pwc-archive/papers-with-abstracts huggingface dataset:
The Relevance of Item-Co-Exposure For Exposure Bias Mitigation
Authors: Thorsten Krause, Alina Deriyeva, Jan Heinrich Beinke, Gerrit York Bartels, Oliver Thomas
Date: 2024-09-19 00:00:00
Tasks: Discrete Choice Models, Recommendation Systems

Through exposing items to users, implicit feedback recommender systems influence the logged interactions, and, ultimately, their own recommendations. This effect is called exposure bias and it can lead to issues such as filter bubbles and echo chambers. Previous research employed the multinomial logit model (MNL) with exposure information to reduce exposure bias on synthetic data. This extended abstract summarizes our previous study in which we investigated whether (i) these findings hold for human-generated choices, (ii) other discrete choice models mitigate bias better, and (iii) an item's estimated relevance can depend on the relevances of the other items that were presented with it. We collected a data set of biased and unbiased choices in a controlled online user study and measured the effects of overexposure and competition. We found that (i) the discrete choice models effectively mitigated exposure bias on human-generated choice data, (ii) there were no significant differences in robustness among the different discrete choice models, and (iii) only multivariate discrete choice models were robust to competition between items. We conclude that discrete choice models mitigate exposure bias effectively because they consider item-co-exposure. Moreover, exposing items alongside more or less popular items can bias future recommendations significantly and item exposure must be tracked for overcoming exposure bias. We consider our work vital for understanding what exposure bias it, how it forms, and how it can be mitigated.
A Simple Model to Estimate Sharing Effects in Social Networks
Authors: Olivier Jeunen
Date: 2024-09-16 00:00:00

Randomised Controlled Trials (RCTs) are the gold standard for estimating treatment effects across many fields of science. Technology companies have adopted A/B-testing methods as a modern RCT counterpart, where end-users are randomly assigned various system variants and user behaviour is tracked continuously. The objective is then to estimate the causal effect that the treatment variant would have on certain metrics of interest to the business. When the outcomes for randomisation units -- end-users in this case -- are not statistically independent, this obfuscates identifiability of treatment effects, and harms decision-makers' observability of the system. Social networks exemplify this, as they are designed to promote inter-user interactions. This interference by design notoriously complicates measurement of, e.g., the effects of sharing. In this work, we propose a simple Markov Decision Process (MDP)-based model describing user sharing behaviour in social networks. We derive an unbiased estimator for treatment effects under this model, and demonstrate through reproducible synthetic experiments that it outperforms existing methods by a significant margin.
Multi-View Adaptive Contrastive Learning for Information Retrieval Based Fault Localization
Authors: Chunying Zhou, Xiaoyuan Xie, Gong Chen, Peng He, Bing Li
Date: 2024-09-19 00:00:00
Tasks: Contrastive Learning, Fault localization, Graph Neural Network, Information Retrieval, Retrieval

Most studies focused on information retrieval-based techniques for fault localization, which built representations for bug reports and source code files and matched their semantic vectors through similarity measurement. However, such approaches often ignore some useful information that might help improve localization performance, such as 1) the interaction relationship between bug reports and source code files; 2) the similarity relationship between bug reports; and 3) the co-citation relationship between source code files. In this paper, we propose a novel approach named Multi-View Adaptive Contrastive Learning for Information Retrieval Fault Localization (MACL-IRFL) to learn the above-mentioned relationships for software fault localization. Specifically, we first generate data augmentations from report-code interaction view, report-report similarity view and code-code co-citation view separately, and adopt graph neural network to aggregate the information of bug reports or source code files from the three views in the embedding process. Moreover, we perform contrastive learning across these views. Our design of contrastive learning task will force the bug report representations to encode information shared by report-report and report-code views,and the source code file representations shared by code-code and report-code views, thereby alleviating the noise from auxiliary information. Finally, to evaluate the performance of our approach, we conduct extensive experiments on five open-source Java projects. The results show that our model can improve over the best baseline up to 28.93%, 25.57% and 20.35% on Accuracy@1, MAP and MRR, respectively.
ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video
Authors: Han Ling, Yinghui Sun, Quansen Sun, yuhui Zheng
Date: 2024-09-16 00:00:00
Tasks: Autonomous Driving, motion prediction, Optical Flow Estimation, Scene Flow Estimation, Zero-shot Generalization

Perceiving and understanding 3D motion is a core technology in fields such as autonomous driving, robots, and motion prediction. This paper proposes a 3D motion perception method called ScaleFlow++ that is easy to generalize. With just a pair of RGB images, ScaleFlow++ can robustly estimate optical flow and motion-in-depth (MID). Most existing methods directly regress MID from two RGB frames or optical flow, resulting in inaccurate and unstable results. Our key insight is cross-scale matching, which extracts deep motion clues by matching objects in pairs of images at different scales. Unlike previous methods, ScaleFlow++ integrates optical flow and MID estimation into a unified architecture, estimating optical flow and MID end-to-end based on feature matching. Moreover, we also proposed modules such as global initialization network, global iterative optimizer, and hybrid training pipeline to integrate global motion information, reduce the number of iterations, and prevent overfitting during training. On KITTI, ScaleFlow++ achieved the best monocular scene flow estimation performance, reducing SF-all from 6.21 to 5.79. The evaluation of MID even surpasses RGBD-based methods. In addition, ScaleFlow++ has achieved stunning zero-shot generalization performance in both rigid and nonrigid scenes. Code is available at \url{https://github.com/HanLingsgjk/CSCV}.
WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild
Authors: Rolandos Alexandros Potamias, Jinglei Zhang, Jiankang Deng, Stefanos Zafeiriou
Date: 2024-09-18 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Hand Pose Estimation, Hand Detection, Hand Pose Estimation, Pose Estimation

In recent years, 3D hand pose estimation methods have garnered significant attention due to their extensive applications in human-computer interaction, virtual reality, and robotics. In contrast, there has been a notable gap in hand detection pipelines, posing significant challenges in constructing effective real-world multi-hand reconstruction systems. In this work, we present a data-driven pipeline for efficient multi-hand reconstruction in the wild. The proposed pipeline is composed of two components: a real-time fully convolutional hand localization and a high-fidelity transformer-based 3D hand reconstruction model. To tackle the limitations of previous methods and build a robust and stable detection network, we introduce a large-scale dataset with over than 2M in-the-wild hand images with diverse lighting, illumination, and occlusion conditions. Our approach outperforms previous methods in both efficiency and accuracy on popular 2D and 3D benchmarks. Finally, we showcase the effectiveness of our pipeline to achieve smooth 3D hand tracking from monocular videos, without utilizing any temporal components. Code, models, and dataset are available https://rolpotamias.github.io/WiLoR.
Self-Supervised Pre-training Tasks for an fMRI Time-series Transformer in Autism Detection
Authors: Yinchi Zhou, Peiyu Duan, Yuexi Du, Nicha C. Dvornek
Date: 2024-09-18 00:00:00
Tasks: Autism detection, Functional Connectivity, Time Series

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that encompasses a wide variety of symptoms and degrees of impairment, which makes the diagnosis and treatment challenging. Functional magnetic resonance imaging (fMRI) has been extensively used to study brain activity in ASD, and machine learning methods have been applied to analyze resting state fMRI (rs-fMRI) data. However, fewer studies have explored the recent transformer-based models on rs-fMRI data. Given the superiority of transformer models in capturing long-range dependencies in sequence data, we have developed a transformer-based self-supervised framework that directly analyzes time-series fMRI data without computing functional connectivity. To address over-fitting in small datasets and enhance the model performance, we propose self-supervised pre-training tasks to reconstruct the randomly masked fMRI time-series data, investigating the effects of various masking strategies. We then finetune the model for the ASD classification task and evaluate it using two public datasets and five-fold cross-validation with different amounts of training data. The experiments show that randomly masking entire ROIs gives better model performance than randomly masking time points in the pre-training step, resulting in an average improvement of 10.8% for AUC and 9.3% for subject accuracy compared with the transformer model trained from scratch across different levels of training data availability. Our code is available on GitHub.
A large-scale study of performance and equity of commercial remote identity verification technologies across demographics
Authors: Kaniz Fatima, Michael Schuckers, Gerardo Cruz-Ortiz, Daqing Hou, Sandip Purnapatra, Tiffany Andrews, Ambuj Neupane, Brandeis Marshall, Stephanie Schuckers
Date: 2024-09-18 00:00:00

As more types of transactions move online, there is an increasing need to verify someone's identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver's license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was inequitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies.
Large Language Models are Strong Audio-Visual Speech Recognition Learners
Authors: Umberto Cappellazzo, Minsu Kim, Honglie Chen, Pingchuan Ma, Stavros Petridis, Daniele Falavigna, Alessio Brutti, Maja Pantic
Date: 2024-09-18 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Visual Speech Recognition

Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. For example, in the audio and speech domains, an LLM can be equipped with (automatic) speech recognition (ASR) abilities by just concatenating the audio tokens, computed with an audio encoder, and the text tokens to achieve state-of-the-art results. On the contrary, tasks like visual and audio-visual speech recognition (VSR/AVSR), which also exploit noise-invariant lip movement information, have received little or no attention. To bridge this gap, we propose Llama-AVSR, a new MLLM with strong audio-visual speech recognition capabilities. It leverages pre-trained audio and video encoders to produce modality-specific tokens which, together with the text tokens, are processed by a pre-trained LLM (e.g., Llama3.1-8B) to yield the resulting response in an auto-regressive fashion. Llama-AVSR requires a small number of trainable parameters as only modality-specific projectors and LoRA modules are trained whereas the multi-modal encoders and LLM are kept frozen. We evaluate our proposed approach on LRS3, the largest public AVSR benchmark, and we achieve new state-of-the-art results for the tasks of ASR and AVSR with a WER of 0.79% and 0.77%, respectively. To bolster our results, we investigate the key factors that underpin the effectiveness of Llama-AVSR: the choice of the pre-trained encoders and LLM, the efficient integration of LoRA modules, and the optimal performance-efficiency trade-off obtained via modality-aware compression rates.
Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus
Authors: Jinchang Zhang, Ningning Xu, Hao Zhang, Guoyu Lu
Date: 2024-09-18 00:00:00
Tasks: 3D geometry, Depth Estimation, Monocular Depth Estimation, Stereo Depth Estimation

Depth estimation is a fundamental task in 3D geometry. While stereo depth estimation can be achieved through triangulation methods, it is not as straightforward for monocular methods, which require the integration of global and local information. The Depth from Defocus (DFD) method utilizes camera lens models and parameters to recover depth information from blurred images and has been proven to perform well. However, these methods rely on All-In-Focus (AIF) images for depth estimation, which is nearly impossible to obtain in real-world applications. To address this issue, we propose a self-supervised framework based on 3D Gaussian splatting and Siamese networks. By learning the blur levels at different focal distances of the same scene in the focal stack, the framework predicts the defocus map and Circle of Confusion (CoC) from a single defocused image, using the defocus map as input to DepthNet for monocular depth estimation. The 3D Gaussian splatting model renders defocused images using the predicted CoC, and the differences between these and the real defocused images provide additional supervision signals for the Siamese Defocus self-supervised network. This framework has been validated on both artificially synthesized and real blurred datasets. Subsequent quantitative and visualization experiments demonstrate that our proposed framework is highly effective as a DFD method.
ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learning
Authors: Yi Yang, Lei Zhong, Huiping Zhuang
Date: 2024-09-18 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Exemplar-Free, Incremental Learning

We introduce a novel Recursive Fusion model, dubbed ReFu, designed to integrate point clouds and meshes for exemplar-free 3D Class-Incremental Learning, where the model learns new 3D classes while retaining knowledge of previously learned ones. Unlike existing methods that either rely on storing historical data to mitigate forgetting or focus on single data modalities, ReFu eliminates the need for exemplar storage while utilizing the complementary strengths of both point clouds and meshes. To achieve this, we introduce a recursive method which continuously accumulates knowledge by updating the regularized auto-correlation matrix. Furthermore, we propose a fusion module, featuring a Pointcloud-guided Mesh Attention Layer that learns correlations between the two modalities. This mechanism effectively integrates point cloud and mesh features, leading to more robust and stable continual learning. Experiments across various datasets demonstrate that our proposed framework outperforms existing methods in 3D class-incremental learning. Project Page: https://arlo397.github.io/ReFu/
Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach
Authors: Nastaran Darabi, Dinithi Jayasuriya, Devashri Naik, Theja Tulabandhula, Amit Ranjan Trivedi
Date: 2024-09-19 00:00:00
Tasks: Adversarial Robustness, object-detection, Object Detection

Adversarial attacks exploit vulnerabilities in a model's decision boundaries through small, carefully crafted perturbations that lead to significant mispredictions. In 3D vision, the high dimensionality and sparsity of data greatly expand the attack surface, making 3D vision particularly vulnerable for safety-critical robotics. To enhance 3D vision's adversarial robustness, we propose a training objective that simultaneously minimizes prediction loss and mutual information (MI) under adversarial perturbations to contain the upper bound of misprediction errors. This approach simplifies handling adversarial examples compared to conventional methods, which require explicit searching and training on adversarial samples. However, minimizing prediction loss conflicts with minimizing MI, leading to reduced robustness and catastrophic forgetting. To address this, we integrate curriculum advisors in the training setup that gradually introduce adversarial objectives to balance training and prevent models from being overwhelmed by difficult cases early in the process. The advisors also enhance robustness by encouraging training on diverse MI examples through entropy regularizers. We evaluated our method on ModelNet40 and KITTI using PointNet, DGCNN, SECOND, and PointTransformers, achieving 2-5% accuracy gains on ModelNet40 and a 5-10% mAP improvement in object detection. Our code is publicly available at https://github.com/nstrndrbi/Mine-N-Learn.
A Novel Perspective for Multi-modal Multi-label Skin Lesion Classification
Authors: Yuan Zhang, Yutong Xie, Hu Wang, Jodie C Avery, M Louise Hull, Gustavo Carneiro
Date: 2024-09-19 00:00:00
Tasks: Diagnostic, Lesion Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Skin Lesion Classification

The efficacy of deep learning-based Computer-Aided Diagnosis (CAD) methods for skin diseases relies on analyzing multiple data modalities (i.e., clinical+dermoscopic images, and patient metadata) and addressing the challenges of multi-label classification. Current approaches tend to rely on limited multi-modal techniques and treat the multi-label problem as a multiple multi-class problem, overlooking issues related to imbalanced learning and multi-label correlation. This paper introduces the innovative Skin Lesion Classifier, utilizing a Multi-modal Multi-label TransFormer-based model (SkinM2Former). For multi-modal analysis, we introduce the Tri-Modal Cross-attention Transformer (TMCT) that fuses the three image and metadata modalities at various feature levels of a transformer encoder. For multi-label classification, we introduce a multi-head attention (MHA) module to learn multi-label correlations, complemented by an optimisation that handles multi-label and imbalanced learning problems. SkinM2Former achieves a mean average accuracy of 77.27% and a mean diagnostic accuracy of 77.85% on the public Derm7pt dataset, outperforming state-of-the-art (SOTA) methods.
Frequency-Guided Spatial Adaptation for Camouflaged Object Detection
Authors: Shizhou Zhang, Dexuan Kong, Yinghui Xing, Yue Lu, Lingyan Ran, Guoqiang Liang, Hexu Wang, Yanning Zhang
Date: 2024-09-19 00:00:00
Tasks: Object, object-detection, Object Detection

Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background.With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released.
Infrared Small Target Detection in Satellite Videos: A New Dataset and A Novel Recurrent Feature Refinement Framework
Authors: Xinyi Ying, Li Liu, Zaipin Lin, Yangsi Shi, Yingqian Wang, Ruojing Li, Xu Cao, Boyang Li, Shilin Zhou
Date: 2024-09-19 00:00:00
Tasks: Motion Compensation, Video Generation

Multi-frame infrared small target (MIRST) detection in satellite videos is a long-standing, fundamental yet challenging task for decades, and the challenges can be summarized as: First, extremely small target size, highly complex clutters & noises, various satellite motions result in limited feature representation, high false alarms, and difficult motion analyses. Second, the lack of large-scale public available MIRST dataset in satellite videos greatly hinders the algorithm development. To address the aforementioned challenges, in this paper, we first build a large-scale dataset for MIRST detection in satellite videos (namely IRSatVideo-LEO), and then develop a recurrent feature refinement (RFR) framework as the baseline method. Specifically, IRSatVideo-LEO is a semi-simulated dataset with synthesized satellite motion, target appearance, trajectory and intensity, which can provide a standard toolbox for satellite video generation and a reliable evaluation platform to facilitate the algorithm development. For baseline method, RFR is proposed to be equipped with existing powerful CNN-based methods for long-term temporal dependency exploitation and integrated motion compensation & MIRST detection. Specifically, a pyramid deformable alignment (PDA) module and a temporal-spatial-frequency modulation (TSFM) module are proposed to achieve effective and efficient feature alignment, propagation, aggregation and refinement. Extensive experiments have been conducted to demonstrate the effectiveness and superiority of our scheme. The comparative results show that ResUNet equipped with RFR outperforms the state-of-the-art MIRST detection methods. Dataset and code are released at https://github.com/XinyiYing/RFR.