2 citations · 2 across the 13 of their papers we have counts for
13 papers
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…
MoQAE: Mixed-Precision Quantization for Long-Context LLM Inference via Mixture of Quantization-Aware Experts
Wei Tao, Haocheng Lu, Xiaoyang Qu +4
One of the primary challenges in optimizing large language models (LLMs) for long-context inference lies in the high memory consumption of the Key-Value (KV) cache. Existing approa…
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…
BAGNet: A Boundary-Aware Graph Attention Network for 3D Point Cloud Semantic Segmentation
Wei Tao, Xiaoyang Qu, Kai Lu +3
Since the point cloud data is inherently irregular and unstructured, point cloud semantic segmentation has always been a challenging task. The graph-based method attempts to model…
MADLLM: Multivariate Anomaly Detection via Pre-trained LLMs
Wei Tao, Xiaoyang Qu, Kai Lu +3
When applying pre-trained large language models (LLMs) to address anomaly detection tasks, the multivariate time series (MTS) modality of anomaly detection does not align with the…
Cocktail: Chunk-Adaptive Mixed-Precision Quantization for Long-Context LLM Inference
Wei Tao, Bin Zhang, Xiaoyang Qu +2
Recently, large language models (LLMs) have been able to handle longer and longer contexts. However, a context that is too long may cause intolerant inference latency and GPU memor…
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…
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
Jinggang Chen, Junjie Li, Xiaoyang Qu +3
Detecting out-of-distribution (OOD) examples is crucial to guarantee the reliability and safety of deep neural networks in real-world settings. In this paper, we offer an innovativ…