Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval

• URL: http://arxiv.org/abs/2405.14726v1
• Date: Thu, 23 May 2024 15:54:59 GMT
• Title: Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval
• Authors: Young Kyun Jang, Donghyun Kim, Ser-nam Lim,
• Abstract summary: Learning to hash is a practical solution for efficient retrieval, offering fast search speed and low storage cost. We explore the potential of enhancing the performance of learning to hash with the proliferation of powerful pre-trained models. We introduce a novel method named Distillation for Cross-Modal Quantization (DCMQ) to improve hash representation learning.
• Score: 44.61221990245263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: ``Learning to hash'' is a practical solution for efficient retrieval, offering fast search speed and low storage cost. It is widely applied in various applications, such as image-text cross-modal search. In this paper, we explore the potential of enhancing the performance of learning to hash with the proliferation of powerful large pre-trained models, such as Vision-Language Pre-training (VLP) models. We introduce a novel method named Distillation for Cross-Modal Quantization (DCMQ), which leverages the rich semantic knowledge of VLP models to improve hash representation learning. Specifically, we use the VLP as a `teacher' to distill knowledge into a `student' hashing model equipped with codebooks. This process involves the replacement of supervised labels, which are composed of multi-hot vectors and lack semantics, with the rich semantics of VLP. In the end, we apply a transformation termed Normalization with Paired Consistency (NPC) to achieve a discriminative target for distillation. Further, we introduce a new quantization method, Product Quantization with Gumbel (PQG) that promotes balanced codebook learning, thereby improving the retrieval performance. Extensive benchmark testing demonstrates that DCMQ consistently outperforms existing supervised cross-modal hashing approaches, showcasing its significant potential.

Related papers

• Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior [14.232144691524528]
  Recent Vision-Language Pretrained models have become the backbone for many downstream tasks. MLE training can lead the context vector to over-fit dominant image features in the training data. This paper presents a Bayesian-based framework of prompt learning, which could alleviate the overfitting issues on few-shot learning application.
  arXiv  Detail & Related papers  (2024-01-09T10:15:59Z)
• CUCL: Codebook for Unsupervised Continual Learning [129.91731617718781]
  The focus of this study is on Unsupervised Continual Learning (UCL), as it presents an alternative to Supervised Continual Learning. We propose a method named Codebook for Unsupervised Continual Learning (CUCL) which promotes the model to learn discriminative features to complete the class boundary. Our method significantly boosts the performances of supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2023-11-25T03:08:50Z)
• REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models [11.78036105494679]
  This paper introduces a novel code-to-code search technique that enhances the performance of Large Language Models (LLMs) We present the first-ever code search method that encodes dynamic information during training without the need to execute either the corpus under search or the search query at inference time.
  arXiv  Detail & Related papers  (2023-05-05T20:46:56Z)
• Vector Quantized Wasserstein Auto-Encoder [57.29764749855623]
  We study learning deep discrete representations from the generative viewpoint. We endow discrete distributions over sequences of codewords and learn a deterministic decoder that transports the distribution over the sequences of codewords to the data distribution. We develop further theories to connect it with the clustering viewpoint of WS distance, allowing us to have a better and more controllable clustering solution.
  arXiv  Detail & Related papers  (2023-02-12T13:51:36Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)
• An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation [2.0137632982900207]
  We present the learning-to-rank distillation-based COP framework, where a high-performance ranking policy can be distilled into a non-iterative, simple model. Specifically, we employ the approximated ranking distillation to render a score-based ranking model learnable via gradient descent. We demonstrate that a distilled model achieves comparable performance to its respective, high-performance RL, but also provides several times faster inferences.
  arXiv  Detail & Related papers  (2021-12-24T10:52:47Z)
• Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates [52.164757178369804]
  Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We conduct an empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework. We also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance.
  arXiv  Detail & Related papers  (2021-01-20T13:59:25Z)
• Robust Training of Vector Quantized Bottleneck Models [21.540133031071438]
  We demonstrate methods for reliable and efficient training of discrete representation using Vector-Quantized Variational Auto-Encoder models (VQ-VAEs) For unsupervised representation learning, they became viable alternatives to continuous latent variable models such as the Variational Auto-Encoder (VAE)
  arXiv  Detail & Related papers  (2020-05-18T08:23:41Z)
• Learning to Hash with Graph Neural Networks for Recommender Systems [103.82479899868191]
  Graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects in the user-item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous. We propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.
  arXiv  Detail & Related papers  (2020-03-04T06:59:56Z)

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.