General Multi-label Image Classification with Transformers (2011.14027v1)

Abstract: Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the complex dependencies among visual features and labels. Our approach consists of a Transformer encoder trained to predict a set of target labels given an input set of masked labels, and visual features from a convolutional neural network. A key ingredient of our method is a label mask training objective that uses a ternary encoding scheme to represent the state of the labels as positive, negative, or unknown during training. Our model shows state-of-the-art performance on challenging datasets such as COCO and Visual Genome. Moreover, because our model explicitly represents the uncertainty of labels during training, it is more general by allowing us to produce improved results for images with partial or extra label annotations during inference. We demonstrate this additional capability in the COCO, Visual Genome, News500, and CUB image datasets.

• The paper introduces C-Tran, a Classification Transformer that models complex inter-label dependencies using a novel label mask objective.
• The method adapts to various inference scenarios, including standard, partial, and extra label conditions, significantly boosting metrics like mAP and F1 scores.
• Extensive experiments on benchmarks such as COCO-80 and Visual Genome demonstrate C-Tran's state-of-the-art performance and robust adaptability.

Overview of "General Multi-label Image Classification with Transformers"

The proposed paper titled "General Multi-label Image Classification with Transformers" presents a sophisticated framework for addressing multi-label image classification leveraging the Classification Transformer (C-Tran). This framework integrates the strengths of Transformer architectures to capture and exploit the complex dependencies between visual features and label sets in images.

The authors of this work focus on advancing multi-label classification, a task that goes beyond the typical single-label image recognition problems. The advent of C-Tran is designed to handle complex inter-label dependencies, a scenario often encountered in real-world imagery where multiple entities or attributes can co-occur.

Methodological Contributions

At the core of the proposed framework is the C-Tran model, which employs a Transformer encoder trained with a novel label mask objective. The label mask leverages a ternary encoding scheme during training, marking labels as positive, negative, or unknown to model varying levels of certainty about label presence robustly. This approach facilitates the Transformer in capturing dependencies not only among image features but also between labels, including hierarchical or lateral relationships.

The distinctiveness of the proposed method is exemplified by its adaptability across various inference contexts. Specifically:

1. Standard Multi-label Classification: The model predicts a set of labels for an input image entirely based on the visual data, devoid of any label pre-conditioning.
2. Inference with Partial Labels: In cases where some label information is accessible during inference, C-Tran can incorporate this partial data, thereby refining the prediction of unknown labels.
3. Inference with Extra Labels: The model can also accept labels that are not the main target but have contextual relevance for better prediction accuracy.

This adaptability is formalized through the label mask training procedure, ensuring that the model generalizes well across all possible inference settings with any degree of known label information. During training, the model sees various simulated conditions of known and unknown labels, allowing for robust learning of label dependencies and interactions with visual data.

Experimental Validation

The verification of C-Tran's performance is detailed through extensive experimentation across multiple challenging benchmarks, including COCO-80, Visual Genome (VG-500), NEWS-500, and CUB datasets. In terms of performance, C-Tran demonstrates state-of-the-art results, significantly boosting metrics like mean average precision (mAP) and F1 scores whenever compared to current leading methods in multi-label classification.

The inclusion of flexible encoder-decoder interactions facilitated by the state embeddings enables the C-Tran to process partial and extra label conditions comprehensively, offering improvements over heuristic inference methodologies such as iterative feedback propagation techniques.

Implications and Future Directions

The implications of C-Tran's utility in multi-label classification enhance both practical applications and theoretical understanding of label interactions. By explicitly modeling label dependencies and incorporating uncertainty at the heart of its architecture, the proposed model anticipates better scalability and adaptability in multi-label tasks, potentially influencing how multi-modal dependencies could be learned in future AI systems.

The flexibility provided by incorporating extra or partial labels during inference could evolve into more nuanced interactive systems that align with human cognitive models of scene understanding. Further developments could focus on reducing the computational complexity of C-Tran to handle even larger label sets, or expanding its utility into related domains such as multimedia tagging and video label extraction where temporal dynamics additionally impact label correlations.

In conclusion, the proposed C-Tran framework constitutes a significant step forward in the domain of multi-label image classification, setting a precedent for future investigations into combining neural transformers with adaptive encoding schemes to leverage complex constituent relationships in visual data.