MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization

• URL: http://arxiv.org/abs/2406.01460v2
• Date: Tue, 4 Jun 2024 07:36:57 GMT
• Title: MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
• Authors: Yu Zhang, Qi Zhang, Zixuan Gong, Yiwei Shi, Yepeng Liu, Duoqian Miao, Yang Liu, Ke Liu, Kun Yi, Wei Fan, Liang Hu, Changwei Wang,
• Abstract summary: Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, leading to rapid advancements in multimodal studies. CLIP relies on a single contrastive supervision for each image-text pair during representation learning. We propose Multi-Perspective Language-Image Pretraining (MLIP) to address these issues.
• Score: 25.53345417279545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, leading to rapid advancements in multimodal studies. However, CLIP faces a notable challenge in terms of inefficient data utilization. It relies on a single contrastive supervision for each image-text pair during representation learning, disregarding a substantial amount of valuable information that could offer richer supervision. Additionally, the retention of non-informative tokens leads to increased computational demands and time costs, particularly in CLIP's ViT image encoder. To address these issues, we propose Multi-Perspective Language-Image Pretraining (MLIP). In MLIP, we leverage the frequency transform's sensitivity to both high and low-frequency variations, which complements the spatial domain's sensitivity limited to low-frequency variations only. By incorporating frequency transforms and token-level alignment, we expand CILP's single supervision into multi-domain and multi-level supervision, enabling a more thorough exploration of informative image features. Additionally, we introduce a token merging method guided by comprehensive semantics from the frequency and spatial domains. This allows us to merge tokens to multi-granularity tokens with a controllable compression rate to accelerate CLIP. Extensive experiments validate the effectiveness of our design.

Related papers

• FoPru: Focal Pruning for Efficient Large Vision-Language Models [11.36025001578531]
  We propose Focal Pruning (FoPru), a training-free method that prunes visual tokens based on the attention-based token significance derived from the vision encoder. Our method can prune a large number of redundant tokens while maintaining high accuracy, leading to significant improvements in inference efficiency.
  arXiv  Detail & Related papers  (2024-11-21T14:22:38Z)
• ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization [49.992614129625274]
  ForgeryGPT is a novel framework that advances the Image Forgery Detection and localization task. It captures high-order correlations of forged images from diverse linguistic feature spaces. It enables explainable generation and interactive dialogue through a newly customized Large Language Model (LLM) architecture.
  arXiv  Detail & Related papers  (2024-10-14T07:56:51Z)
• Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning [49.275450836604726]
  We present a novel frequency-based Self-Supervised Learning (SSL) approach that significantly enhances its efficacy for pre-training. We employ a two-branch framework empowered by knowledge distillation, enabling the model to take both the filtered and original images as input.
  arXiv  Detail & Related papers  (2024-09-16T15:10:07Z)
• MouSi: Poly-Visual-Expert Vision-Language Models [132.58949014605477]
  This paper proposes the use of ensemble experts technique to synergize the capabilities of individual visual encoders. This technique introduces a fusion network to unify the processing of outputs from different visual experts. In our implementation, this technique significantly reduces the positional occupancy in models like SAM, from a substantial 4096 to a more efficient and manageable 64 or even down to 1.
  arXiv  Detail & Related papers  (2024-01-30T18:09:11Z)
• Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models [121.83413400686139]
  This paper proposes to improve the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism. We introduce a novel method that incorporates multi-task encoders and visual tools into the existing MLLMs training and inference pipeline.
  arXiv  Detail & Related papers  (2024-01-06T02:02:34Z)
• C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing [12.930814370829893]
  We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. Existing prompt learning techniques overlook the importance of incorporating domain and content information into the prompts. We propose a solution that ensures domain-invariant prompt learning while enhancing the expressiveness of visual features.
  arXiv  Detail & Related papers  (2023-11-27T13:35:20Z)
• Dynamic Spectrum Mixer for Visual Recognition [17.180863898764194]
  We propose a content-adaptive yet computationally efficient structure, dubbed Dynamic Spectrum Mixer (DSM) DSM represents token interactions in the frequency domain by employing the Cosine Transform. It can learn long-term spatial dependencies with log-linear complexity.
  arXiv  Detail & Related papers  (2023-09-13T04:51:15Z)
• MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition [41.78245303513613]
  We introduce MA-FSAR, a framework that employs the Fine-Tuning (PEFT) technique to enhance the CLIP visual encoder in terms of action-related temporal and semantic representations. In addition to these token-level designs, we propose a prototype-level text-guided construction module to further enrich the temporal and semantic characteristics of video prototypes.
  arXiv  Detail & Related papers  (2023-08-03T04:17:25Z)
• Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training [88.80694147730883]
  We investigate a variety of Modality-Shared Contrastive Language-Image Pre-training (MS-CLIP) frameworks. In studied conditions, we observe that a mostly unified encoder for vision and language signals outperforms all other variations that separate more parameters. Our approach outperforms vanilla CLIP by 1.6 points in linear probing on a collection of 24 downstream vision tasks.
  arXiv  Detail & Related papers  (2022-07-26T05:19:16Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)

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.