SimCon Loss with Multiple Views for Text Supervised Semantic Segmentation
This addresses noisy web data for text-supervised semantic segmentation, offering incremental improvements with robust training and faster convergence.
The paper tackles the problem of noisy image-text alignment in text-supervised semantic segmentation by proposing a novel SimCon loss function that uses intra-modal similarities to select positive samples, achieving consistent improvements of +3.0% to +6.9% over state-of-the-art on benchmark datasets.
Learning to segment images purely by relying on the image-text alignment from web data can lead to sub-optimal performance due to noise in the data. The noise comes from the samples where the associated text does not correlate with the image's visual content. Instead of purely relying on the alignment from the noisy data, this paper proposes a novel loss function termed SimCon, which accounts for intra-modal similarities to determine the appropriate set of positive samples to align. Further, using multiple views of the image (created synthetically) for training and combining the SimCon loss with it makes the training more robust. This version of the loss is termed MV-SimCon. The empirical results demonstrate that using the proposed loss function leads to consistent improvements on zero-shot, text supervised semantic segmentation and outperforms state-of-the-art by $+3.0\%$, $+3.3\%$ and $+6.9\%$ on PASCAL VOC, PASCAL Context and MSCOCO, respectively. With test time augmentations, we set a new record by improving these results further to $58.7\%$, $26.6\%$, and $33.3\%$ on PASCAL VOC, PASCAL Context, and MSCOCO, respectively. In addition, using the proposed loss function leads to robust training and faster convergence.