Adapting Vision-Language Model with Fine-grained Semantics for Open-Vocabulary Segmentation

• URL: http://arxiv.org/abs/2409.16278v2
• Date: Mon, 09 Dec 2024 12:16:38 GMT
• Title: Adapting Vision-Language Model with Fine-grained Semantics for Open-Vocabulary Segmentation
• Authors: Yong Xien Chng, Xuchong Qiu, Yizeng Han, Kai Ding, Wan Ding, Gao Huang,
• Abstract summary: Open-vocabulary segmentation is primarily bottlenecked by mask classification, not mask generation. We propose a novel Fine-grained Semantic Adaptation (FISA) method to address this limitation. FISA enhances the extracted visual features with fine-grained semantic awareness by explicitly integrating this crucial semantic information early in the visual encoding process.
• Score: 42.020470627552136
• License:
• Abstract: Despite extensive research, open-vocabulary segmentation methods still struggle to generalize across diverse domains. To reduce the computational cost of adapting Vision-Language Models (VLMs) while preserving their pre-trained knowledge, most methods freeze the VLMs for mask classification and train only the mask generator. However, our comprehensive analysis reveals a surprising insight: open-vocabulary segmentation is primarily bottlenecked by mask classification, not mask generation. This discovery prompts us to rethink the existing paradigm and explore an alternative approach. Instead of freezing the VLM, we propose to freeze the pre-trained mask generator and focus on optimizing the mask classifier. Building on the observation that VLMs pre-trained on global-pooled image-text features often fail to capture fine-grained semantics necessary for effective mask classification, we propose a novel Fine-grained Semantic Adaptation (FISA) method to address this limitation. FISA enhances the extracted visual features with fine-grained semantic awareness by explicitly integrating this crucial semantic information early in the visual encoding process. As our method strategically optimizes only a small portion of the VLM's parameters, it enjoys the efficiency of adapting to new data distributions while largely preserving the valuable VLM pre-trained knowledge. Extensive ablation studies confirm the superiority of our approach. Notably, FISA achieves new state-of-the-art results across multiple representative benchmarks, improving performance by up to +1.0 PQ and +3.0 mIoU and reduces training costs by nearly 5x compared to previous best methods. Our code and data will be made public.

Related papers

• FrozenSeg: Harmonizing Frozen Foundation Models for Open-Vocabulary Segmentation [47.0028071183214]
  FrozenSeg is designed to integrate spatial knowledge from a localization foundation model (e.g., SAM) and semantic knowledge extracted from a ViL model (e.g., CLIP) FrozenSeg advances state-of-the-art results across various segmentation benchmarks, trained exclusively on COCO panoptic data, and tested in a zero-shot manner.
  arXiv  Detail & Related papers  (2024-09-05T13:36:50Z)
• OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation [54.98688607911399]
  We propose the task of open-vocabulary domain adaptation to infuse domain-specific knowledge into Vision-Language Models (VLMs) Existing VLM adaptation methods improve performance on base (training) queries, but fail to preserve the open-set capabilities of VLMs on novel queries. Our approach is the only parameter-efficient method that consistently surpasses the original VLM on novel classes.
  arXiv  Detail & Related papers  (2024-05-30T15:16:06Z)
• Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization [42.82742477950748]
  Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning. We introduce the Multi-level Optimized Mask Autoencoder (MLO-MAE), a novel framework that learns an optimal masking strategy during pretraining. Our experimental findings highlight MLO-MAE's significant advancements in visual representation learning.
  arXiv  Detail & Related papers  (2024-02-28T07:37:26Z)
• Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations [86.47908754383198]
  Open-Vocabulary (OV) methods leverage large-scale image-caption pairs and vision-language models to learn novel categories. Our method generates pseudo-mask annotations by leveraging the localization ability of a pre-trained vision-language model for objects present in image-caption pairs. Our method trained with just pseudo-masks significantly improves the mAP scores on the MS-COCO dataset and OpenImages dataset.
  arXiv  Detail & Related papers  (2023-03-29T17:58:39Z)
• Global Knowledge Calibration for Fast Open-Vocabulary Segmentation [124.74256749281625]
  We introduce a text diversification strategy that generates a set of synonyms for each training category. We also employ a text-guided knowledge distillation method to preserve the generalizable knowledge of CLIP. Our proposed model achieves robust generalization performance across various datasets.
  arXiv  Detail & Related papers  (2023-03-16T09:51:41Z)
• Exploiting Shape Cues for Weakly Supervised Semantic Segmentation [15.791415215216029]
  Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise class predictions with only image-level labels for training. We propose to exploit shape information to supplement the texture-biased property of convolutional neural networks (CNNs) We further refine the predictions in an online fashion with a novel refinement method that takes into account both the class and the color affinities.
  arXiv  Detail & Related papers  (2022-08-08T17:25:31Z)
• MST: Masked Self-Supervised Transformer for Visual Representation [52.099722121603506]
  Transformer has been widely used for self-supervised pre-training in Natural Language Processing (NLP) We present a novel Masked Self-supervised Transformer approach named MST, which can explicitly capture the local context of an image. MST achieves Top-1 accuracy of 76.9% with DeiT-S only using 300-epoch pre-training by linear evaluation.
  arXiv  Detail & Related papers  (2021-06-10T11:05:18Z)
• Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model [57.77981008219654]
  Masked Language Model (MLM) framework has been widely adopted for self-supervised language pre-training. We propose a fully-explored masking strategy, where a text sequence is divided into a certain number of non-overlapping segments.
  arXiv  Detail & Related papers  (2020-10-12T21:28:14Z)

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.