mlsanityData to 14 Oct 2026

AC-Lite : A Lightweight Image Captioning Model for Low-Resource Assamese Language

arXiv 2503.01453 ↗First posted 3 Mar 2025cs.CV cs.AI cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Most existing works in image caption synthesis use computation heavy deep neural networks and generates image descriptions in English language. This often restricts this important assistive tool for widespread use across language and accessibility barriers. This work presents AC-Lite, a computationally efficient model for image captioning in low-resource Assamese language. AC-Lite reduces computational requirements by replacing computation-heavy deep network components with lightweight alternatives. The AC-Lite model is designed through extensive ablation experiments with different image feature extractor networks and language decoders. A combination of ShuffleNetv2x1.5 with GRU based language decoder along with bilinear attention is found to provide the best performance with minimum compute. AC-Lite was observed to achieve an 82.3 CIDEr score on the COCO-AC dataset with 2.45 GFLOPs and 22.87M parameters.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 39 references that are in the map. First posted on arXiv 3 Mar 2025.

Builds on

26 of 39 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  7. 2014Going Deeper with Convolutionscited by 4,241
  8. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  9. 2019Searching for MobileNetV3cited by 1,994
  10. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  11. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
  12. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,312
14 more
  1. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
  2. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  3. 2014Show and Tell: A Neural Image Caption Generatorcited by 879
  4. 2016Self-critical Sequence Training for Image Captioningcited by 547
  5. 2019Meshed-Memory Transformer for Image Captioningcited by 343
  6. 2019Attention on Attention for Image Captioningcited by 289
  7. 2018A Comprehensive Survey of Deep Learning for Image Captioningcited by 214
  8. 2020X-Linear Attention Networks for Image Captioningcited by 198
  9. 2019Image Captioning: Transforming Objects into Wordscited by 171
  10. 2022SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentationcited by 82
  11. 2022I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioningcited by 16
  12. 2022Efficient Image Captioning for Edge Devicescited by 14
  13. 2019COMIC: Towards A Compact Image Captioning Model with Attentioncited by 7
  14. 2022ACORT: A Compact Object Relation Transformer for Parameter Efficient Image Captioningcited by 6

Built on by

Not cited yet.