Scene Graph Embeddings Using Relative Similarity Supervision

Scene graphs are a powerful structured representation of the underlying content of images, and embeddings derived from them have been shown to be useful in multiple downstream tasks. In this work, we employ a graph convolutional network to exploit structure in scene graphs and produce image embeddings useful for semantic image retrieval. Different from classification-centric supervision traditionally available for learning image representations, we address the task of learning from relative similarity labels in a ranking context. Rooted within the contrastive learning paradigm, we propose a novel loss function that operates on pairs of similar and dissimilar images and imposes relative ordering between them in embedding space. We demonstrate that this Ranking loss, coupled with an intuitive triple sampling strategy, leads to robust representations that outperform well-known contrastive losses on the retrieval task. In addition, we provide qualitative evidence of how retrieved results that utilize structured scene information capture the global context of the scene, different from visual similarity search.Comment: Accepted to AAAI 202

Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions

As multimodal learning finds applications in a wide variety of high-stakes societal tasks, investigating their robustness becomes important. Existing work has focused on understanding the robustness of vision-and-language models to imperceptible variations on benchmark tasks. In this work, we investigate the robustness of multimodal classifiers to cross-modal dilutions - a plausible variation. We develop a model that, given a multimodal (image + text) input, generates additional dilution text that (a) maintains relevance and topical coherence with the image and existing text, and (b) when added to the original text, leads to misclassification of the multimodal input. Via experiments on Crisis Humanitarianism and Sentiment Detection tasks, we find that the performance of task-specific fusion-based multimodal classifiers drops by 23.3% and 22.5%, respectively, in the presence of dilutions generated by our model. Metric-based comparisons with several baselines and human evaluations indicate that our dilutions show higher relevance and topical coherence, while simultaneously being more effective at demonstrating the brittleness of the multimodal classifiers. Our work aims to highlight and encourage further research on the robustness of deep multimodal models to realistic variations, especially in human-facing societal applications. The code and other resources are available at https://claws-lab.github.io/multimodal-robustness/.Comment: Accepted at the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP); Full Paper (Oral

CyCLIP: Cyclic Contrastive Language-Image Pretraining

Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness. Such models typically require joint reasoning in the image and text representation spaces for downstream inference tasks. Contrary to prior beliefs, we demonstrate that the image and text representations learned via a standard contrastive objective are not interchangeable and can lead to inconsistent downstream predictions. To mitigate this issue, we formalize consistency and propose CyCLIP, a framework for contrastive representation learning that explicitly optimizes for the learned representations to be geometrically consistent in the image and text space. In particular, we show that consistent representations can be learned by explicitly symmetrizing (a) the similarity between the two mismatched image-text pairs (cross-modal consistency); and (b) the similarity between the image-image pair and the text-text pair (in-modal consistency). Empirically, we show that the improved consistency in CyCLIP translates to significant gains over CLIP, with gains ranging from 10%-24% for zero-shot classification accuracy on standard benchmarks (CIFAR-10, CIFAR-100, ImageNet1K) and 10%-27% for robustness to various natural distribution shifts. The code is available at https://github.com/goel-shashank/CyCLIP.Comment: 19 pages, 13 tables, 6 figures, Oral at NeuRIPS 202

On Aggregating Labels from Multiple Crowd Workers to Infer Relevance of Documents

Abstract. We consider the problem of acquiring relevance judgements for in-formation retrieval (IR) test collections through crowdsourcing when no true relevance labels are available. We collect multiple, possibly noisy relevance la-bels per document from workers of unknown labelling accuracy. We use these labels to infer the document relevance based on two methods. The first method is the commonly used majority voting (MV) which determines the document relevance based on the label that received the most votes, treating all the work-ers equally. The second is a probabilistic model that concurrently estimates the document relevance and the workers accuracy using expectation maximization (EM). We run simulations and conduct experiments with crowdsourced rele-vance labels from the INEX 2010 Book Search track to investigate the accuracy and robustness of the relevance assessments to the noisy labels. We observe the effect of the derived relevance judgments on the ranking of the search systems. Our experimental results show that the EM method outperforms the MV method in the accuracy of relevance assessments and IR systems ranking. The performance improvements are especially noticeable when the number of labels per document is small and the labels are of varied quality.

Statement

programm