1 citations · 1 across the 11 of their papers we have counts for
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WindowQuant: Mixed-Precision KV Cache Quantization based on Window-Level Similarity for VLMs Inference Optimization
Wei Tao, Xiaoyang Qu, Peiqiang Wang +4
Recently, video language models (VLMs) have been applied in various fields. However, the visual token sequence of the VLM is too long, which may cause intolerant inference latency…
Vista: Scene-Aware Optimization for Streaming Video Question Answering under Post-Hoc Queries
Haocheng Lu, Nan Zhang, Wei Tao +4
Streaming video question answering (Streaming Video QA) poses distinct challenges for multimodal large language models (MLLMs), as video frames arrive sequentially and user queries…
Triage: Hierarchical Visual Budgeting for Efficient Video Reasoning in Vision-Language Models
Anmin Wang, Nan Zhang, Wei Tao +4
Vision-Language Models (VLMs) face significant computational challenges in video processing due to massive data redundancy, which creates prohibitively long token sequences. To add…
RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models
Junjie Li, Nan Zhang, Xiaoyang Qu +4
Object Navigation (ObjectNav) is a fundamental task in embodied artificial intelligence. Although significant progress has been made in semantic map construction and target directi…
VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection
Bin Zhang, Xiaoyang Qu, Guokuan Li +2
As object detectors are increasingly deployed as black-box cloud services or pre-trained models with restricted access to the original training data, the challenge of zero-shot obj…
RUNA: Object-level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representations
Bin Zhang, Jinggang Chen, Xiaoyang Qu +5
Enabling object detectors to recognize out-of-distribution (OOD) objects is vital for building reliable systems. A primary obstacle stems from the fact that models frequently do no…
PRENet: A Plane-Fit Redundancy Encoding Point Cloud Sequence Network for Real-Time 3D Action Recognition
Shenglin He, Xiaoyang Qu, Jiguang Wan +3
Recognizing human actions from point cloud sequence has attracted tremendous attention from both academia and industry due to its wide applications. However, most previous studies…
Value-Driven Mixed-Precision Quantization for Patch-Based Inference on Microcontrollers
Wei Tao, Shenglin He, Kai Lu +5
Deploying neural networks on microcontroller units (MCUs) presents substantial challenges due to their constrained computation and memory resources. Previous researches have explor…
EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
Liang Wang, Nan Zhang, Xiaoyang Qu +6
Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewe…
Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning
Liang Wang, Kai Lu, Nan Zhang +5
This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes. Shoggoth uses online kno…