End-to-end weakly-supervised semantic alignment (1712.06861v2)

Abstract: We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category. This is a challenging task due to large intra-class variation, changes in viewpoint and background clutter. We present the following three principal contributions. First, we develop a convolutional neural network architecture for semantic alignment that is trainable in an end-to-end manner from weak image-level supervision in the form of matching image pairs. The outcome is that parameters are learnt from rich appearance variation present in different but semantically related images without the need for tedious manual annotation of correspondences at training time. Second, the main component of this architecture is a differentiable soft inlier scoring module, inspired by the RANSAC inlier scoring procedure, that computes the quality of the alignment based on only geometrically consistent correspondences thereby reducing the effect of background clutter. Third, we demonstrate that the proposed approach achieves state-of-the-art performance on multiple standard benchmarks for semantic alignment.

End-to-End Weakly-Supervised Semantic Alignment for Image Correspondence

In the domain of computer vision, semantic alignment refers to the task of establishing dense correspondence between images containing objects of the same category. This paper addresses the inherent challenges of semantic alignment resulting from intra-class variation, viewpoint changes, and background clutter. The research introduces a convolutional neural network architecture capable of learning semantic alignment in an end-to-end manner, using weak supervision, specifically leveraging matching image pairs without explicit correspondence annotations.

Contributions and Methodology

The authors present a novel approach with three principal contributions:

1. End-to-End Trainable Architecture: The paper develops a CNN model that learns semantic alignment with weak image-level supervision. This framework eschews the requirement for manually annotated correspondences during training, which traditionally impedes large-scale learning efforts. Instead, it learns from image pairs that are semantically related but do not require annotation of dense correspondence points, thereby capturing the rich appearance variations inherently present in real-world datasets.
2. Differentiable Soft Inlier Scoring Module: Inspired by the robust RANSAC algorithm, the authors propose a soft inlier scoring mechanism that evaluates the quality of alignment based on geometrically consistent correspondences. This module diminishes the adverse effect of background clutter in images, enhancing the reliability of correspondence predictions. The scoring mechanism operates within a differentiable framework, facilitating end-to-end backpropagation to optimize the model effectively.
3. State-of-the-Art Performance: The proposed model sets new benchmarks in semantic alignment accuracy across multiple well-established datasets. The architecture outperforms existing methods that employ strong supervision, demonstrating the efficacy of weak supervision in capturing complex variations in appearance and geometry.

Evaluation and Results

The authors evaluate their architecture on three standard benchmarks: PF-PASCAL, Caltech-101, and TSS. Despite using a weak supervision approach, their model significantly outperforms previous state-of-the-art methods that rely on strong supervision techniques, with improvements seen across various metrics such as PCK, LT-ACC, and IoU. Additionally, the results on diverse dataset categories illustrate the model's generalized capability to adapt to different object categories and image conditions without further training adjustments.

Implications and Future Work

The proposed method offers substantial theoretical and practical implications for the field of computer vision, especially in areas involving large-scale image datasets. The capability to train models from weak supervision simplifies data collection processes and reduces the reliance on costly and time-consuming manual annotations. Practically, it paves the way for deploying semantic alignment models in real-world applications like object recognition, image editing, and robotics, where manual annotations are often infeasible.

Future research could explore the application of this framework to more complex scenarios involving multiple objects within an image or entirely non-matching pairs, further expanding the model's versatility and applicability. Additionally, incorporating the same weak supervision strategy in other subdomains of visual correspondence tasks might lead to more generalized approaches adaptable across varying degrees of image complexity and categorical diversity.

In conclusion, this paper offers a compelling argument for end-to-end trainable semantic alignment using weakly supervised learning, opening avenues for scaling and improving correspondence models in practical, large-scale environments.