Fine-tuned CLIP Models are Efficient Video Learners (2212.03640v3)

Abstract: Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain. In this pursuit, new parametric modules are added to learn temporal information and inter-frame relationships which require meticulous design efforts. Furthermore, when the resulting models are learned on videos, they tend to overfit on the given task distribution and lack in generalization aspect. This begs the following question: How to effectively transfer image-level CLIP representations to videos? In this work, we show that a simple Video Fine-tuned CLIP (ViFi-CLIP) baseline is generally sufficient to bridge the domain gap from images to videos. Our qualitative analysis illustrates that the frame-level processing from CLIP image-encoder followed by feature pooling and similarity matching with corresponding text embeddings helps in implicitly modeling the temporal cues within ViFi-CLIP. Such fine-tuning helps the model to focus on scene dynamics, moving objects and inter-object relationships. For low-data regimes where full fine-tuning is not viable, we propose a `bridge and prompt' approach that first uses fine-tuning to bridge the domain gap and then learns prompts on language and vision side to adapt CLIP representations. We extensively evaluate this simple yet strong baseline on zero-shot, base-to-novel generalization, few-shot and fully supervised settings across five video benchmarks. Our code is available at https://github.com/muzairkhattak/ViFi-CLIP.

Fine-tuned CLIP Models as Efficient Video Learners

The exploration of effective methodologies for adapting pretrained models to new domains is a salient topic of inquiry, highlighted by the work investigating fine-tuning multimodal vision-LLMs for video-based tasks. "Fine-tuned CLIP Models are Efficient Video Learners" examines the efficacy of a streamlined fine-tuning procedure for CLIP (Contrastive Language–Image Pretraining) models in the context of video data.

The authors of this paper propose a compelling alternative to the prevalent approach, which often involves the introduction of complex architectural components to adapt image-pretrained models to the spatiotemporal nature of video data. The strategy introduced, ViFi-CLIP (Video Fine-tuned CLIP), challenges the necessity of intricate design efforts by demonstrating that conventional fine-tuning of the existing CLIP model is sufficient to close the domain gap between image and video tasks.

Methodological Insights

ViFi-CLIP revolves around the full fine-tuning of the CLIP architecture, which involves simultaneous fine-tuning of both the visual and text encoders. This approach stands in contrast to other approaches that focus on selectively adapting only specific components of the model or integrating additional spatiotemporal modules.

The capability of ViFi-CLIP is explored through multiple experimental settings: zero-shot, few-shot, base-to-novel class generalization, and fully-supervised learning. Benchmark datasets, such as Kinetics-400, HMDB-51, UCF-101, and SSv2, serve as platforms to evaluate the adaptability and performance of the approach. Importantly, the paper employs embedding-level temporal pooling as a mechanism to implicitly capture inter-frame relationships.

Results and Comparative Analysis

Empirical results indicate a significant improvement in video understanding capabilities when using ViFi-CLIP over baseline CLIP models, showcasing strong performance under various conditions, including zero-shot and few-shot learning contexts. Notably, ViFi-CLIP achieves commendable gains on competitive benchmarks, reflecting its ability to handle the intricate dynamics of video data without relying on added complexity typically seen in recent state-of-the-art methods like XCLIP.

The authors present comprehensive t-SNE visualizations which further validate the improved class separability achieved by ViFi-CLIP, emphasizing the model's enhanced generalization capabilities. In exploring these results, the authors posit that the simple fine-tuning mechanism adequately leverages the temporal dynamics inherent in video data, leading to embeddings that are more distinct and effectively clustered.

Implications and Future Directions

The findings reported in this paper carry significant implications for both theoretical and practical pursuits in computer vision and multimodal learning. The ability to efficiently fine-tune existing large-scale models for distinct video tasks without additional parametric complexity suggests a potential shift in how domain adaptation might be approached in the era of foundation models.

Moreover, the paper highlights the cost and resource efficiencies gained through streamlined adaptation processes, likely catalyzing further research into efficient cross-domain transfer learning strategies. In contexts where data is not abundant, the proposed "bridge and prompt" method furthers the model's applicability, focusing on prompt learning to handle scarce data scenarios.

Future explorations may involve deep investigations into the scalability of ViFi-CLIP across other complex domains, such as the fusion of additional modalities or tasks involving greater temporal coherence. Additionally, as the push for efficiency and simplicity persists amidst the rapid evolution of AI technologies, the principles outlined in this research may inform the broader conversation around model usability in diverse operational landscapes.

Ultimately, this paper underscores the potential of minimalistic yet powerful strategies in advancing the field of video understanding, encouraging a discourse on the balance between model complexity and functional efficacy.