Knowledge Graph Large Language Model (KG-LLM) for Link Prediction

The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques. This paper introduces a novel methodology, the Knowledge Graph Large Language Model Framework (KG-LLM), which leverages pivotal NLP paradigms, including chain-of-thought (CoT) prompting and in-context learning (ICL), to enhance multi-hop link prediction in KGs. By converting the KG to a CoT prompt, our framework is designed to discern and learn the latent representations of entities and their interrelations. To show the efficacy of the KG-LLM Framework, we fine-tune three leading Large Language Models (LLMs) within this framework, employing both non-ICL and ICL tasks for a comprehensive evaluation. Further, we explore the framework's potential to provide LLMs with zero-shot capabilities for handling previously unseen prompts. Our experimental findings discover that integrating ICL and CoT not only augments the performance of our approach but also significantly boosts the models' generalization capacity, thereby ensuring more precise predictions in unfamiliar scenarios.

Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we introduce an innovative pre-training approach, Grounded Point Colorization (GPC), to bridge the gap between data and labels by teaching the model to colorize LiDAR point clouds, equipping it with valuable semantic cues. To tackle challenges arising from color variations and selection bias, we incorporate color as "context" by providing ground-truth colors as hints during colorization. Experimental results on the KITTI and Waymo datasets demonstrate GPC's remarkable effectiveness. Even with limited labeled data, GPC significantly improves fine-tuning performance; notably, on just 20% of the KITTI dataset, GPC outperforms training from scratch with the entire dataset. In sum, we introduce a fresh perspective on pre-training for 3D object detection, aligning the objective with the model's intended role and ultimately advancing the accuracy and efficiency of 3D object detection for autonomous vehicles.

Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) with only image-level supervision has garnered increasing attention due to its low annotation cost compared to pixel-level annotation. Most existing methods rely on Class Activation Maps (CAM) to generate pixel-level pseudo labels for supervised training. However, it is well known that CAM often suffers from partial activation -- activating the most discriminative part instead of the entire object area, and false activation -- unnecessarily activating the background around the object. In this study, we introduce a simple yet effective approach to address these limitations by harnessing the recently released Segment Anything Model (SAM) to generate higher-quality pseudo labels with CAM. SAM is a segmentation foundation model that demonstrates strong zero-shot ability in partitioning images into segments but lacks semantic labels for these regions. To circumvent this, we employ pseudo labels for a specific class as the signal to select the most relevant masks and label them to generate the refined pseudo labels for this class. The segments generated by SAM are highly precise, leading to substantial improvements in partial and false activation. Moreover, existing post-processing modules for producing pseudo labels, such as AffinityNet, are often computationally heavy, with a significantly long training time. Surprisingly, we discovered that using the initial CAM with SAM can achieve on-par performance as the post-processed pseudo label generated from these modules with much less computational cost. Our approach is highly versatile and capable of seamless integration into existing WSSS models without modification to base networks or pipelines. Despite its simplicity, our approach improves the mean Intersection over Union (mIoU) of pseudo labels from five state-of-the-art WSSS methods by 6.2\% on average on the PASCAL VOC 2012 dataset.

Learning with Free Object Segments for Long-Tailed Instance Segmentation

One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects. In this paper, we explore the possibility to increase the training examples without laborious data collection and annotation. We find that an abundance of instance segments can potentially be obtained freely from object-centric images, according to two insights: (i) an object-centric image usually contains one salient object in a simple background; (ii) objects from the same class often share similar appearances or similar contrasts to the background. Motivated by these insights, we propose a simple and scalable framework FreeSeg for extracting and leveraging these "free" object foreground segments to facilitate model training in long-tailed instance segmentation. Concretely, we investigate the similarity among object-centric images of the same class to propose candidate segments of foreground instances, followed by a novel ranking of segment quality. The resulting high-quality object segments can then be used to augment the existing long-tailed datasets, e.g., by copying and pasting the segments onto the original training images. Extensive experiments show that FreeSeg yields substantial improvements on top of strong baselines and achieves state-of-the-art accuracy for segmenting rare object categories.

Adversarial Defense by Latent Style Transformations

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack-agnostic defense against adversarial attacks on high-resolution images by detecting suspicious inputs. The intuition behind our approach is that the essential characteristics of a normal image are generally consistent with non-essential style transformations, e.g., slightly changing the facial expression of human portraits. In contrast, adversarial examples are generally sensitive to such transformations. In our approach to detect adversarial instances, we propose an in\underline{V}ertible \underline{A}utoencoder based on the \underline{S}tyleGAN2 generator via \underline{A}dversarial training (VASA) to inverse images to disentangled latent codes that reveal hierarchical styles. We then build a set of edited copies with non-essential style transformations by performing latent shifting and reconstruction, based on the correspondences between latent codes and style transformations. The classification-based consistency of these edited copies is used to distinguish adversarial instances.

Defending Adversarial Attacks via Semantic Feature Manipulation

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner. The intuition is that the classification result of a normal image is generally resistant to non-significant intrinsic feature changes, e.g., varying thickness of handwritten digits. In contrast, adversarial examples are sensitive to such changes since the perturbation lacks transferability. To enable manipulation of features, a combo-variational autoencoder is applied to learn disentangled latent codes that reveal semantic features. The resistance to classification change over the morphs, derived by varying and reconstructing latent codes, is used to detect suspicious inputs. Further, combo-VAE is enhanced to purify the adversarial examples with good quality by considering both class-shared and class-unique features. We empirically demonstrate the effectiveness of detection and the quality of purified instance. Our experiments on three datasets show that FM-Defense can detect nearly $100\%$ of adversarial examples produced by different state-of-the-art adversarial attacks. It achieves more than $99\%$ overall purification accuracy on the suspicious instances that close the manifold of normal examples.

OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning

Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data, which is significant for numerous domain applications, e.g. in industrial inspection, medical imaging, and security enforcement. There are two key research challenges associated with existing anomaly detention approaches: (1) many of them perform well on low-dimensional problems however the performance on high-dimensional instances is limited, such as images; (2) many of them depend on often still rely on traditional supervised approaches and manual engineering of features, while the topic has not been fully explored yet using modern deep learning approaches, even when the well-label samples are limited. In this paper, we propose a One-for-all Image Anomaly Detection system (OIAD) based on disentangled learning using only clean samples. Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, called structure consistency. We implement this idea and evaluate its performance for anomaly detention. Our experiments with three datasets show that OIAD can detect over $90\%$ of anomalies while maintaining a high low false alarm rate. It can also detect suspicious samples from samples labeled as clean, coincided with what humans would deem unusual.

Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models

Transfer learning, that transfer the learned knowledge of pre-trained Teacher models over large datasets via fine-tuning, provides an effective solution for feasibly and fast customize accurate Student models. Many pre-trained Teacher models are publicly available and maintained by public platforms, increasing their vulnerability to backdoor attacks. In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: pruning-based, retraining-based and input pre-processing-based defenses. Specifically, (A) ranking-based selection mechanism to speed up the backdoor trigger generation and perturbation process while defeating pruning-based and/or retraining-based defenses. (B) autoencoder-powered trigger generation is proposed to produce a robust trigger that can defeat the input pre-processing-based defense, while guaranteeing that selected neuron(s) can be significantly activated. (C) defense-aware retraining to generate the manipulated model using reverse-engineered model inputs. We use the real-world image and bioelectric signal analytics applications to demonstrate the power of our attack and conduct a comprehensive empirical analysis of the possible factors that affect the attack. The efficiency/effectiveness and feasibility/easiness of such attacks are validated by empirically evaluating the state-of-the-art image, Electroencephalography (EEG) and Electrocardiography (ECG) learning systems.