A-CAP: Anticipation Captioning with Commonsense Knowledge

Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task called Anticipation Captioning, which generates a caption for an unseen oracle image using a sparsely temporally-ordered set of images. To tackle this new task, we propose a model called A-CAP, which incorporates commonsense knowledge into a pre-trained vision-language model, allowing it to anticipate the caption. Through both qualitative and quantitative evaluations on a customized visual storytelling dataset, A-CAP outperforms other image captioning methods and establishes a strong baseline for anticipation captioning. We also address the challenges inherent in this task.Comment: Accepted to CVPR 202

Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning

Vision-language pre-training models (VLP) are vulnerable, especially to multimodal adversarial samples, which can be crafted by adding imperceptible perturbations on both original images and texts. However, under the black-box setting, there have been no works to explore the transferability of multimodal adversarial attacks against the VLP models. In this work, we take CLIP as the surrogate model and propose a gradient-based multimodal attack method to generate transferable adversarial samples against the VLP models. By applying the gradient to optimize the adversarial images and adversarial texts simultaneously, our method can better search for and attack the vulnerable images and text information pairs. To improve the transferability of the attack, we utilize contrastive learning including image-text contrastive learning and intra-modal contrastive learning to have a more generalized understanding of the underlying data distribution and mitigate the overfitting of the surrogate model so that the generated multimodal adversarial samples have a higher transferability for VLP models. Extensive experiments validate the effectiveness of the proposed method

Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations

Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the "knowledge acquisition" problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query. We use a simple QA example from a children's story to motivate Braid's design and explain how the various components work together to produce a coherent logical explanation. Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to state-of-the-art results while providing frame-based explanations.Comment: Accepted at AAAI-202