Overcoming Domain Limitations in Open-vocabulary Segmentation

• URL: http://arxiv.org/abs/2410.11536v1
• Date: Tue, 15 Oct 2024 12:11:41 GMT
• Title: Overcoming Domain Limitations in Open-vocabulary Segmentation
• Authors: Dongjun Hwang, Seong Joon Oh, Junsuk Choe,
• Abstract summary: Open-vocabulary segmentation (OVS) has gained attention for its ability to recognize a broader range of classes. OVS models show significant performance drops when applied to unseen domains beyond the previous training dataset. We propose a method that allows OVS models to learn information from new domains while preserving prior knowledge.
• Score: 24.169403141373927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-vocabulary segmentation (OVS) has gained attention for its ability to recognize a broader range of classes. However, OVS models show significant performance drops when applied to unseen domains beyond the previous training dataset. Fine-tuning these models on new datasets can improve performance, but often leads to the catastrophic forgetting of previously learned knowledge. To address this issue, we propose a method that allows OVS models to learn information from new domains while preserving prior knowledge. Our approach begins by evaluating the input sample's proximity to multiple domains, using precomputed multivariate normal distributions for each domain. Based on this prediction, we dynamically interpolate between the weights of the pre-trained decoder and the fine-tuned decoders. Extensive experiments demonstrate that this approach allows OVS models to adapt to new domains while maintaining performance on the previous training dataset. The source code is available at https://github.com/dongjunhwang/dwi.

Related papers

• Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting [70.83781268763215]
  Vision-language models (VLMs) have achieved impressive performance across diverse multimodal tasks by leveraging large-scale pre-training.<n>VLMs face unique challenges such as cross-modal feature drift, parameter interference due to shared architectures, and zero-shot capability erosion.<n>This survey aims to serve as a comprehensive and diagnostic reference for researchers developing lifelong vision-language systems.
  arXiv  Detail & Related papers  (2025-08-06T09:03:10Z)
• ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation [18.666044903856363]
  Training-free open-vocabulary semantic segmentation (OVS) aims to segment images given a set of arbitrary textual categories without costly model fine-tuning.<n>We introduce a data-quality-oriented framework, comprising a data pipeline to construct a reference set with well-paired segment-text embeddings and a simple similarity-based retrieval.<n>Our method outperforms all existing training-free OVS approaches, highlighting the importance of data-centric design for advancing OVS without training.
  arXiv  Detail & Related papers  (2025-06-26T13:22:03Z)
• Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning [64.1745161657794]
  Domain-Incremental Learning (DIL) involves the progressive adaptation of a model to new concepts across different domains. Recent advances in pre-trained models provide a solid foundation for DIL. However, learning new concepts often results in the catastrophic forgetting of pre-trained knowledge. We propose DUal ConsolidaTion (Duct) to unify and consolidate historical knowledge.
  arXiv  Detail & Related papers  (2024-10-01T17:58:06Z)
• Embedding And Clustering Your Data Can Improve Contrastive Pretraining [0.0]
  We explore extending training data stratification beyond source granularity by leveraging a pretrained text embedding model and the classic k-means clustering algorithm. Experimentally, we observe a notable increase in NDCG@10 when pretraining a BERT-based text embedding model on query-passage pairs from the MSMARCO passage retrieval dataset.
  arXiv  Detail & Related papers  (2024-07-26T17:36:40Z)
• 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)
• Contrastive Continual Multi-view Clustering with Filtered Structural Fusion [57.193645780552565]
  Multi-view clustering thrives in applications where views are collected in advance. It overlooks scenarios where data views are collected sequentially, i.e., real-time data. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. We propose Contrastive Continual Multi-view Clustering with Filtered Structural Fusion.
  arXiv  Detail & Related papers  (2023-09-26T14:18:29Z)
• Multivariate Prototype Representation for Domain-Generalized Incremental Learning [35.83706574551515]
  We design a DGCIL approach that remembers old classes, adapts to new classes, and can classify reliably objects from unseen domains. Our loss formulation maintains classification boundaries and suppresses the domain-specific information of each class.
  arXiv  Detail & Related papers  (2023-09-24T06:42:04Z)
• CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation [128.00940554196976]
  Vision-Language Continual Pretraining (VLCP) has shown impressive results on diverse downstream tasks by offline training on large-scale datasets. To support the study of Vision-Language Continual Pretraining (VLCP), we first contribute a comprehensive and unified benchmark dataset P9D. The data from each industry as an independent task supports continual learning and conforms to the real-world long-tail nature to simulate pretraining on web data.
  arXiv  Detail & Related papers  (2023-08-14T13:53:18Z)
• Multi-View Class Incremental Learning [57.14644913531313]
  Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. This paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views.
  arXiv  Detail & Related papers  (2023-06-16T08:13:41Z)
• Continual Vision-Language Representation Learning with Off-Diagonal Information [112.39419069447902]
  Multi-modal contrastive learning frameworks like CLIP typically require a large amount of image-text samples for training. This paper discusses the feasibility of continual CLIP training using streaming data.
  arXiv  Detail & Related papers  (2023-05-11T08:04:46Z)
• A Distinct Unsupervised Reference Model From The Environment Helps Continual Learning [5.332329421663282]
  Open-Set Semi-Supervised Continual Learning (OSSCL) is a more realistic semi-supervised continual learning setting. We present a model with two distinct parts: (i) the reference network captures general-purpose and task-agnostic knowledge in the environment by using a broad spectrum of unlabeled samples, and (ii) the learner network is designed to learn task-specific representations by exploiting supervised samples.
  arXiv  Detail & Related papers  (2023-01-11T15:05:36Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting [51.44987756859706]
  Off-the-shelf methods have some drawbacks to handle multiple domains. Lifelong Crowd Counting aims at alleviating the catastrophic forgetting and improving the generalization ability.
  arXiv  Detail & Related papers  (2022-05-06T15:37:56Z)
• Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis [96.53859361560505]
  Aspect-based Sentiment Analysis (ABSA) aims to determine the sentiment polarity towards an aspect. There always exists severe domain shift between the pretraining and downstream ABSA datasets. We introduce a unified alignment pretraining framework into the vanilla pretrain-finetune pipeline.
  arXiv  Detail & Related papers  (2021-10-26T04:03:45Z)
• Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning [14.226973149346883]
  Acquiring knowledge about new data samples without forgetting previous ones is a critical problem of continual learning. We propose a new method for unsupervised continual knowledge consolidation in generative models that relies on the partitioning of Variational Autoencoder's latent space. On top of the standard continual learning evaluation benchmarks, we evaluate our method on a new knowledge consolidation scenario and show that the proposed approach outperforms state-of-the-art by up to twofold.
  arXiv  Detail & Related papers  (2021-06-23T06:58:40Z)
• Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations [78.12377360145078]
  Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection. In this paper, we first study how biases in the dataset affect existing methods. We show that current contrastive approaches work surprisingly well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed and (iii) general versus domain-specific datasets.
  arXiv  Detail & Related papers  (2021-06-10T17:59:13Z)
• A Procedural World Generation Framework for Systematic Evaluation of Continual Learning [2.599882743586164]
  We introduce a computer graphics simulation framework that repeatedly renders only upcoming urban scene fragments. At its core lies a modular parametric generative model with adaptable generative factors.
  arXiv  Detail & Related papers  (2021-06-04T16:31:43Z)
• Universal Representation Learning from Multiple Domains for Few-shot Classification [41.821234589075445]
  We propose to learn a single set of universal deep representations by distilling knowledge of multiple separately trained networks. We show that the universal representations can be further refined for previously unseen domains by an efficient adaptation step.
  arXiv  Detail & Related papers  (2021-03-25T13:49:12Z)
• A Batch Normalization Classifier for Domain Adaptation [0.0]
  Adapting a model to perform well on unforeseen data outside its training set is a common problem that continues to motivate new approaches. We demonstrate that application of batch normalization in the output layer, prior to softmax activation, results in improved generalization across visual data domains in a refined ResNet model.
  arXiv  Detail & Related papers  (2021-03-22T08:03:44Z)
• $n$-Reference Transfer Learning for Saliency Prediction [73.17061116358036]
  We propose a few-shot transfer learning paradigm for saliency prediction. The proposed framework is gradient-based and model-agnostic. The results show that the proposed framework achieves a significant performance improvement.
  arXiv  Detail & Related papers  (2020-07-09T23:20:44Z)
• Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision [73.76277367528657]
  Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation. To cope with this limitation, automatically annotated data generated from graphic engines are used to train segmentation models. We propose a two-step self-supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together.
  arXiv  Detail & Related papers  (2020-04-16T15:24:11Z)

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.