pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Manipulation using Behavior Trees
Authors: Jacques Cloete, Wolfgang Merkt, Ioannis Havoutis
Date: 2024-06-20 00:00:00

Many manipulation tasks pose a challenge since they depend on non-visual environmental information that can only be determined after sustained physical interaction has already begun. This is particularly relevant for effort-sensitive, dynamics-dependent tasks such as tightening a valve. To perform these tasks safely and reliably, robots must be able to quickly adapt in response to unexpected changes during task execution, and should also learn from past experience to better inform future decisions. Humans can intuitively respond and adapt their manipulation strategy to suit such problems, but representing and implementing such behaviors for robots remains a challenge. In this work we show how this can be achieved within the framework of behavior trees. We present the adaptive behavior tree, a scalable and generalizable behavior tree design that enables a robot to quickly adapt to and learn from both visual and non-visual observations during task execution, preempting task failure or switching to a different manipulation strategy. The adaptive behavior tree selects the manipulation strategy that is predicted to optimize task performance, and learns from past experience to improve these predictions for future attempts. We test our approach on a variety of tasks commonly found in industry; the adaptive behavior tree demonstrates safety, robustness (100% success rate) and efficiency in task completion (up to 36% task speedup from the baseline).
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
Authors: Xuefei Ning, Zifu Wang, Shiyao Li, Zinan Lin, Peiran Yao, Tianyu Fu, Matthew B. Blaschko, Guohao Dai, Huazhong Yang, Yu Wang
Date: 2024-06-20 00:00:00
Tasks: In-Context Learning, Knowledge Distillation

Teaching to improve student models (e.g., knowledge distillation) is an extensively studied methodology in LLMs. However, for humans, teaching improves not only students but also teachers, by fostering more rigorous and clear reasoning as well as knowledge building. We ask: Can LLMs also learn by teaching (LbT) for better reasoning? If the answer is yes, we can potentially unlock the possibility of continuously advancing the models without solely relying on human-produced data or stronger models. In this paper, we provide a preliminary exploration on this question. We show that LbT ideas can be incorporated into existing LLM training/prompting pipelines and bring improvements. Specifically, we design three methods, each mimicking one of the three levels of LbT: observing students' feedback, learning from the feedback, and learning iteratively, with the goals of improving answer accuracy without training or improving models' inherent capability with fine-tuning. We reveal some findings: (1) Teaching materials that make it easier for students to learn have clearer and more accurate logic when using in-context learning as the student's "learning" method; (2) Weak-to-strong generalization: LbT might help improve strong models by teaching weak models; (3) Diversity in students might help: teaching multiple students could be better than teaching one student or the teacher itself. We hope that our exploration can inspire future research on LbT and more broadly adopting the advanced techniques in education to improve LLMs. The code and website are at https://github.com/imagination-research/lbt and https://sites.google.com/view/llm-learning-by-teaching.
SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors
Authors: Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, Prateek Mittal
Date: 2024-06-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Evaluating aligned large language models' (LLMs) ability to recognize and reject unsafe user requests is crucial for safe, policy-compliant deployments. Existing evaluation efforts, however, face three limitations that we address with SORRY-Bench, our proposed benchmark. First, existing methods often use coarse-grained taxonomies of unsafe topics, and are over-representing some fine-grained topics. For example, among the ten existing datasets that we evaluated, tests for refusals of self-harm instructions are over 3x less represented than tests for fraudulent activities. SORRY-Bench improves on this by using a fine-grained taxonomy of 45 potentially unsafe topics, and 450 class-balanced unsafe instructions, compiled through human-in-the-loop methods. Second, linguistic characteristics and formatting of prompts are often overlooked, like different languages, dialects, and more -- which are only implicitly considered in many evaluations. We supplement SORRY-Bench with 20 diverse linguistic augmentations to systematically examine these effects. Third, existing evaluations rely on large LLMs (e.g., GPT-4) for evaluation, which can be computationally expensive. We investigate design choices for creating a fast, accurate automated safety evaluator. By collecting 7K+ human annotations and conducting a meta-evaluation of diverse LLM-as-a-judge designs, we show that fine-tuned 7B LLMs can achieve accuracy comparable to GPT-4 scale LLMs, with lower computational cost. Putting these together, we evaluate over 40 proprietary and open-source LLMs on SORRY-Bench, analyzing their distinctive refusal behaviors. We hope our effort provides a building block for systematic evaluations of LLMs' safety refusal capabilities, in a balanced, granular, and efficient manner.
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought
Authors: Gabriel Sarch, Lawrence Jang, Michael J. Tarr, William W. Cohen, Kenneth Marino, Katerina Fragkiadaki
Date: 2024-06-20 00:00:00
Tasks: Action Anticipation, Continual Learning, Decision Making, Few-Shot Learning, In-Context Learning, Instruction Following, Prompt Engineering, Retrieval

Large-scale LLMs and VLMs excel at few-shot learning but require high-quality examples. We introduce In-Context Abstraction Learning (ICAL), which iteratively refines suboptimal trajectories into high-quality data with optimized actions and detailed reasoning. Given an inefficient demonstration, a VLM corrects actions and annotates causal relationships, object states, subgoals, and task-relevant visuals, forming "programs of thought." With human feedback, these programs are improved as the agent executes them in a similar environment. The resulting examples, used as prompt context or fine-tuning data, significantly boost decision-making while reducing human feedback needs. ICAL surpasses state-of-the-art in TEACh (dialogue-based instruction following), VisualWebArena (multimodal web agents), and Ego4D (egocentric video action anticipation). In TEACh, combining fine-tuning and retrieval on ICAL examples outperforms raw human demonstrations and expert examples, achieving a 17.5% increase in goal-condition success. In VisualWebArena, retrieval-augmented GPT-4V with ICAL improves task success rate 1.6x over GPT-4V, while fine-tuning Qwen2-VL achieves a 2.8x improvement. In Ego4D, ICAL outperforms few-shot GPT-4V and remains competitive with supervised models. Overall, ICAL scales 2x better than raw human demonstrations and reduces manual prompt engineering.
Adversaries Can Misuse Combinations of Safe Models
Authors: Erik Jones, Anca Dragan, Jacob Steinhardt
Date: 2024-06-20 00:00:00
Tasks: Red Teaming

Developers try to evaluate whether an AI system can be misused by adversaries before releasing it; for example, they might test whether a model enables cyberoffense, user manipulation, or bioterrorism. In this work, we show that individually testing models for misuse is inadequate; adversaries can misuse combinations of models even when each individual model is safe. The adversary accomplishes this by first decomposing tasks into subtasks, then solving each subtask with the best-suited model. For example, an adversary might solve challenging-but-benign subtasks with an aligned frontier model, and easy-but-malicious subtasks with a weaker misaligned model. We study two decomposition methods: manual decomposition where a human identifies a natural decomposition of a task, and automated decomposition where a weak model generates benign tasks for a frontier model to solve, then uses the solutions in-context to solve the original task. Using these decompositions, we empirically show that adversaries can create vulnerable code, explicit images, python scripts for hacking, and manipulative tweets at much higher rates with combinations of models than either individual model. Our work suggests that even perfectly-aligned frontier systems can enable misuse without ever producing malicious outputs, and that red-teaming efforts should extend beyond single models in isolation.
Full-Scale Indexing and Semantic Annotation of CT Imaging: Boosting FAIRness
Authors: Hannes Ulrich, Robin Hendel, Santiago Pazmino, Björn Bergh, Björn Schreiweis
Date: 2024-06-21 00:00:00
Tasks: Computed Tomography (CT), Fairness

Background: The integration of artificial intelligence into medicine has led to significant advances, particularly in diagnostics and treatment planning. However, the reliability of AI models is highly dependent on the quality of the training data, especially in medical imaging, where varying patient data and evolving medical knowledge pose a challenge to the accuracy and generalizability of given datasets. Results: The proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability. Through an automated indexing process, CT image series are semantically enhanced using the TotalSegmentator framework for segmentation and resulting SNOMED CT annotations. The metadata is standardized with HL7 FHIR resources to enable efficient data recognition and data exchange between research projects. Conclusions: The study successfully integrates a robust process within the UKSH MeDIC, leading to the semantic enrichment of over 230,000 CT image series and over 8 million SNOMED CT annotations. The standardized representation using HL7 FHIR resources improves discoverability and facilitates interoperability, providing a foundation for the FAIRness of medical imaging data. However, developing automated annotation methods that can keep pace with growing clinical datasets remains a challenge to ensure continued progress in large-scale integration and indexing of medical imaging for advanced healthcare AI applications.
Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs
Authors: Debnath Kundu, Atharva Mehta, Rajesh Kumar, Naman Lal, Avinash Anand, Apoorv Singh, Rajiv Ratn Shah
Date: 2024-06-21 00:00:00

The transition to online examinations and assignments raises significant concerns about academic integrity. Traditional plagiarism detection systems often struggle to identify instances of intelligent cheating, particularly when students utilize advanced generative AI tools to craft their responses. This study proposes a keystroke dynamics-based method to differentiate between bona fide and assisted writing within academic contexts. To facilitate this, a dataset was developed to capture the keystroke patterns of individuals engaged in writing tasks, both with and without the assistance of generative AI. The detector, trained using a modified TypeNet architecture, achieved accuracies ranging from 74.98% to 85.72% in condition-specific scenarios and from 52.24% to 80.54% in condition-agnostic scenarios. The findings highlight significant differences in keystroke dynamics between genuine and assisted writing. The outcomes of this study enhance our understanding of how users interact with generative AI and have implications for improving the reliability of digital educational platforms.
GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation
Authors: Chubin Zhang, Hongliang Song, Yi Wei, Yu Chen, Jiwen Lu, Yansong Tang
Date: 2024-06-21 00:00:00
Tasks: 3D Generation, GPU

In this work, we introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an approach which can predict high-quality assets with 512k Gaussians and 21 input images in only 11 GB GPU memory. Previous works neglect the inherent sparsity of 3D structure and do not utilize explicit geometric relationships between 3D and 2D images. This limits these methods to a low-resolution representation and makes it difficult to scale up to the dense views for better quality. GeoLRM tackles these issues by incorporating a novel 3D-aware transformer structure that directly processes 3D points and uses deformable cross-attention mechanisms to effectively integrate image features into 3D representations. We implement this solution through a two-stage pipeline: initially, a lightweight proposal network generates a sparse set of 3D anchor points from the posed image inputs; subsequently, a specialized reconstruction transformer refines the geometry and retrieves textural details. Extensive experimental results demonstrate that GeoLRM significantly outperforms existing models, especially for dense view inputs. We also demonstrate the practical applicability of our model with 3D generation tasks, showcasing its versatility and potential for broader adoption in real-world applications. The project page: https://linshan-bin.github.io/GeoLRM/.
Masked Extended Attention for Zero-Shot Virtual Try-On In The Wild
Authors: Nadav Orzech, Yotam Nitzan, Ulysse Mizrahi, Dov Danon, Amit H. Bermano
Date: 2024-06-21 00:00:00
Tasks: Virtual Try-on

Virtual Try-On (VTON) is a highly active line of research, with increasing demand. It aims to replace a piece of garment in an image with one from another, while preserving person and garment characteristics as well as image fidelity. Current literature takes a supervised approach for the task, impairing generalization and imposing heavy computation. In this paper, we present a novel zero-shot training-free method for inpainting a clothing garment by reference. Our approach employs the prior of a diffusion model with no additional training, fully leveraging its native generalization capabilities. The method employs extended attention to transfer image information from reference to target images, overcoming two significant challenges. We first initially warp the reference garment over the target human using deep features, alleviating "texture sticking". We then leverage the extended attention mechanism with careful masking, eliminating leakage of reference background and unwanted influence. Through a user study, qualitative, and quantitative comparison to state-of-the-art approaches, we demonstrate superior image quality and garment preservation compared unseen clothing pieces or human figures.
Rethinking Remote Sensing Change Detection With A Mask View
Authors: Xiaowen Ma, Zhenkai Wu, Rongrong Lian, Wei zhang, Siyang Song
Date: 2024-06-21 00:00:00
Tasks: Change Detection, Decoder

Remote sensing change detection aims to compare two or more images recorded for the same area but taken at different time stamps to quantitatively and qualitatively assess changes in geographical entities and environmental factors. Mainstream models usually built on pixel-by-pixel change detection paradigms, which cannot tolerate the diversity of changes due to complex scenes and variation in imaging conditions. To address this shortcoming, this paper rethinks the change detection with the mask view, and further proposes the corresponding: 1) meta-architecture CDMask and 2) instance network CDMaskFormer. Components of CDMask include Siamese backbone, change extractor, pixel decoder, transformer decoder and normalized detector, which ensures the proper functioning of the mask detection paradigm. Since the change query can be adaptively updated based on the bi-temporal feature content, the proposed CDMask can adapt to different latent data distributions, thus accurately identifying regions of interest changes in complex scenarios. Consequently, we further propose the instance network CDMaskFormer customized for the change detection task, which includes: (i) a Spatial-temporal convolutional attention-based instantiated change extractor to capture spatio-temporal context simultaneously with lightweight operations; and (ii) a scene-guided axial attention-instantiated transformer decoder to extract more spatial details. State-of-the-art performance of CDMaskFormer is achieved on five benchmark datasets with a satisfactory efficiency-accuracy trade-off. Code is available at https://github.com/xwmaxwma/rschange.
Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network
Authors: Zesheng Liu, Maryam Rahnemoonfar
Date: 2024-06-21 00:00:00
Tasks: Graph Neural Network

Learning spatio-temporal patterns of polar ice layers is crucial for monitoring the change in ice sheet balance and evaluating ice dynamic processes. While a few researchers focus on learning ice layer patterns from echogram images captured by airborne snow radar sensors via different convolutional neural networks, the noise in the echogram images proves to be a major obstacle. Instead, we focus on geometric deep learning based on graph neural networks to learn the spatio-temporal patterns from thickness information of shallow ice layers and make predictions for deep layers. In this paper, we propose a physics-informed hybrid graph neural network that combines the GraphSAGE framework for graph feature learning with the long short-term memory (LSTM) structure for learning temporal changes, and introduce measurements of physical ice properties from Model Atmospheric Regional (MAR) weather model as physical node features. We found that our proposed network can consistently outperform the current non-inductive or non-physical model in predicting deep ice layer thickness.
You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation
Authors: Hongyu Chen, Weiming Zeng, Luhui Cai, Lei Wang, Jia Lu, Yueyang Li, Hongjie Yan, Wai Ting Siok, Nizhuan Wang
Date: 2024-06-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

High-precision acquisition of dense-channel electroencephalogram (EEG) signals is often impeded by the costliness and lack of portability of equipment. In contrast, generating dense-channel EEG signals effectively from sparse channels shows promise and economic viability. However, sparse-channel EEG poses challenges such as reduced spatial resolution, information loss, signal mixing, and heightened susceptibility to noise and interference. To address these challenges, we first theoretically formulate the dense-channel EEG generation problem as by optimizing a set of cross-channel EEG signal generation problems. Then, we propose the YOAS framework for generating dense-channel data from sparse-channel EEG signals. The YOAS totally consists of four sequential stages: Data Preparation, Data Preprocessing, Biased-EEG Generation, and Synthetic EEG Generation. Data Preparation and Preprocessing carefully consider the distribution of EEG electrodes and low signal-to-noise ratio problem of EEG signals. Biased-EEG Generation includes sub-modules of BiasEEGGanFormer and BiasEEGDiffFormer, which facilitate long-term feature extraction with attention and generate signals by combining electrode position alignment with diffusion model, respectively. Synthetic EEG Generation synthesizes the final signals, employing a deduction paradigm for multi-channel EEG generation. Extensive experiments confirmed YOAS's feasibility, efficiency, and theoretical validity, even remarkably enhancing data discernibility. This breakthrough in dense-channel EEG signal generation from sparse-channel data opens new avenues for exploration in EEG signal processing and application.
Fingerprint Membership and Identity Inference Against Generative Adversarial Networks
Authors: Saverio Cavasin, Daniele Mari, Simone Milani, Mauro Conti
Date: 2024-06-21 00:00:00
Tasks: Generative Adversarial Network, Inference Attack

Generative models are gaining significant attention as potential catalysts for a novel industrial revolution. Since automated sample generation can be useful to solve privacy and data scarcity issues that usually affect learned biometric models, such technologies became widely spread in this field. In this paper, we assess the vulnerabilities of generative machine learning models concerning identity protection by designing and testing an identity inference attack on fingerprint datasets created by means of a generative adversarial network. Experimental results show that the proposed solution proves to be effective under different configurations and easily extendable to other biometric measurements.
Landscape More Secure Than Portrait? Zooming Into the Directionality of Digital Images With Security Implications
Authors: Benedikt Lorch, Rainer Böhme
Date: 2024-06-21 00:00:00
Tasks: Steganalysis

The orientation in which a source image is captured can affect the resulting security in downstream applications. One reason for this is that many state-of-the-art methods in media security assume that image statistics are similar in the horizontal and vertical directions, allowing them to reduce the number of features (or trainable weights) by merging coefficients. We show that this artificial symmetrization tends to suppress important properties of natural images and common processing operations, causing a loss of performance. We also observe the opposite problem, where unaddressed directionality causes learning-based methods to overfit to a single orientation. These are vulnerable to manipulation if an adversary chooses inputs with the less common orientation. This paper takes a comprehensive approach, identifies and systematizes causes of directionality at several stages of a typical acquisition pipeline, measures their effect, and demonstrates for three selected security applications (steganalysis, forensic source identification, and the detection of synthetic images) how the performance of state-of-the-art methods can be improved by properly accounting for directionality.
DiffExplainer: Unveiling Black Box Models Via Counterfactual Generation
Authors: Yingying Fang, Shuang Wu, Zihao Jin, Caiwen Xu, Shiyi Wang, Simon Walsh, Guang Yang
Date: 2024-06-21 00:00:00
Tasks: counterfactual, image-classification, Image Classification, Medical Image Classification, Prognosis

In the field of medical imaging, particularly in tasks related to early disease detection and prognosis, understanding the reasoning behind AI model predictions is imperative for assessing their reliability. Conventional explanation methods encounter challenges in identifying decisive features in medical image classifications, especially when discriminative features are subtle or not immediately evident. To address this limitation, we propose an agent model capable of generating counterfactual images that prompt different decisions when plugged into a black box model. By employing this agent model, we can uncover influential image patterns that impact the black model's final predictions. Through our methodology, we efficiently identify features that influence decisions of the deep black box. We validated our approach in the rigorous domain of medical prognosis tasks, showcasing its efficacy and potential to enhance the reliability of deep learning models in medical image classification compared to existing interpretation methods. The code will be publicly available at https://github.com/ayanglab/DiffExplainer.