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20102023
most citedA Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

188 citations · 445 across the 40 of their papers we have counts for

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6 papers · 1 filter

cs.CV2023

No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention and Zoom-in Boundary Detection

Qi Zhang, Sipeng Zheng, Qin Jin

Temporal video grounding (TVG) aims to retrieve the time interval of a language query from an untrimmed video. A significant challenge in TVG is the low "Semantic Noise Ratio (SNR)…

cs.CV2023

Movie101: A New Movie Understanding Benchmark

Zihao Yue, Qi Zhang, Anwen Hu +3

To help the visually impaired enjoy movies, automatic movie narrating systems are expected to narrate accurate, coherent, and role-aware plots when there are no speaking lines of a…

cs.CV2023

Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition

Nan Li, Mehdi Bennis, Alexandros Iosifidis +1

This paper studies the computational offloading of video action recognition in edge computing. To achieve effective semantic information extraction and compression, following seman…

cs.CV2022★ 1 cited

Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications

Andrea Cavagna, Nan Li, Alexandros Iosifidis +1

This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients o…

cs.CV2022★ 2 cited

Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing

Zhongtian Dong, Nan Li, Alexandros Iosifidis +1

For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we…

cs.CV2022

Attention-based Feature Compression for CNN Inference Offloading in Edge Computing

Nan Li, Alexandros Iosifidis, Qi Zhang

This paper studies the computational offloading of CNN inference in device-edge co-inference systems. Inspired by the emerging paradigm semantic communication, we propose a novel a…