LiT Tuned Models for Efficient Species Detection

• URL: http://arxiv.org/abs/2302.10281v1
• Date: Sun, 12 Feb 2023 20:36:55 GMT
• Title: LiT Tuned Models for Efficient Species Detection
• Authors: Andre Nakkab, Benjamin Feuer, Chinmay Hegde
• Abstract summary: Our paper introduces a simple methodology for adapting any fine-grained image classification dataset for distributed vision-language pretraining. We implement this methodology on the challenging iNaturalist-2021 dataset, comprised of approximately 2.7 million images of macro-organisms across 10,000 classes. Our model (trained using a new method called locked-image text tuning) uses a pre-trained, frozen vision representation, proving that language alignment alone can attain strong transfer learning performance.
• Score: 22.3395465641384
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in training vision-language models have demonstrated unprecedented robustness and transfer learning effectiveness; however, standard computer vision datasets are image-only, and therefore not well adapted to such training methods. Our paper introduces a simple methodology for adapting any fine-grained image classification dataset for distributed vision-language pretraining. We implement this methodology on the challenging iNaturalist-2021 dataset, comprised of approximately 2.7 million images of macro-organisms across 10,000 classes, and achieve a new state-of-the art model in terms of zero-shot classification accuracy. Somewhat surprisingly, our model (trained using a new method called locked-image text tuning) uses a pre-trained, frozen vision representation, proving that language alignment alone can attain strong transfer learning performance, even on fractious, long-tailed datasets. Our approach opens the door for utilizing high quality vision-language pretrained models in agriculturally relevant applications involving species detection.

Related papers

• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language Models [36.59260354292177]
  Recent advancements in text-to-image generation have inspired researchers to generate datasets tailored for perception models using generative models. We aim to fine-tune vision-language models to a specific classification model without access to any real images. Despite the high fidelity of generated images, we observed a significant performance degradation when fine-tuning the model using the generated datasets.
  arXiv  Detail & Related papers  (2024-06-08T10:43:49Z)
• Enhancing Large Vision Language Models with Self-Training on Image Comprehension [131.14381425260706]
  We introduce Self-Training on Image (STIC), which emphasizes a self-training approach specifically for image comprehension. First, the model self-constructs a preference for image descriptions using unlabeled images. To further self-improve reasoning on the extracted visual information, we let the model reuse a small portion of existing instruction-tuning data.
  arXiv  Detail & Related papers  (2024-05-30T05:53:49Z)
• Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use [14.2527771630478]
  We propose a new framework that alleviates manual effort by replacing human labeling with natural language interactions. Our framework eliminates the need for crowd-sourced annotations. Our trained models outperform traditional Agile Modeling as well as state-of-the-art zero-shot classification models.
  arXiv  Detail & Related papers  (2024-03-05T03:34:11Z)
• No Data Augmentation? Alternative Regularizations for Effective Training on Small Datasets [0.0]
  We study alternative regularization strategies to push the limits of supervised learning on small image classification datasets. In particular, we employ a agnostic to select (semi) optimal learning rate and weight decay couples via the norm of model parameters. We reach a test accuracy of 66.5%, on par with the best state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-04T16:13:59Z)
• DINOv2: Learning Robust Visual Features without Supervision [75.42921276202522]
  This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature.
  arXiv  Detail & Related papers  (2023-04-14T15:12:19Z)
• The effectiveness of MAE pre-pretraining for billion-scale pretraining [65.98338857597935]
  We introduce an additional pre-pretraining stage that is simple and uses the self-supervised MAE technique to initialize the model. We measure the effectiveness of pre-pretraining on 10 different visual recognition tasks spanning image classification, video recognition, object detection, low-shot classification and zero-shot recognition.
  arXiv  Detail & Related papers  (2023-03-23T17:56:12Z)
• SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models [9.017387427570538]
  Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs. Due to their size, fine-tuning these models on new datasets can be prohibitively expensive, both in terms of the supervision and compute required. We present a new approach called SVL-Adapter that combines the complementary strengths of both vision-language pretraining and self-supervised representation learning.
  arXiv  Detail & Related papers  (2022-10-07T19:35:08Z)
• Expanding Language-Image Pretrained Models for General Video Recognition [136.0948049010682]
  Contrastive language-image pretraining has shown great success in learning visual-textual joint representation from web-scale data. We present a simple yet effective approach that adapts the pretrained language-image models to video recognition directly. Our approach surpasses the current state-of-the-art methods by +7.6% and +14.9% in terms of top-1 accuracy under two popular protocols.
  arXiv  Detail & Related papers  (2022-08-04T17:59:54Z)
• Revisiting Classifier: Transferring Vision-Language Models for Video Recognition [102.93524173258487]
  Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research. In this study, we focus on transferring knowledge for video classification tasks. We utilize the well-pretrained language model to generate good semantic target for efficient transferring learning.
  arXiv  Detail & Related papers  (2022-07-04T10:00:47Z)

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.