27 citations · 27 across the 9 of their papers we have counts for
9 papers
AB: Adaptive Asymmetric Adapter for Alleviating Branch Bias in Vision-Language Image Classification with Few-Shot Learning
Yiyun Zhou, Zhonghua Jiang, Wenkang Han +4
Efficient transfer learning methods for large-scale vision-language models (, CLIP) enable strong few-shot transfer, yet existing adaptation methods follow a fixed fine-tunin…
RetentiveKV: State-Space Memory for Uncertainty-Aware Multimodal KV Cache Eviction
Sihao Liu, YuFan Xiong, Zhonghua Jiang +2
Multimodal Large Language Models face severe challenges in computational efficiency and memory consumption due to the substantial expansion of the visual KV cache when processing l…
AccKV: Towards Efficient Audio-Video LLMs Inference via Adaptive-Focusing and Cross-Calibration KV Cache Optimization
Zhonghua Jiang, Kui Chen, Kunxi Li +5
Recent advancements in Audio-Video Large Language Models (AV-LLMs) have enhanced their capabilities in tasks like audio-visual question answering and multimodal dialog systems. Vid…
FlowMM: Cross-Modal Information Flow Guided KV Cache Merging for Efficient Multimodal Context Inference
Kunxi Li, Yufan Xiong, Zhonghua Jiang +4
Traditional KV cache eviction strategies, which discard less critical KV-pairs based on attention scores, often degrade generation quality, causing context loss or hallucinations.…
PureKV: Plug-and-Play KV Cache Optimization with Spatial-Temporal Sparse Attention for Vision-Language Large Models
Zhonghua Jiang, Kunxi Li, Yiyun Zhou +4
Vision-Language Large Models (VLLMs) face significant efficiency challenges when processing high-resolution inputs. The quadratic complexity in attention and autoregressive generat…
MadaKV: Adaptive Modality-Perception KV Cache Eviction for Efficient Multimodal Long-Context Inference
Kunxi Li, Zhonghua Jiang, Zhouzhou Shen +5
This paper introduces MadaKV, a modality-adaptive key-value (KV) cache eviction strategy designed to enhance the efficiency of multimodal large language models (MLLMs) in long-cont…
TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting
Jianchuan Chen, Jingchuan Hu, Gaige Wang +4
Realistic 3D full-body talking avatars hold great potential in AR, with applications ranging from e-commerce live streaming to holographic communication. Despite advances in 3D Gau…
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
Zhonghua Jiang, Jimin Xu, Shengyu Zhang +5
Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL met…
GaussianTalker: Speaker-specific Talking Head Synthesis via 3D Gaussian Splatting
Hongyun Yu, Zhan Qu, Qihang Yu +8
Recent works on audio-driven talking head synthesis using Neural Radiance Fields (NeRF) have achieved impressive results. However, due to inadequate pose and expression control cau…