One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering

• URL: http://arxiv.org/abs/2411.02210v1
• Date: Mon, 04 Nov 2024 16:04:59 GMT
• Title: One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering
• Authors: Deepayan Das, Davide Talon, Massimiliano Mancini, Yiming Wang, Elisa Ricci,
• Abstract summary: Vision-Language Models (VLMs) have shown significant promise in Visual Question Answering (VQA) tasks by leveraging web-scale multimodal datasets. These models often struggle with continual learning due to catastrophic forgetting when adapting to new tasks. We propose the first data-free method that leverages the language generation capability of a VLM, instead of relying on external models.
• Score: 31.025439143093585
• License:
• Abstract: Vision-Language Models (VLMs) have shown significant promise in Visual Question Answering (VQA) tasks by leveraging web-scale multimodal datasets. However, these models often struggle with continual learning due to catastrophic forgetting when adapting to new tasks. As an effective remedy to mitigate catastrophic forgetting, rehearsal strategy uses the data of past tasks upon learning new task. However, such strategy incurs the need of storing past data, which might not be feasible due to hardware constraints or privacy concerns. In this work, we propose the first data-free method that leverages the language generation capability of a VLM, instead of relying on external models, to produce pseudo-rehearsal data for addressing continual VQA. Our proposal, named as GaB, generates pseudo-rehearsal data by posing previous task questions on new task data. Yet, despite being effective, the distribution of generated questions skews towards the most frequently posed questions due to the limited and task-specific training data. To mitigate this issue, we introduce a pseudo-rehearsal balancing module that aligns the generated data towards the ground-truth data distribution using either the question meta-statistics or an unsupervised clustering method. We evaluate our proposed method on two recent benchmarks, \ie VQACL-VQAv2 and CLOVE-function benchmarks. GaB outperforms all the data-free baselines with substantial improvement in maintaining VQA performance across evolving tasks, while being on-par with methods with access to the past data.

Related papers

• Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond [13.867793835583463]
  We propose an uncertainty-aware memory-based approach to solve catastrophic forgetting. We retrieve samples with specific characteristics, and - by retraining the model on such samples - we demonstrate the potential of this approach.
  arXiv  Detail & Related papers  (2024-05-29T09:29:39Z)
• Adaptive Rentention & Correction for Continual Learning [114.5656325514408]
  A common problem in continual learning is the classification layer's bias towards the most recent task. We name our approach Adaptive Retention & Correction (ARC) ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets.
  arXiv  Detail & Related papers  (2024-05-23T08:43:09Z)
• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• Prior-Free Continual Learning with Unlabeled Data in the Wild [24.14279172551939]
  We propose a Prior-Free Continual Learning (PFCL) method to incrementally update a trained model on new tasks. PFCL learns new tasks without knowing the task identity or any previous data. Our experiments show that our PFCL method significantly mitigates forgetting in all three learning scenarios.
  arXiv  Detail & Related papers  (2023-10-16T13:59:56Z)
• Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets [73.2096288987301]
  We propose a simple approach that uses a small amount of downstream expert data to selectively query relevant behaviors from an offline, unlabeled dataset. We observe that our method learns to query only the relevant transitions to the task, filtering out sub-optimal or task-irrelevant data. Our simple querying approach outperforms more complex goal-conditioned methods by 20% across simulated and real robotic manipulation tasks from images.
  arXiv  Detail & Related papers  (2023-04-18T05:42:53Z)
• Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks [17.879087904904935]
  Multi-Task Learning (MTL) is widely-accepted in Natural Language Processing as a standard technique for learning multiple related tasks in one model. As systems usually evolve over time, adding a new task to an existing MTL model usually requires retraining the model from scratch on all the tasks. In this paper, we approach the problem of incrementally expanding MTL models' capability to solve new tasks over time by distilling the knowledge of an already trained model on n tasks into a new one for solving n+1 tasks.
  arXiv  Detail & Related papers  (2023-02-22T00:18:25Z)
• Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams [18.010095381310972]
  This paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-labeled tasks. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.
  arXiv  Detail & Related papers  (2022-11-21T07:40:28Z)
• Task Residual for Tuning Vision-Language Models [69.22958802711017]
  We propose a new efficient tuning approach for vision-language models (VLMs) named Task Residual Tuning (TaskRes) TaskRes explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. The proposed TaskRes is simple yet effective, which significantly outperforms previous methods on 11 benchmark datasets.
  arXiv  Detail & Related papers  (2022-11-18T15:09:03Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation [4.111899441919165]
  It is necessary for the model to learn novel relational patterns with very few labeled data while avoiding catastrophic forgetting of previous task knowledge. We propose a novel method based on embedding space regularization and data augmentation. Our method generalizes to new few-shot tasks and avoids catastrophic forgetting of previous tasks by enforcing extra constraints on the relational embeddings and by adding extra relevant data in a self-supervised manner.
  arXiv  Detail & Related papers  (2022-03-04T05:19:09Z)

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.