pwc-archive/papers-with-abstracts huggingface dataset:
Backdoor Attacks against Image-to-Image Networks
Authors: Wenbo Jiang, Hongwei Li, Jiaming He, Rui Zhang, Guowen Xu, Tianwei Zhang, Rongxing Lu
Date: 2024-07-15 00:00:00
Tasks: Backdoor Attack, Denoising, image-classification, Image Classification, Image Super-Resolution, Multi-Task Learning, object-detection, Object Detection, Super-Resolution

Recently, deep learning-based Image-to-Image (I2I) networks have become the predominant choice for I2I tasks such as image super-resolution and denoising. Despite their remarkable performance, the backdoor vulnerability of I2I networks has not been explored. To fill this research gap, we conduct a comprehensive investigation on the susceptibility of I2I networks to backdoor attacks. Specifically, we propose a novel backdoor attack technique, where the compromised I2I network behaves normally on clean input images, yet outputs a predefined image of the adversary for malicious input images containing the trigger. To achieve this I2I backdoor attack, we propose a targeted universal adversarial perturbation (UAP) generation algorithm for I2I networks, where the generated UAP is used as the backdoor trigger. Additionally, in the backdoor training process that contains the main task and the backdoor task, multi-task learning (MTL) with dynamic weighting methods is employed to accelerate convergence rates. In addition to attacking I2I tasks, we extend our I2I backdoor to attack downstream tasks, including image classification and object detection. Extensive experiments demonstrate the effectiveness of the I2I backdoor on state-of-the-art I2I network architectures, as well as the robustness against different mainstream backdoor defenses.
Enhancing Building Safety Design for Active Shooter Incidents: Exploration of Building Exit Parameters using Reinforcement Learning-Based Simulations
Authors: Ruying Liu, Wanjing Wu, Burcin Becerik-Gerber, Gale M. Lucas
Date: 2024-07-15 00:00:00

With the alarming rise in active shooter incidents (ASIs) in the United States, enhancing public safety through building design has become a pressing need. This study proposes a reinforcement learning-based simulation approach addressing gaps in existing research that has neglected the dynamic behaviours of shooters. We developed an autonomous agent to simulate an active shooter within a realistic office environment, aiming to offer insights into the interactions between building design parameters and ASI outcomes. A case study is conducted to quantitatively investigate the impact of building exit numbers (total count of accessible exits) and configuration (arrangement of which exits are available or not) on evacuation and harm rates. Findings demonstrate that greater exit availability significantly improves evacuation outcomes and reduces harm. Exits nearer to the shooter's initial position hold greater importance for accessibility than those farther away. By encompassing dynamic shooter behaviours, this study offers preliminary insights into effective building safety design against evolving threats.
A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT
Authors: Chunshi Wang, Bin Zhao, Shuxue Ding
Date: 2024-07-15 00:00:00
Tasks: Segmentation, STS

Cone beam computed tomography (CBCT) is a common way of diagnosing dental related diseases. Accurate segmentation of 3D tooth is of importance for the treatment. Although deep learning based methods have achieved convincing results in medical image processing, they need a large of annotated data for network training, making it very time-consuming in data collection and annotation. Besides, domain shift widely existing in the distribution of data acquired by different devices impacts severely the model generalization. To resolve the problem, we propose a multi-stage framework for 3D tooth segmentation in dental CBCT, which achieves the third place in the "Semi-supervised Teeth Segmentation" 3D (STS-3D) challenge. The experiments on validation set compared with other semi-supervised segmentation methods further indicate the validity of our approach.
Expanding the Scope: Inductive Knowledge Graph Reasoning with Multi-Starting Progressive Propagation
Authors: Zhoutian Shao, Yuanning Cui, Wei Hu
Date: 2024-07-15 00:00:00
Tasks: Knowledge Graphs

Knowledge graphs (KGs) are widely acknowledged as incomplete, and new entities are constantly emerging in the real world. Inductive KG reasoning aims to predict missing facts for these new entities. Among existing models, graph neural networks (GNNs) based ones have shown promising performance for this task. However, they are still challenged by inefficient message propagation due to the distance and scalability issues. In this paper, we propose a new inductive KG reasoning model, MStar, by leveraging conditional message passing neural networks (C-MPNNs). Our key insight is to select multiple query-specific starting entities to expand the scope of progressive propagation. To propagate query-related messages to a farther area within limited steps, we subsequently design a highway layer to propagate information toward these selected starting entities. Moreover, we introduce a training strategy called LinkVerify to mitigate the impact of noisy training samples. Experimental results validate that MStar achieves superior performance compared with state-of-the-art models, especially for distant entities.
Learning Rapid Turning, Aerial Reorientation, and Balancing using Manipulator as a Tail
Authors: Insung Yang, Jemin Hwangbo
Date: 2024-07-15 00:00:00

In this research, we investigated the innovative use of a manipulator as a tail in quadruped robots to augment their physical capabilities. Previous studies have primarily focused on enhancing various abilities by attaching robotic tails that function solely as tails on quadruped robots. While these tails improve the performance of the robots, they come with several disadvantages, such as increased overall weight and higher costs. To mitigate these limitations, we propose the use of a 6-DoF manipulator as a tail, allowing it to serve both as a tail and as a manipulator. To control this highly complex robot, we developed a controller based on reinforcement learning for the robot equipped with the manipulator. Our experimental results demonstrate that robots equipped with a manipulator outperform those without a manipulator in tasks such as rapid turning, aerial reorientation, and balancing. These results indicate that the manipulator can improve the agility and stability of quadruped robots, similar to a tail, in addition to its manipulation capabilities.
Melon Fruit Detection and Quality Assessment Using Generative AI-Based Image Data Augmentation
Authors: Seungri Yoon, Yunseong Cho, Tae In Ahn
Date: 2024-07-15 00:00:00
Tasks: Data Augmentation, SSIM

Monitoring and managing the growth and quality of fruits are very important tasks. To effectively train deep learning models like YOLO for real-time fruit detection, high-quality image datasets are essential. However, such datasets are often lacking in agriculture. Generative AI models can help create high-quality images. In this study, we used MidJourney and Firefly tools to generate images of melon greenhouses and post-harvest fruits through text-to-image, pre-harvest image-to-image, and post-harvest image-to-image methods. We evaluated these AIgenerated images using PSNR and SSIM metrics and tested the detection performance of the YOLOv9 model. We also assessed the net quality of real and generated fruits. Our results showed that generative AI could produce images very similar to real ones, especially for post-harvest fruits. The YOLOv9 model detected the generated images well, and the net quality was also measurable. This shows that generative AI can create realistic images useful for fruit detection and quality assessment, indicating its great potential in agriculture. This study highlights the potential of AI-generated images for data augmentation in melon fruit detection and quality assessment and envisions a positive future for generative AI applications in agriculture.
Cooperative Reward Shaping for Multi-Agent Pathfinding
Authors: Zhenyu Song, Ronghao Zheng, Senlin Zhang, Meiqin Liu
Date: 2024-07-15 00:00:00
Tasks: Collision Avoidance, Multi-agent Reinforcement Learning, Q-Learning

The primary objective of Multi-Agent Pathfinding (MAPF) is to plan efficient and conflict-free paths for all agents. Traditional multi-agent path planning algorithms struggle to achieve efficient distributed path planning for multiple agents. In contrast, Multi-Agent Reinforcement Learning (MARL) has been demonstrated as an effective approach to achieve this objective. By modeling the MAPF problem as a MARL problem, agents can achieve efficient path planning and collision avoidance through distributed strategies under partial observation. However, MARL strategies often lack cooperation among agents due to the absence of global information, which subsequently leads to reduced MAPF efficiency. To address this challenge, this letter introduces a unique reward shaping technique based on Independent Q-Learning (IQL). The aim of this method is to evaluate the influence of one agent on its neighbors and integrate such an interaction into the reward function, leading to active cooperation among agents. This reward shaping method facilitates cooperation among agents while operating in a distributed manner. The proposed approach has been evaluated through experiments across various scenarios with different scales and agent counts. The results are compared with those from other state-of-the-art (SOTA) planners. The evidence suggests that the approach proposed in this letter parallels other planners in numerous aspects, and outperforms them in scenarios featuring a large number of agents.
Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity
Authors: Santiago Pascual, Chunghsin Yeh, Ioannis Tsiamas, Joan Serrà
Date: 2024-07-15 00:00:00
Tasks: Video-to-Sound Generation

Video-to-audio (V2A) generation leverages visual-only video features to render plausible sounds that match the scene. Importantly, the generated sound onsets should match the visual actions that are aligned with them, otherwise unnatural synchronization artifacts arise. Recent works have explored the progression of conditioning sound generators on still images and then video features, focusing on quality and semantic matching while ignoring synchronization, or by sacrificing some amount of quality to focus on improving synchronization only. In this work, we propose a V2A generative model, named MaskVAT, that interconnects a full-band high-quality general audio codec with a sequence-to-sequence masked generative model. This combination allows modeling both high audio quality, semantic matching, and temporal synchronicity at the same time. Our results show that, by combining a high-quality codec with the proper pre-trained audio-visual features and a sequence-to-sequence parallel structure, we are able to yield highly synchronized results on one hand, whilst being competitive with the state of the art of non-codec generative audio models. Sample videos and generated audios are available at https://maskvat.github.io .
By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting
Authors: Hyungjun Yoon, Biniyam Aschalew Tolera, Taesik Gong, Kimin Lee, Sung-Ju Lee
Date: 2024-07-15 00:00:00
Tasks: Visual Prompting

Large language models (LLMs) have demonstrated exceptional abilities across various domains. However, utilizing LLMs for ubiquitous sensing applications remains challenging as existing text-prompt methods show significant performance degradation when handling long sensor data sequences. We propose a visual prompting approach for sensor data using multimodal LLMs (MLLMs). We design a visual prompt that directs MLLMs to utilize visualized sensor data alongside the target sensory task descriptions. Additionally, we introduce a visualization generator that automates the creation of optimal visualizations tailored to a given sensory task, eliminating the need for prior task-specific knowledge. We evaluated our approach on nine sensory tasks involving four sensing modalities, achieving an average of 10% higher accuracy than text-based prompts and reducing token costs by 15.8 times. Our findings highlight the effectiveness and cost-efficiency of visual prompts with MLLMs for various sensory tasks. The source code is available at https://github.com/diamond264/ByMyEyes.
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks
Authors: Zirui Xu, Sandilya Sai Garimella, Vasileios Tzoumas
Date: 2024-07-15 00:00:00
Tasks: Decision Making, Distributed Optimization, Motion Planning, RAG

We provide a communication- and computation-efficient method for distributed submodular optimization in robot mesh networks. Submodularity is a property of diminishing returns that arises in active information gathering such as mapping, surveillance, and target tracking. Our method, Resource-Aware distributed Greedy (RAG), introduces a new distributed optimization paradigm that enables scalable and near-optimal action coordination. To this end, RAG requires each robot to make decisions based only on information received from and about their neighbors. In contrast, the current paradigms allow the relay of information about all robots across the network. As a result, RAG's decision-time scales linearly with the network size, while state-of-the-art near-optimal submodular optimization algorithms scale cubically. We also characterize how the designed mesh-network topology affects RAG's approximation performance. Our analysis implies that sparser networks favor scalability without proportionally compromising approximation performance: while RAG's decision time scales linearly with network size, the gain in approximation performance scales sublinearly. We demonstrate RAG's performance in simulated scenarios of area detection with up to 45 robots, simulating realistic robot-to-robot (r2r) communication speeds such as the 0.25 Mbps speed of the Digi XBee 3 Zigbee 3.0. In the simulations, RAG enables real-time planning, up to three orders of magnitude faster than competitive near-optimal algorithms, while also achieving superior mean coverage performance. To enable the simulations, we extend the high-fidelity and photo-realistic simulator AirSim by integrating a scalable collaborative autonomy pipeline to tens of robots and simulating r2r communication delays. Our code is available at https://github.com/UM-iRaL/Resource-Aware-Coordination-AirSim.
NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models
Authors: Pranshu Pandya, Vatsal Gupta, Agney S Talwarr, Tushar Kataria, Dan Roth, Vivek Gupta
Date: 2024-07-15 00:00:00
Tasks: Common Sense Reasoning, Multiple-choice, Visual Reasoning

Cognitive textual and visual reasoning tasks, including puzzles, series, and analogies, demand the ability to quickly reason, decipher, and evaluate patterns both textually and spatially. Due to extensive training on vast amounts of human-curated data, LLMs and VLMs excel in common-sense reasoning tasks, however still struggle with more complex reasoning that demands deeper cognitive understanding. We introduce NTSEBench, a new dataset designed to evaluate cognitive multi-modal reasoning and problem-solving skills of large models. The dataset contains 2728 multiple-choice questions, accompanied by a total of 4,642 images, categorized into 26 different types. These questions are drawn from the nationwide NTSE examination in India and feature a mix of visual and textual general aptitude challenges, designed to assess intelligence and critical thinking skills beyond mere rote learning. We establish baselines on the dataset using state-of-the-art LLMs and VLMs. To facilitate a comparison between open source and propriety models, we propose four distinct modeling strategies to handle different modalities -- text and images -- in the dataset instances.
Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI Datasets
Authors: Linxuan Han, Sa Xiao, Zimeng Li, Haidong Li, Xiuchao Zhao, Yeqing Han, Fumin Guo, Xin Zhou
Date: 2024-07-15 00:00:00
Tasks: MRI segmentation, Self-Supervised Learning

Multi-modal magnetic resonance imaging (MRI) provides information of lesions for computer-aided diagnosis from different views. Deep learning algorithms are suitable for identifying specific anatomical structures, segmenting lesions, and classifying diseases. Manual labels are limited due to the high expense, which hinders further improvement of accuracy. Self-supervised learning, particularly masked image modeling (MIM), has shown promise in utilizing unlabeled data. However, we spot model collapse when applying MIM to multi-modal MRI datasets. The performance of downstream tasks does not see any improvement following the collapsed model. To solve model collapse, we analyze and address it in two types: complete collapse and dimensional collapse. We find complete collapse occurs because the collapsed loss value in multi-modal MRI datasets falls below the normally converged loss value. Based on this, the hybrid mask pattern (HMP) masking strategy is introduced to elevate the collapsed loss above the normally converged loss value and avoid complete collapse. Additionally, we reveal that dimensional collapse stems from insufficient feature uniformity in MIM. We mitigate dimensional collapse by introducing the pyramid barlow twins (PBT) module as an explicit regularization method. Overall, we construct the enhanced MIM (E-MIM) with HMP and PBT module to avoid model collapse multi-modal MRI. Experiments are conducted on three multi-modal MRI datasets to validate the effectiveness of our approach in preventing both types of model collapse. By preventing model collapse, the training of the model becomes more stable, resulting in a decent improvement in performance for segmentation and classification tasks. The code is available at https://github.com/LinxuanHan/E-MIM.
An Empirical Study of Mamba-based Pedestrian Attribute Recognition
Authors: Xiao Wang, Weizhe Kong, Jiandong Jin, Shiao Wang, Ruichong Gao, Qingchuan Ma, Chenglong Li, Jin Tang
Date: 2024-07-15 00:00:00
Tasks: Articles, Attribute, Mamba, Pedestrian Attribute Recognition

Current strong pedestrian attribute recognition models are developed based on Transformer networks, which are computationally heavy. Recently proposed models with linear complexity (e.g., Mamba) have garnered significant attention and have achieved a good balance between accuracy and computational cost across a variety of visual tasks. Relevant review articles also suggest that while these models can perform well on some pedestrian attribute recognition datasets, they are generally weaker than the corresponding Transformer models. To further tap into the potential of the novel Mamba architecture for PAR tasks, this paper designs and adapts Mamba into two typical PAR frameworks, i.e., the text-image fusion approach and pure vision Mamba multi-label recognition framework. It is found that interacting with attribute tags as additional input does not always lead to an improvement, specifically, Vim can be enhanced, but VMamba cannot. This paper further designs various hybrid Mamba-Transformer variants and conducts thorough experimental validations. These experimental results indicate that simply enhancing Mamba with a Transformer does not always lead to performance improvements but yields better results under certain settings. We hope this empirical study can further inspire research in Mamba for PAR, and even extend into the domain of multi-label recognition, through the design of these network structures and comprehensive experimentation. The source code of this work will be released at \url{https://github.com/Event-AHU/OpenPAR}
Mutual Learning for Acoustic Matching and Dereverberation via Visual Scene-driven Diffusion
Authors: Jian Ma, Wenguan Wang, Yi Yang, Feng Zheng
Date: 2024-07-15 00:00:00

Visual acoustic matching (VAM) is pivotal for enhancing the immersive experience, and the task of dereverberation is effective in improving audio intelligibility. Existing methods treat each task independently, overlooking the inherent reciprocity between them. Moreover, these methods depend on paired training data, which is challenging to acquire, impeding the utilization of extensive unpaired data. In this paper, we introduce MVSD, a mutual learning framework based on diffusion models. MVSD considers the two tasks symmetrically, exploiting the reciprocal relationship to facilitate learning from inverse tasks and overcome data scarcity. Furthermore, we employ the diffusion model as foundational conditional converters to circumvent the training instability and over-smoothing drawbacks of conventional GAN architectures. Specifically, MVSD employs two converters: one for VAM called reverberator and one for dereverberation called dereverberator. The dereverberator judges whether the reverberation audio generated by reverberator sounds like being in the conditional visual scenario, and vice versa. By forming a closed loop, these two converters can generate informative feedback signals to optimize the inverse tasks, even with easily acquired one-way unpaired data. Extensive experiments on two standard benchmarks, i.e., SoundSpaces-Speech and Acoustic AVSpeech, exhibit that our framework can improve the performance of the reverberator and dereverberator and better match specified visual scenarios.
Accessing Vision Foundation Models at ImageNet-level Costs
Authors: Yitian Zhang, Xu Ma, Yue Bai, Huan Wang, Yun Fu
Date: 2024-07-15 00:00:00
Tasks: Knowledge Distillation, Transfer Learning

Vision foundation models are renowned for their generalization ability due to massive training data. Nevertheless, they demand tremendous training resources, and the training data is often inaccessible, e.g., CLIP, DINOv2, posing great challenges to developing derivatives that could advance research in this field. In this work, we offer a very simple and general solution, named Proteus, to distill foundation models into smaller equivalents on ImageNet-1K without access to the original training data. Specifically, we remove the designs from conventional knowledge distillation settings that result in dataset bias and present three levels of training objectives, i.e., token, patch, and feature, to maximize the efficacy of knowledge transfer. In this manner, Proteus is trained at ImageNet-level costs with surprising ability, facilitating the accessibility of training foundation models for the broader research community. Leveraging DINOv2-g/14 as the teacher, Proteus-L/14 matches the performance of the Oracle method DINOv2-L/14 (142M training data) across 15 benchmarks and outperforms other vision foundation models including CLIP-L/14 (400M), OpenCLIP-L/14 (400M/2B) and SynCLR-L/14 (600M).