In this paper, we introduce a novel approach to novel object captioning which employs relative contrastive learning to learn visual and semantic alignment. Our approach maximizes compatibility between regions and object tags in a contrastive manner. To set up a proper contrastive learning objective, for each image, we augment tags by leveraging the relative nature of positive and negative pairs obtained from foundation models such as CLIP. We then use the rank of each augmented tag in a list as a relative relevance label to contrast each top-ranked tag with a set of lower-ranked tags. This learning objective encourages the top-ranked tags to be more compatible with their image and text context than lower-ranked tags, thus improving the discriminative ability of the learned multi-modality representation. We evaluate our approach on two datasets and show that our proposed RCA-NOC approach outperforms state-of-the-art methods by a large margin, demonstrating its effectiveness in improving vision-language representation for novel object captioning.

Referring expression comprehension aims to localize a natural language description in an image. Using location priors to help reduce inaccuracies in cross-modal alignments is the state of the art for CNN-based methods tackling this problem. Recent Transformer-based models cast aside this idea, making the case for steering away from hand-designed components. In this work, we propose LUNA, which uses language as continuing anchors to guide box prediction in a Transformer decoder, and thus show that language-guided location priors can be effectively exploited in a Transformer-based architecture. Our method first initializes an anchor box from the input expression via a small "proto-decoder,'' and then uses this anchor and its refined successors as location guidance in a modified Transformer decoder. At each decoder layer, the anchor box is first used as a query for gathering multi-modal context, and then updated based on the gathered context (producing the next, refined anchor). In the end, a lightweight assessment pathway evaluates the quality of all produced anchors, yielding the final prediction in a dynamic way. This approach allows box decoding to be conditioned on learned anchors, which facilitates accurate grounding, as we shown in the experiments. Our method outperforms existing state-of-the-art methods on the datasets of ReferIt Game, RefCOCO/+/g, and Flickr30K Entities.

Deep neural networks (DNNs) have witnessed great successes in semantic segmentation, which requires a large number of labeled data for training. We present a novel learning framework called Uncertainty guided Cross-head Cotraining (UCC) for semi-supervised semantic segmentation. Our framework introduces weak and strong augmentations within a shared encoder to achieve cotraining, which naturally combines the benefits of consistency and self-training. Every segmentation head interacts with its peers and, the weak augmentation result is used for supervising the strong. The consistency training samples' diversity can be boosted by Dynamic Cross-Set Copy-Paste (DCSCP), which also alleviates the distribution mismatch and class imbalance problems. Moreover, our proposed Uncertainty Guided Re-weight Module (UGRM) enhances the self-training pseudo labels by suppressing the effect of the low-quality pseudo labels from its peer via modeling uncertainty. Extensive experiments on Cityscapes and PASCAL VOC 2012 demonstrate the effectiveness of our UCC. Our approach significantly outperforms other state-of-the-art semi-supervised semantic segmentation methods. It achieves 77.17%, 76.49% mIoU on Cityscapes and PASCAL VOC 2012 datasets respectively under 1/16 protocols, which are + 10.1%, + 7.91% better than the supervised baseline.