8th Workshop on Efficient Deep Learning for Computer Vision

Workshop at CVPR 2025

Recent advances in deep learning have provided significant improvements in the ability to understand visual content. Mainstream computer vision research often gives little consideration to computation time, resource requirements, and constraints such as energy consumption, memory footprint and model size, or carbon emission. As computer vision is increasingly deployed in resource-limited systems (such as mobile phones, wearable devices, Internet of Things, autonomous aerial vehicles), addressing all of these metrics becomes essential. In this year’s ECV workshop, the topics of interest include but are not limited to the following:

Topics:

  • Mobile Generative vs Discriminative models
  • Augmented and Virtual Reality for computer vision, such as image and video synthesis, object tracking, object recognition, style transfer
  • Integration of large language models (LLM) and computer vision, such as language-guided synthesis of images or videos; as well as image understanding via Vision-Language Models.
  • Applications of computer vision running on mobile or wearable devices
  • Training on mobile devices
  • User experience of computer vision
  • Efficient Foundation Models
    • Compact and efficient architectures for foundation models, such as Vision-Language Models, Generative Adversarial Networks (GANs), Autoregressive models, Denoising Diffusion Probabilistic Models (DDPMs) and Multi-Modal Language models.
    • Efficient generative architecture search algorithms for generative model tasks (domain adaptation, style transfer,Latency-aware loss design for efficient model training)
    • Tunable efficiency-accuracy tradeoffs
  • Efficient Neural Architecture, Compression, Quantization and Hardware Acceleration
    • Compact and efficient neural network architecture design
    • Efficient neural architecture search algorithms for different vision tasks (detection, segmentation, generative models, etc.)
    • Hardware accelerators to support computer vision
    • Hardware-software codesign for computer vision
  • Data-Efficient Learning
    • Methods, algorithms, systems that improves data efficiency of deep learning
    • Different learning modalities (e.g., supervised, self-supervised, reinforcement) that can improve efficiency for computer vision
    • Zero-shot, few-shots learning, language-supervised learning
    • Open-set recognition, detection, and segmentation
  • Efficient 3D models
    • Efficient models for 3D understanding
    • Efficient neural rendering

Speakers and Topics:

NameOrganizationTopic
Song HanSong Han MITEfficient Multi-modal LLM on the Edge
Xianming LiuXianming Liu Xpeng Scaling Up Autonomous Driving with Large Foundation Models
Mergen NachinMergen Nachin MetaLow power LLM Deployment at scale with PyTorch
Yan Deng Yan Deng
Keli ChengKeli Cheng
Qualcomm Real-Time Photo-Realistic Gaussian Splatting Avatar on the Edge
Xiaolong WangXiaolong Wang UC San DiegoEfficient On Device LLMs for Robotics
Jim FanJim Fan NVIDIA The Role of Foundation Models in Simulation and Control

Workshop Schedule (8AM to noon or 1-5PM waiting for CVPR to confirm)

TimeTopic
08:00AMWelcome by Organizers
08:05AMSong Han
08:30AMXianming Liu
08:55AMMergen Nachin
09:20AMYan Deng, Keli Cheng
09:45AMBreak
10:00AMXiaolong Wang
10:25AMJim Fan
10:50AMPresentations of Competition Winners
10:50-11:00AM Introduction and summary talk from LPCVC organizers
11:00-11:45AMWinners of Each Track (15 minutes per track)
11:45AMDiscussion and QA
12:00PMAdjourn

Organizer:

NameOrganizationEmail
(Primary contact) Shuai ZhangQualcomm Technologies, Inc.shuazhan@qti.qualcomm.com
George K. ThiruvathukalLoyola University Chicagogthiruvathukal@luc.edu
Yung-Hsiang LuPurdue Universityyunglu@purdue.edu
Mooi Choo ChuahLehigh Universitymcc7@lehigh.edu
Pavlo MolchanovNVIDIApmolchanov@NVIDIA.com
Hongxu (Danny) YinNVIDIAdannyy@NVIDIA.com
Ning BiQualcomm Technologies, Inc.nbi@qti.qualcomm.com
Krishna SridharQualcomm Technologies, Inc.srsr@qti.qualcomm.com
Fatih PorikliQualcomm Technologies, Inc.fporikli@qti.qualcomm.com

Technical Program Committee:

NameOrganizationEmail
Bo Lang (PhD student)Lehigh Universitybol221@lehigh.edu
Amirhossein HabibianQualcomm Technologies, Inc.ahabibia@qti.qualcomm.com
Ashwin MurthyQualcomm Technologies, Inc.ashwmurt@qti.qualcomm.com
Xiao HuQualcomm Technologies, Inc.hux@qti.qualcomm.com
Taotao JingQualcomm Technologies, Inc.tjing@qti.qualcomm.com
Xin LiQualcomm Technologies, Inc.lxi@qti.qualcomm.com
Gaowen LiuCisco Systemsgaoliu@cisco.com
Sifei LiuNVIDIAsifeil@NVIDIA.com
Abhinav GoelNVIDIAabgoel@NVIDIA.com
Jaeyoun KimGooglejaeyounkim@google.com
Joe SpisakMetajspisak@meta.com
Bichen WuMetawbc@meta.com
Weiwei LiXpengweiweil@xiaopeng.com
Yamini NimmagaddaIntelyamini.nimmagadda@intel.com