VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition (2111.13579v4)

Abstract: Deep learning-based models encounter challenges when processing long-tailed data in the real world. Existing solutions usually employ some balancing strategies or transfer learning to deal with the class imbalance problem, based on the image modality. In this work, we present a visual-linguistic long-tailed recognition framework, termed VL-LTR, and conduct empirical studies on the benefits of introducing text modality for long-tailed recognition (LTR). Compared to existing approaches, the proposed VL-LTR has the following merits. (1) Our method can not only learn visual representation from images but also learn corresponding linguistic representation from noisy class-level text descriptions collected from the Internet; (2) Our method can effectively use the learned visual-linguistic representation to improve the visual recognition performance, especially for classes with fewer image samples. We also conduct extensive experiments and set the new state-of-the-art performance on widely-used LTR benchmarks. Notably, our method achieves 77.2% overall accuracy on ImageNet-LT, which significantly outperforms the previous best method by over 17 points, and is close to the prevailing performance training on the full ImageNet. Code is available at https://github.com/ChangyaoTian/VL-LTR.

Evaluation of "VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition"

The paper presented in the paper introduces VL-LTR, a novel framework leveraging visual-linguistic pre-trained models to enhance long-tailed visual recognition (LTR). Despite the promising performance of contemporary computer vision foundation models such as CLIP and ALI-GN on various tasks, their efficacy in addressing long-tailed data distributions has yet to be thoroughly tested. This research paper posits that incorporating text modality via a robust visual-linguistic approach can significantly address the imbalances inherent in long-tailed datasets, setting a new benchmark in this domain.

At the crux of this approach is the proposed VL-LTR framework, which is distinctive for its integration of class-wise text descriptions with visual data, thereby forming a more comprehensive, multimodal representation. Unlike prior LTR methods that predominantly focus on visual data rebalancing through techniques like re-sampling or re-weighting, this approach leverages the synergy between visual and linguistic representations to enhance recognition rates for underrepresented classes.

Methodology and Experimental Insights

VL-LTR comprises two main components:

1. Class-wise Visual-Linguistic Pre-training (CVLP): This component leverages pretrained visual and LLMs to establish correlations at the class level between images and associated textual descriptions. The textual data, encompassing potentially noisy Internet-sourced descriptions, is utilized to improve feature extraction for classes with limited visual data.
2. Language-Guided Recognition (LGR) Head: Post-pretraining, this component refines image classification by integrating filtered, high-confidence text descriptions, thus improving performance by dynamically leveraging visual and corresponding linguistic features.

The experimental results compellingly demonstrate the efficacy of VL-LTR across several benchmarks: ImageNet-LT, Places-LT, and iNaturalist 2018. Notably, it achieves a remarkable 77.2% overall accuracy on ImageNet-LT using the ViT-Base backbone, surpassing previous methods by over 17 percentage points. In addition, the framework shows significant improvements in medium-shot and few-shot categories, which are typically challenging due to limited training samples.

Implications and Future Directions

VL-LTR's successful integration of text information reveals promising directions for future research. By effectively bridging visual and text domains, the methodology not only improves long-tailed class representation but also provides a more flexible and robust approach to multimodal learning. This integration could extend beyond image classification to a broader range of visual tasks, potentially impacting areas such as image captioning, retrieval, and semantic segmentation.

Though robust, the method is not without its challenges. The reliance on a large-scale pretrained LLM implies substantial computational overhead and resource constraints, which could pose challenges for deployment in resource-limited settings. Additionally, the model's dependence on text descriptions also highlights a potential limitation when facing domains with scarce textual data.

In conclusion, VL-LTR offers a significant advancement in the field of long-tailed visual recognition by utilizing class-level visual-linguistic representations. Its success presents compelling evidence for the benefits of multimodal learning, encouraging further investigation into more efficient integration methods and extending its applicability to diverse visual and linguistic tasks. The framework stands as a robust contribution in the ongoing development of comprehensive multimodal AI systems.