Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed asSemi-supervised Contrastive Learning (SsCL), which combines the well-known contrastive loss in self-supervised learning with the cross entropy loss in semi-supervised learning, and jointly optimizes the two objectives in an end-to-end way. The highlight is that different from self-training based semi-supervised learning that conducts prediction and retraining over the same model weights, SsCL interchanges the predictions over the unlabeled data between the two branches, and thus formulates a co-calibration procedure, which we find is beneficial for better prediction and avoids being trapped in local minimum. Towards this goal, the contrastive loss branch models pairwise similarities among samples, using the pseudo labels generated from the cross entropy branch, and in turn calibrates the prediction distribution of the cross entropy branch with the contrastive similarity. We show that SsCL produces more discriminative representation and is beneficial to semi-supervised learning. Notably, on ImageNet with ResNet50 as the backbone, SsCL achieves$\bm {60.2\%}$and$\bm {72.1\%}$top-1 accuracy with 1% and 10% labeled samples respectively, which significantly outperforms the baseline, and is better than previous semi-supervised and self-supervised methods.

How to achieve better results with fewer labeling costs remains a challenging task. In this paper, we present a new active learning framework, which for the first time incorporates contrastive learning into recently proposed one-bit supervision. Here one-bit supervision denotes a simple Yes or No query about the correctness of the model's prediction, and is more efficient than previous active learning methods requiring assigning accurate labels to the queried samples. We claim that such one-bit information is intrinsically in accordance with the goal of contrastive loss that pulls positive pairs together and pushes negative samples away. Towards this goal, we design an uncertainty metric to actively select samples for query. These samples are then fed into different branches according to the queried results. The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling. Additionally, we design a negative loss that penalizes the negative samples away from the incorrect predicted class, which can be treated as optimizing hard negatives for the corresponding category. Our method, termed as ObCP, produces a more powerful active learning framework, and experiments on several benchmarks demonstrate its superiority.

Semi-supervised learning, which assigns pseudo labels with models trained using limited labeled data, has been widely used in object detection to reduce the labeling cost. However, the provided pseudo annotations inevitably suffer noise since the initial model is not perfect. To address this issue, this paper introduces contrastive learning into semi-supervised object detection, and we claim that contrastive loss, which inherently relies on data augmentations, is much more robust than traditional softmax regression for noisy labels. To take full advantage of it in the detection task, we incorporate labels prior to contrastive loss and leverage plenty of region proposals to enhance diversity, which is crucial for contrastive learning. In this way, the model is optimized to make the region-level features with the same class be translation and scale invariant. Furthermore, we redesign the negative memory bank in contrastive learning to make the training more efficient. As far as we know, we are the first attempt that introduces contrastive learning in semi-supervised object detection. Experimental results on detection benchmarks demonstrate the superiority of our method. Notably, our method achieves 79.9% accuracy on VOC, which is 6.2% better than the supervised baseline and 0.7% improvement compared with the state-of-the-art method.