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Continual Learning with Probabilistic Finetuning for Vision-Language Models

Core Concepts
CLAP4CLIP develops probabilistic modeling over task-specific modules with visual-guided text features, providing more reliable fine-tuning in continual learning. It further alleviates forgetting by exploiting the rich pre-trained knowledge of CLIP for weight initialization and distribution regularization of task-specific modules.
The paper proposes CLAP4CLIP, a continual learning (CL) framework that leverages the pre-trained CLIP model for CL tasks. The key contributions are: Probabilistic modeling: CLAP4CLIP develops a variational inference framework to model the distribution of visual-guided text features, rather than modeling the distribution of either modality alone. This helps address biases in image-text alignment. Visual-guided attention: CLAP4CLIP uses a visual-guided attention (VGA) module to refine the task-specific text features based on the visual features, alleviating cross-modal deviation during incremental training. Task-specific distribution encoders: CLAP4CLIP maintains task-specific encoders to parameterize the posterior distributions over the task embeddings, making the class-specific latent variables more separable across tasks. Leveraging CLIP's pre-trained knowledge: CLAP4CLIP initializes the task-specific modules' weights using the language information from CLIP's pre-trained text features. It also regularizes the past task distributions using a language-aware distillation loss. The experiments show that CLAP4CLIP consistently outperforms existing deterministic finetuning methods for CLIP in continual learning settings across multiple datasets. The probabilistic nature of CLAP4CLIP also provides superior uncertainty quantification capabilities for novel data detection and exemplar selection within CL setups.
"Learning in the real world involves dealing with the ever-changing distributions of task streams and their data." "Given the constraints on resources and privacy, there is also no guarantee on re-training a network on all previously seen data." "Recent years have seen a surge of pre-trained multi-modal foundation models achieving state-of-the-art performances on several domains, such as the Contrastive Language-Image Pre-training (CLIP) model."
"An issue with the existing deterministic approaches is that these overlook the uncertainties arising from many possible interactions between visual and textual cues." "Uncertainty-awareness can further be crucial for CL models deployed in mission-critical settings (healthcare, transport, etc.) as it can help calibrate predictions to reliably assess the models' predictive confidences."

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by Saurav Jha,D... at 03-29-2024

Deeper Inquiries

How can the proposed probabilistic finetuning approach be extended to other pre-trained vision-language models beyond CLIP

The proposed probabilistic finetuning approach can be extended to other pre-trained vision-language models beyond CLIP by following a similar framework. First, the model would need to have a pre-trained backbone for both the vision and language modalities. The probabilistic modeling over task-specific modules with visual-guided text features can be adapted to these models by incorporating variational inference techniques to capture uncertainties in the interactions between the modalities. Additionally, task-specific distribution encoders can be implemented to improve the discriminative capabilities of the model across different tasks. By leveraging the rich pre-trained knowledge of these models and incorporating probabilistic finetuning, it is possible to enhance their adaptability to continual learning tasks.

What are the potential limitations of the language-aware regularization technique, and how can it be further improved to better capture the semantic relationships between tasks

The language-aware regularization technique may have limitations in capturing the semantic relationships between tasks due to the complexity and variability of natural language. One potential limitation is the reliance on hand-crafted prompts for initializing the weights and regularizing the distributions. These prompts may not always capture the full semantic diversity of the tasks, leading to suboptimal performance. To address this limitation, the language-aware regularization technique can be further improved by incorporating more advanced natural language processing techniques, such as contextual embeddings or transformer models, to generate task-specific language features. By leveraging more sophisticated language representations, the model can better capture the nuances and semantic relationships between tasks, leading to more effective regularization and improved performance.

Can the visual-guided attention mechanism be adapted to leverage the rich spatial information in the visual features, rather than just the global context, to further enhance the cross-modal alignment

The visual-guided attention mechanism can be adapted to leverage the rich spatial information in the visual features by incorporating spatial attention mechanisms. Instead of using globally pooled visual features, the model can utilize spatial attention mechanisms, such as spatial transformers or spatial attention modules, to focus on specific regions of the image that are relevant to the text features. By incorporating spatial information into the attention mechanism, the model can align the visual and textual features at a more granular level, capturing fine-grained details and improving the overall cross-modal alignment. This adaptation can enhance the model's ability to understand the spatial relationships between visual and textual elements, leading to more accurate and contextually relevant representations.