid of auxiliary semantic information such as attributes. Current works proposed to detect attributes f…
GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model
Caiyang Yu, Xianggen Liu, Yifan Wang +5
Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have ach…
Partial Differential Equations Meet Deep Neural Networks: A Survey
Shudong Huang, Wentao Feng, Chenwei Tang +1
Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling. Mechanism-based computation following P…
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning
Yi Gao, Chenwei Tang, Jiancheng Lv
Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased t…
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
Junyu Luo, Yong Xu, Chenwei Tang +1
The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator…