pwc-archive/papers-with-abstracts huggingface dataset:
HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting
Authors: Shaohan Yu, Pan Deng, Yu Zhao, Junting Liu, Zi'ang Wang
Date: 2024-11-30 00:00:00
Tasks: Fairness, Mixture-of-Experts, Prediction

Achieving both accurate and consistent predictive performance across spatial nodes is crucial for ensuring the validity and reliability of outcomes in fair spatial-temporal forecasting tasks. However, existing training methods treat heterogeneous nodes with a fully averaged perspective, resulting in inherently biased prediction targets. Balancing accuracy and consistency is particularly challenging due to the multi-objective nature of spatial-temporal forecasting. To address this issue, we propose a novel Heterogeneity-Informed Mixture-of-Experts (HiMoE) framework that delivers both uniform and precise spatial-temporal predictions. From a model architecture perspective, we design the Heterogeneity-Informed Graph Convolutional Network (HiGCN) to address trend heterogeneity, and we introduce the Node-wise Mixture-of-Experts (NMoE) module to handle cardinality heterogeneity across nodes. From an evaluation perspective, we propose STFairBench, a benchmark that handles fairness in spatial-temporal prediction from both training and evaluation stages. Extensive experiments on four real-world datasets demonstrate that HiMoE achieves state-of-the-art performance, outperforming the best baseline by at least 9.22% across all evaluation metrics.
Digital Epidemiology: Leveraging Social Media for Insight into Epilepsy and Mental Health
Authors: Liza Dahiya, Rachit Bagga
Date: 2024-12-02 00:00:00
Tasks: Epidemiology

Social media platforms, particularly Reddit's r/Epilepsy community, offer a unique perspective into the experiences of individuals with epilepsy (PWE) and their caregivers. This study analyzes 57k posts and 533k comments to explore key themes across demographics such as age, gender, and relationships. Our findings highlight significant discussions on epilepsy-related challenges, including depression (with 39.75\% of posts indicating severe symptoms), driving restrictions, workplace concerns, and pregnancy-related issues in women with epilepsy. We introduce a novel engagement metric, F(P), which incorporates post length, sentiment scores, and readability to quantify community interaction. This analysis underscores the importance of integrated care addressing both neurological and mental health challenges faced by PWE. The insights from this study inform strategies for targeted support and awareness interventions.
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer
Authors: Jin Lyu, Tianyi Zhu, Yi Gu, Li Lin, Pujin Cheng, Yebin Liu, Xiaoying Tang, Liang An
Date: 2024-12-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Conditional Image Generation, Contrastive Learning, Image Generation

Quantitative analysis of animal behavior and biomechanics requires accurate animal pose and shape estimation across species, and is important for animal welfare and biological research. However, the small network capacity of previous methods and limited multi-species dataset leave this problem underexplored. To this end, this paper presents AniMer to estimate animal pose and shape using family aware Transformer, enhancing the reconstruction accuracy of diverse quadrupedal families. A key insight of AniMer is its integration of a high-capacity Transformer-based backbone and an animal family supervised contrastive learning scheme, unifying the discriminative understanding of various quadrupedal shapes within a single framework. For effective training, we aggregate most available open-sourced quadrupedal datasets, either with 3D or 2D labels. To improve the diversity of 3D labeled data, we introduce CtrlAni3D, a novel large-scale synthetic dataset created through a new diffusion-based conditional image generation pipeline. CtrlAni3D consists of about 10k images with pixel-aligned SMAL labels. In total, we obtain 41.3k annotated images for training and validation. Consequently, the combination of a family aware Transformer network and an expansive dataset enables AniMer to outperform existing methods not only on 3D datasets like Animal3D and CtrlAni3D, but also on out-of-distribution Animal Kingdom dataset. Ablation studies further demonstrate the effectiveness of our network design and CtrlAni3D in enhancing the performance of AniMer for in-the-wild applications. The project page of AniMer is https://luoxue-star.github.io/AniMer_project_page/.
Towards Universal Soccer Video Understanding
Authors: Jiayuan Rao, HaoNing Wu, Hao Jiang, Ya zhang, Yanfeng Wang, Weidi Xie
Date: 2024-12-02 00:00:00
Proceeding: CVPR 2025 1
Tasks: Action Classification, Sports Understanding, Video Understanding

As a globally celebrated sport, soccer has attracted widespread interest from fans all over the world. This paper aims to develop a comprehensive multi-modal framework for soccer video understanding. Specifically, we make the following contributions in this paper: (i) we introduce SoccerReplay-1988, the largest multi-modal soccer dataset to date, featuring videos and detailed annotations from 1,988 complete matches, with an automated annotation pipeline; (ii) we present the first visual-language foundation model in the soccer domain, MatchVision, which leverages spatiotemporal information across soccer videos and excels in various downstream tasks; (iii) we conduct extensive experiments and ablation studies on event classification, commentary generation, and multi-view foul recognition. MatchVision demonstrates state-of-the-art performance on all of them, substantially outperforming existing models, which highlights the superiority of our proposed data and model. We believe that this work will offer a standard paradigm for sports understanding research.
VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval
Authors: Dhiman Paul, Md Rizwan Parvez, Nabeel Mohammed, Shafin Rahman
Date: 2024-12-02 00:00:00
Tasks: Highlight Detection, Moment Retrieval

Video Highlight Detection and Moment Retrieval (HD/MR) are essential in video analysis. Recent joint prediction transformer models often overlook their cross-task dynamics and video-text alignment and refinement. Moreover, most models typically use limited, uni-directional attention mechanisms, resulting in weakly integrated representations and suboptimal performance in capturing the interdependence between video and text modalities. Although large-language and vision-language models (LLM/LVLMs) have gained prominence across various domains, their application in this field remains relatively underexplored. Here we propose VideoLights, a novel HD/MR framework addressing these limitations through (i) Convolutional Projection and Feature Refinement modules with an alignment loss for better video-text feature alignment, (ii) Bi-Directional Cross-Modal Fusion network for strongly coupled query-aware clip representations, and (iii) Uni-directional joint-task feedback mechanism enhancing both tasks through correlation. In addition, (iv) we introduce hard positive/negative losses for adaptive error penalization and improved learning, and (v) leverage LVLMs like BLIP-2 for enhanced multimodal feature integration and intelligent pretraining using synthetic data generated from LVLMs. Comprehensive experiments on QVHighlights, TVSum, and Charades-STA benchmarks demonstrate state-of-the-art performance. Codes and models are available at https://github.com/dpaul06/VideoLights .
Occam's LGS: A Simple Approach for Language Gaussian Splatting
Authors: Jiahuan Cheng, Jan-Nico Zaech, Luc van Gool, Danda Pani Paudel
Date: 2024-12-02 00:00:00
Tasks: 3DGS, 3D Reconstruction, Scene Understanding

TL;DR: Gaussian Splatting is a widely adopted approach for 3D scene representation that offers efficient, high-quality 3D reconstruction and rendering. A major reason for the success of 3DGS is its simplicity of representing a scene with a set of Gaussians, which makes it easy to interpret and adapt. To enhance scene understanding beyond the visual representation, approaches have been developed that extend 3D Gaussian Splatting with semantic vision-language features, especially allowing for open-set tasks. In this setting, the language features of 3D Gaussian Splatting are often aggregated from multiple 2D views. Existing works address this aggregation problem using cumbersome techniques that lead to high computational cost and training time. In this work, we show that the sophisticated techniques for language-grounded 3D Gaussian Splatting are simply unnecessary. Instead, we apply Occam's razor to the task at hand and perform weighted multi-view feature aggregation using the weights derived from the standard rendering process, followed by a simple heuristic-based noisy Gaussian filtration. Doing so offers us state-of-the-art results with a speed-up of two orders of magnitude. We showcase our results in two commonly used benchmark datasets: LERF and 3D-OVS. Our simple approach allows us to perform reasoning directly in the language features, without any compression whatsoever. Such modeling in turn offers easy scene manipulation, unlike the existing methods -- which we illustrate using an application of object insertion in the scene. Furthermore, we provide a thorough discussion regarding the significance of our contributions within the context of the current literature. Project Page: https://insait-institute.github.io/OccamLGS/
Visual Error Patterns in Multi-Modal AI: A Statistical Approach
Authors: Ching-Yi Wang
Date: 2024-11-27 00:00:00
Tasks: Feature Importance

Multi-modal large language models (MLLMs), such as GPT-4o, excel at integrating text and visual data but face systematic challenges when interpreting ambiguous or incomplete visual stimuli. This study leverages statistical modeling to analyze the factors driving these errors, using a dataset of geometric stimuli characterized by features like 3D, rotation, and missing face/side. We applied parametric methods, non-parametric methods, and ensemble techniques to predict classification errors, with the non-linear gradient boosting model achieving the highest performance (AUC=0.85) during cross-validation. Feature importance analysis highlighted difficulties in depth perception and reconstructing incomplete structures as key contributors to misclassification. These findings demonstrate the effectiveness of statistical approaches for uncovering limitations in MLLMs and offer actionable insights for enhancing model architectures by integrating contextual reasoning mechanisms.
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
Authors: Cevahir Koprulu, Po-han Li, Tianyu Qiu, Ruihan Zhao, Tyler Westenbroek, David Fridovich-Keil, Sandeep Chinchali, Ufuk Topcu
Date: 2024-12-02 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning (RL)

Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to solve each new task. However, discovering sequences of actions that lead to a non-zero reward becomes exponentially more difficult as the task horizon increases. Manually shaping rewards can accelerate learning for a fixed task, but it is an arduous process that must be repeated for each new environment. We introduce a systematic reward-shaping framework that distills the information contained in 1) a task-agnostic prior data set and 2) a small number of task-specific expert demonstrations, and then uses these priors to synthesize dense dynamics-aware rewards for the given task. This supervision substantially accelerates learning in our experiments, and we provide analysis demonstrating how the approach can effectively guide online learning agents to faraway goals.
A Cross-Scene Benchmark for Open-World Drone Active Tracking
Authors: Haowei Sun, Jinwu Hu, Zhirui Zhang, Haoyuan Tian, Xinze Xie, Yufeng Wang, Zhuliang Yu, Xiaohua Xie, Mingkui Tan
Date: 2024-12-01 00:00:00
Tasks: Domain Generalization

Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations, providing a more practical solution for effective tracking in dynamic environments. However, accurate Drone Visual Active Tracking using reinforcement learning remains challenging due to the absence of a unified benchmark, the complexity of open-world environments with frequent interference, and the diverse motion behavior of dynamic targets. To address these issues, we propose a unified cross-scene cross-domain benchmark for open-world drone active tracking called DAT. The DAT benchmark provides 24 visually complex environments to assess the algorithms' cross-scene and cross-domain generalization abilities, and high-fidelity modeling of realistic robot dynamics. Additionally, we propose a reinforcement learning-based drone tracking method called R-VAT, which aims to improve the performance of drone tracking targets in complex scenarios. Specifically, inspired by curriculum learning, we introduce a Curriculum-Based Training strategy that progressively enhances the agent tracking performance in vast environments with complex interference. We design a goal-centered reward function to provide precise feedback to the drone agent, preventing targets farther from the center of view from receiving higher rewards than closer ones. This allows the drone to adapt to the diverse motion behavior of open-world targets. Experiments demonstrate that the R-VAT has about 400% improvement over the SOTA method in terms of the cumulative reward metric.
Residual Attention Single-Head Vision Transformer Network for Rolling Bearing Fault Diagnosis in Noisy Environments
Authors: Songjiang Lai, Tsun-Hin Cheung, Jiayi Zhao, Kaiwen Xue, Ka-Chun Fung, Kin-Man Lam
Date: 2024-11-27 00:00:00
Tasks: Computational Efficiency, Fault Detection, Fault Diagnosis

Rolling bearings play a crucial role in industrial machinery, directly influencing equipment performance, durability, and safety. However, harsh operating conditions, such as high speeds and temperatures, often lead to bearing malfunctions, resulting in downtime, economic losses, and safety hazards. This paper proposes the Residual Attention Single-Head Vision Transformer Network (RA-SHViT-Net) for fault diagnosis in rolling bearings. Vibration signals are transformed from the time to frequency domain using the Fast Fourier Transform (FFT) before being processed by RA-SHViT-Net. The model employs the Single-Head Vision Transformer (SHViT) to capture local and global features, balancing computational efficiency and predictive accuracy. To enhance feature extraction, the Adaptive Hybrid Attention Block (AHAB) integrates channel and spatial attention mechanisms. The network architecture includes Depthwise Convolution, Single-Head Self-Attention, Residual Feed-Forward Networks (Res-FFN), and AHAB modules, ensuring robust feature representation and mitigating gradient vanishing issues. Evaluation on the Case Western Reserve University and Paderborn University datasets demonstrates the RA-SHViT-Net's superior accuracy and robustness in complex, noisy environments. Ablation studies further validate the contributions of individual components, establishing RA-SHViT-Net as an effective tool for early fault detection and classification, promoting efficient maintenance strategies in industrial settings. Keywords: rolling bearings, fault diagnosis, Vision Transformer, attention mechanism, noisy environments, Fast Fourier Transform (FFT)
MolReFlect: Towards Fine-grained In-Context Alignment between Molecules and Texts
Authors: Jiatong Li, Yunqing Liu, Wei Liu, Jingdi Lei, Di Zhang, Wenqi Fan, Dongzhan Zhou, Yuqiang Li, Qing Li
Date: 2024-11-22 00:00:00
Proceeding: arXiv preprint 2024 11
Tasks: Descriptive, Molecule Captioning, Text-based de novo Molecule Generation

Molecule discovery is a pivotal research field, impacting everything from the medicines we take to the materials we use. Recently, Large Language Models (LLMs) have been widely adopted in molecule understanding and generation, yet the alignments between molecules and their corresponding captions remain a significant challenge. Previous endeavours often treat the molecule as a general SMILES string or molecular graph, neglecting the fine-grained alignments between the molecular sub-structures and the descriptive textual phrases, which are crucial for accurate and explainable predictions. In this case, we introduce MolReFlect, a novel teacher-student framework designed to contextually perform the molecule-caption alignments in a fine-grained way. Our approach initially leverages a larger teacher LLM to label the detailed alignments by directly extracting critical phrases from molecule captions or SMILES strings and implying them to corresponding sub-structures or characteristics. To refine these alignments, we propose In-Context Selective Reflection, which retrieves previous extraction results as context examples for teacher LLM to reflect and lets a smaller student LLM select from in-context reflection and previous extraction results. Finally, we enhance the learning process of the student LLM through Chain-of-Thought In-Context Molecule Tuning, integrating the fine-grained alignments and the reasoning processes within the Chain-of-Thought format. Our experimental results demonstrate that MolReFlect enables LLMs like Mistral-7B to significantly outperform the previous baselines, achieving SOTA performance on the ChEBI-20 dataset. This advancement not only enhances the generative capabilities of LLMs in the molecule-caption translation task, but also contributes to a more explainable framework.
How Much Can Time-related Features Enhance Time Series Forecasting?
Authors: Chaolv Zeng, Yuan Tian, Guanjie Zheng, Yunjun Gao
Date: 2024-12-02 00:00:00
Tasks: Computational Efficiency, Time Series, Time Series Forecasting

Recent advancements in long-term time series forecasting (LTSF) have primarily focused on capturing cross-time and cross-variate (channel) dependencies within historical data. However, a critical aspect often overlooked by many existing methods is the explicit incorporation of \textbf{time-related features} (e.g., season, month, day of the week, hour, minute), which are essential components of time series data. The absence of this explicit time-related encoding limits the ability of current models to capture cyclical or seasonal trends and long-term dependencies, especially with limited historical input. To address this gap, we introduce a simple yet highly efficient module designed to encode time-related features, Time Stamp Forecaster (TimeSter), thereby enhancing the backbone's forecasting performance. By integrating TimeSter with a linear backbone, our model, TimeLinear, significantly improves the performance of a single linear projector, reducing MSE by an average of 23\% on benchmark datasets such as Electricity and Traffic. Notably, TimeLinear achieves these gains while maintaining exceptional computational efficiency, delivering results that are on par with or exceed state-of-the-art models, despite using a fraction of the parameters.
FD-LLM: Large Language Model for Fault Diagnosis of Machines
Authors: Hamzah A. A. M. Qaid, Bo Zhang, Dan Li, See-Kiong Ng, Wei Li
Date: 2024-12-02 00:00:00
Tasks: Fault Detection, Fault Diagnosis, Language Modeling, Language Modelling, Large Language Model, Multi-class Classification

Large language models (LLMs) are effective at capturing complex, valuable conceptual representations from textual data for a wide range of real-world applications. However, in fields like Intelligent Fault Diagnosis (IFD), incorporating additional sensor data-such as vibration signals, temperature readings, and operational metrics-is essential but it is challenging to capture such sensor data information within traditional text corpora. This study introduces a novel IFD approach by effectively adapting LLMs to numerical data inputs for identifying various machine faults from time-series sensor data. We propose FD-LLM, an LLM framework specifically designed for fault diagnosis by formulating the training of the LLM as a multi-class classification problem. We explore two methods for encoding vibration signals: the first method uses a string-based tokenization technique to encode vibration signals into text representations, while the second extracts statistical features from both the time and frequency domains as statistical summaries of each signal. We assess the fault diagnosis capabilities of four open-sourced LLMs based on the FD-LLM framework, and evaluate the models' adaptability and generalizability under various operational conditions and machine components, namely for traditional fault diagnosis, cross-operational conditions, and cross-machine component settings. Our results show that LLMs such as Llama3 and Llama3-instruct demonstrate strong fault detection capabilities and significant adaptability across different operational conditions, outperforming state-of-the-art deep learning (DL) approaches in many cases.
GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024
Authors: Xingyu Liu, Gu Wang, Chengxi Li, Yingyue Li, Chenyangguang Zhang, Ziqin Huang, Xiangyang Ji
Date: 2024-12-02 00:00:00
Tasks: Mixed Reality, object-detection, Object Detection

We present GFreeDet, an unseen object detection approach that leverages Gaussian splatting and vision Foundation models under model-free setting. Unlike existing methods that rely on predefined CAD templates, GFreeDet reconstructs objects directly from reference videos using Gaussian splatting, enabling robust detection of novel objects without prior 3D models. Evaluated on the BOP-H3 benchmark, GFreeDet achieves comparable performance to CAD-based methods, demonstrating the viability of model-free detection for mixed reality (MR) applications. Notably, GFreeDet won the best overall method and the best fast method awards in the model-free 2D detection track at BOP Challenge 2024.
Video-Driven Graph Network-Based Simulators
Authors: Franciszek Szewczyk, Gilles Louppe, Matthia Sabatelli
Date: 2024-09-10 00:00:00

Lifelike visualizations in design, cinematography, and gaming rely on precise physics simulations, typically requiring extensive computational resources and detailed physical input. This paper presents a method that can infer a system's physical properties from a short video, eliminating the need for explicit parameter input, provided it is close to the training condition. The learned representation is then used within a Graph Network-based Simulator to emulate the trajectories of physical systems. We demonstrate that the video-derived encodings effectively capture the physical properties of the system and showcase a linear dependence between some of the encodings and the system's motion.