Towards Versatile and Efficient Visual Knowledge Injection into Pre-trained Language Models with Cross-Modal Adapters

Humans learn language via multi-modal knowledge. However, due to the text-only pre-training scheme, most existing pre-trained language models (PLMs) are hindered from the multi-modal information. To inject visual knowledge into PLMs, existing methods incorporate either the text or image encoder of vision-language models (VLMs) to encode the visual information and update all the original parameters of PLMs for knowledge fusion. In this paper, we propose a new plug-and-play module, X-adapter, to flexibly leverage the aligned visual and textual knowledge learned in pre-trained VLMs and efficiently inject them into PLMs. Specifically, we insert X-adapters into PLMs, and only the added parameters are updated during adaptation. To fully exploit the potential in VLMs, X-adapters consist of two sub-modules, V-expert and T-expert, to fuse VLMs' image and text representations, respectively. We can opt for activating different sub-modules depending on the downstream tasks. Experimental results show that our method can significantly improve the performance on object-color reasoning and natural language understanding (NLU) tasks compared with PLM baselines.

Geometry-Aware Attenuation Field Learning for Sparse-View CBCT Reconstruction

Cone Beam Computed Tomography (CBCT) is the most widely used imaging method in dentistry. As hundreds of X-ray projections are needed to reconstruct a high-quality CBCT image (i.e., the attenuation field) in traditional algorithms, sparse-view CBCT reconstruction has become a main focus to reduce radiation dose. Several attempts have been made to solve it while still suffering from insufficient data or poor generalization ability for novel patients. This paper proposes a novel attenuation field encoder-decoder framework by first encoding the volumetric feature from multi-view X-ray projections, then decoding it into the desired attenuation field. The key insight is when building the volumetric feature, we comply with the multi-view CBCT reconstruction nature and emphasize the view consistency property by geometry-aware spatial feature querying and adaptive feature fusing. Moreover, the prior knowledge information learned from data population guarantees our generalization ability when dealing with sparse view input. Comprehensive evaluations have demonstrated the superiority in terms of reconstruction quality, and the downstream application further validates the feasibility of our method in real-world clinics.

GTV: Generating Tabular Data via Vertical Federated Learning

Generative Adversarial Networks (GANs) have achieved state-of-the-art results in tabular data synthesis, under the presumption of direct accessible training data. Vertical Federated Learning (VFL) is a paradigm which allows to distributedly train machine learning model with clients possessing unique features pertaining to the same individuals, where the tabular data learning is the primary use case. However, it is unknown if tabular GANs can be learned in VFL. Demand for secure data transfer among clients and GAN during training and data synthesizing poses extra challenge. Conditional vector for tabular GANs is a valuable tool to control specific features of generated data. But it contains sensitive information from real data - risking privacy guarantees. In this paper, we propose GTV, a VFL framework for tabular GANs, whose key components are generator, discriminator and the conditional vector. GTV proposes an unique distributed training architecture for generator and discriminator to access training data in a privacy-preserving manner. To accommodate conditional vector into training without privacy leakage, GTV designs a mechanism training-with-shuffling to ensure that no party can reconstruct training data with conditional vector. We evaluate the effectiveness of GTV in terms of synthetic data quality, and overall training scalability. Results show that GTV can consistently generate high-fidelity synthetic tabular data of comparable quality to that generated by centralized GAN algorithm. The difference on machine learning utility can be as low as to 2.7%, even under extremely imbalanced data distributions across clients and different number of clients.

FastMIM: Expediting Masked Image Modeling Pre-training for Vision

The combination of transformers and masked image modeling (MIM) pre-training framework has shown great potential in various vision tasks. However, the pre-training computational budget is too heavy and withholds the MIM from becoming a practical training paradigm. This paper presents FastMIM, a simple and generic framework for expediting masked image modeling with the following two steps: (i) pre-training vision backbones with low-resolution input images; and (ii) reconstructing Histograms of Oriented Gradients (HOG) feature instead of original RGB values of the input images. In addition, we propose FastMIM-P to progressively enlarge the input resolution during pre-training stage to further enhance the transfer results of models with high capacity. We point out that: (i) a wide range of input resolutions in pre-training phase can lead to similar performances in fine-tuning phase and downstream tasks such as detection and segmentation; (ii) the shallow layers of encoder are more important during pre-training and discarding last several layers can speed up the training stage with no harm to fine-tuning performance; (iii) the decoder should match the size of selected network; and (iv) HOG is more stable than RGB values when resolution transfers;. Equipped with FastMIM, all kinds of vision backbones can be pre-trained in an efficient way. For example, we can achieve 83.8%/84.1% top-1 accuracy on ImageNet-1K with ViT-B/Swin-B as backbones. Compared to previous relevant approaches, we can achieve comparable or better top-1 accuracy while accelerate the training procedure by $\sim$5$\times$. Code can be found in https://github.com/ggjy/FastMIM.pytorch.

Distributed Black-box Attack against Image Classification Cloud Services

Black-box adversarial attacks can fool image classifiers into misclassifying images without requiring access to model structure and weights. Recently proposed black-box attacks can achieve a success rate of more than 95% after less than 1,000 queries. The question then arises of whether black-box attacks have become a real threat against IoT devices that rely on cloud APIs to achieve image classification. To shed some light on this, note that prior research has primarily focused on increasing the success rate and reducing the number of required queries. However, another crucial factor for black-box attacks against cloud APIs is the time required to perform the attack. This paper applies black-box attacks directly to cloud APIs rather than to local models, thereby avoiding multiple mistakes made in prior research. Further, we exploit load balancing to enable distributed black-box attacks that can reduce the attack time by a factor of about five for both local search and gradient estimation methods.

Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion

Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been proposed as a solution to alleviate incompleteness and expand the coverage of KGs. It aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have mostly focused on designing local neighbor aggregators to learn entity-level information and/or imposing sequential dependency assumption at the triplet level to learn meta relation information. However, valuable pairwise triplet-level interactions and context-level relational information have been largely overlooked for learning meta representations of few-shot relations. In this paper, we propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize very well to new unseen relations. Extensive experiments on two benchmark datasets validate the superiority of HiRe against other state-of-the-art methods.

Adversarial Detection: Attacking Object Detection in Real Time

Intelligent robots rely on object detection models to perceive the environment. Following advances in deep learning security it has been revealed that object detection models are vulnerable to adversarial attacks. However, prior research primarily focuses on attacking static images or offline videos. Therefore, it is still unclear if such attacks could jeopardize real-world robotic applications in dynamic environments. This paper bridges this gap by presenting the first real-time online attack against object detection models. We devise three attacks that fabricate bounding boxes for nonexistent objects at desired locations. The attacks achieve a success rate of about 90% within about 20 iterations. The demo video is available at: https://youtu.be/zJZ1aNlXsMU.

Man-in-the-Middle Attack against Object Detection Systems

Is deep learning secure for robots? As embedded systems have access to more powerful CPUs and GPUs, deep-learning-enabled object detection systems become pervasive in robotic applications. Meanwhile, prior research unveils that deep learning models are vulnerable to adversarial attacks. Does this put real-world robots at threat? Our research borrows the idea of the Main-in-the-Middle attack from Cryptography to attack an object detection system. Our experimental results prove that we can generate a strong Universal Adversarial Perturbation (UAP) within one minute and then use the perturbation to attack a detection system via the Man-in-the-Middle attack. Our findings raise a serious concern over the applications of deep learning models in safety-critical systems such as autonomous driving.