Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead

• URL: http://arxiv.org/abs/2105.09121v1
• Date: Wed, 19 May 2021 13:30:34 GMT
• Title: Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead
• Authors: Arian Bakhtiarnia, Qi Zhang and Alexandros Iosifidis
• Abstract summary: We introduce a novel architecture for early exiting based on the vision transformer architecture. We show that our method works for both classification and regression problems. We also introduce a novel method for integrating audio and visual modalities within early exits in audiovisual data analysis.
• Score: 88.17413955380262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying deep learning models in time-critical applications with limited computational resources, for instance in edge computing systems and IoT networks, is a challenging task that often relies on dynamic inference methods such as early exiting. In this paper, we introduce a novel architecture for early exiting based on the vision transformer architecture, as well as a fine-tuning strategy that significantly increase the accuracy of early exit branches compared to conventional approaches while introducing less overhead. Through extensive experiments on image and audio classification as well as audiovisual crowd counting, we show that our method works for both classification and regression problems, and in both single- and multi-modal settings. Additionally, we introduce a novel method for integrating audio and visual modalities within early exits in audiovisual data analysis, that can lead to a more fine-grained dynamic inference.

Related papers

• Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving [52.808273563372126]
  This paper proposes a novel hierarchical BEV perception paradigm, aiming to provide a library of fundamental perception modules and user-friendly graphical interface. We conduct the Pretrain-Finetune strategy to effectively utilize large scale public datasets and streamline development processes. We also present a Multi-Module Learning (MML) approach, enhancing performance through synergistic and iterative training of multiple models.
  arXiv  Detail & Related papers  (2024-07-17T11:17:20Z)
• Tailored Design of Audio-Visual Speech Recognition Models using Branchformers [0.0]
  We propose a novel framework for the design of parameter-efficient Audio-Visual Speech Recognition systems. To be more precise, the proposed framework consists of two steps: first, estimating audio- and video-only systems, and then designing a tailored audio-visual unified encoder. Results reflect how our tailored AVSR system is able to reach state-of-the-art recognition rates.
  arXiv  Detail & Related papers  (2024-07-09T07:15:56Z)
• Improved Baselines for Data-efficient Perceptual Augmentation of LLMs [66.05826802808177]
  In computer vision, large language models (LLMs) can be used to prime vision-language tasks such as image captioning and visual question answering. We present an experimental evaluation of different interfacing mechanisms, across multiple tasks. We identify a new interfacing mechanism that yields (near) optimal results across different tasks, while obtaining a 4x reduction in training time.
  arXiv  Detail & Related papers  (2024-03-20T10:57:17Z)
• Training dynamic models using early exits for automatic speech recognition on resource-constrained devices [15.879328412777008]
  Early-exit architectures enable the development of dynamic models capable of adapting their size and architecture to varying levels of computational resources and ASR performance demands. We show that early-exit models trained from scratch not only preserve performance when using fewer encoder layers but also exhibit enhanced task accuracy compared to single-exit or pre-trained models. Results provide insights into the training dynamics of early-exit architectures for ASR models.
  arXiv  Detail & Related papers  (2023-09-18T07:45:16Z)
• Improving Audio-Visual Segmentation with Bidirectional Generation [40.78395709407226]
  We introduce a bidirectional generation framework for audio-visual segmentation. This framework establishes robust correlations between an object's visual characteristics and its associated sound. We also introduce an implicit volumetric motion estimation module to handle temporal dynamics.
  arXiv  Detail & Related papers  (2023-08-16T11:20:23Z)
• Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
  We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes. Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
  arXiv  Detail & Related papers  (2023-08-14T08:19:24Z)
• mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections [104.14624185375897]
  mPLUG is a new vision-language foundation model for both cross-modal understanding and generation. It achieves state-of-the-art results on a wide range of vision-language downstream tasks, such as image captioning, image-text retrieval, visual grounding and visual question answering.
  arXiv  Detail & Related papers  (2022-05-24T11:52:06Z)
• Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy [26.975596225131824]
  We propose a joint training framework, using the acoustic features and raw images directly as inputs for the AVSC task. Specifically, we retrieve the bottom layers of pre-trained image models as visual encoder, and jointly optimize the scene classifier and 1D-CNN based acoustic encoder during training.
  arXiv  Detail & Related papers  (2022-04-25T03:37:02Z)
• Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation [47.984368369734995]
  We introduce a novel recurrent encoding-decoding neural network architecture for event-based optical flow estimation. The network is end-to-end trained with self-supervised learning on the Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms all the existing state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2021-09-10T13:37:37Z)
• Attention Bottlenecks for Multimodal Fusion [90.75885715478054]
  Machine perception models are typically modality-specific and optimised for unimodal benchmarks. We introduce a novel transformer based architecture that uses fusion' for modality fusion at multiple layers. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks.
  arXiv  Detail & Related papers  (2021-06-30T22:44:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.