pwc-archive/papers-with-abstracts huggingface dataset:
Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles
Authors: Yassine Kebbati, Naima Ait-Oufroukh, Vincent Vigneron, Dalil Ichalal
Date: 2022-08-05 00:00:00
Proceeding: 2022 European Control Conference (ECC) 2022 8
Tasks: Autonomous Driving, Autonomous Vehicles

Autonomous driving is achieved by controlling the coupled nonlinear longitudinal and lateral vehicle dynamics. Longitudinal control greatly affects lateral dynamics and must preserve lateral stability conditions, while lateral controllers must take into account actuator limits and ride comfort. This work deals with the coordinated longitudinal and lateral control for autonomous driving. An improved particle swarm optimized PID (PSO-PID) is proposed to handle the task of speed tracking based on nonlinear longitudinal dynamics. An enhanced linear parameter varying model predictive controller (LPV-MPC) is also designed to control lateral dynamics, the latter is formulated with an adaptive LPV model in which the tire cornering stiffness coefficients are estimated by a recursive estimator. The proposed LPV-MPC is enhanced with an improved cost function to provide better performance and stability. Matlab/Carsim co-simulations are carried out to validate the proposed controllers.
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model
Authors: Taehwan Yoon, Bongjun Choi
Date: 2025-06-29 00:00:00
Tasks: Brain Tumor Segmentation, Federated Learning, Model Optimization, Transfer Learning

Federated learning(FL) is used for distributed scenarios to train artificial intelligence(AI) models while ensuring users' privacy. In federated learning scenario, the server generally never knows about users' data. This type of concept makes the AI training process efficient in terms of data privacy. However, regarding model performance, federated AI models may not sufficiently satisfy AI users' expectations. Furthermore, AI users have a wide range of different needs. It is not easy to satisfy the whole users needs. These types of issues can be addressed through AI model optimization, fine-tuning, or personalization to achieve optimal model performance. To address model optimization challenges, we propose reference model-based federated learning for optimal fine-tuning, which overcomes catastrophic forgetting in each round. This method is derived from Bayesian parameter-efficient transfer learning, which includes an optimal proximal term and enables overcoming the catastrophic forgetting issue in each round by utilizing a reference model that incorporates previous model parameters. As a result, this method achieves both high model performance and low computing cost.
Structural feature enhanced transformer for fine-grained image recognition
Authors: Ying Yu, Wei Wei, Cairong Zhao, Jin Qian, Enhong Chen
Date: 2025-06-14 00:00:00
Proceeding: Pattern Recognition 2025 6
Tasks: Computational Efficiency, Fine-Grained Image Classification, Fine-Grained Image Recognition

Existing fine-grained image recognition (FGIR) models mainly rely on high-level semantic features to extract discriminative information, ignoring the potential role of the overall structural information of objects and the structural relationships between key parts. To address this issue, we propose the Structural Feature Enhancement Transformer (SFETrans). SFETrans consists of a visual transformer backbone network responsible for extracting complex semantic features. Additionally, it includes a structural modeling (SM) branch and an amplitude component exchange (ACE) module, both dedicated to enhancing the learning of structural features. The SM branch actively models the structural relationships between key parts of objects and extracts corresponding structural features, while the ACE module guides the model to learn structural information in the phase spectrum by introducing implicit constraints during training. By synergizing the backbone network and the two modules, SFETrans exhibits competitive performance on four benchmark datasets and outperforms other comparison methods in terms of computational efficiency.
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
Authors: Zuyao You, Zuxuan Wu
Date: 2025-06-27 00:00:00
Tasks: Foreground Segmentation, object-detection, Object Detection, Reasoning Segmentation, Reinforcement Learning (RL), Salient Object Detection, Segmentation

We present Seg-R1, a preliminary exploration of using reinforcement learning (RL) to enhance the pixel-level understanding and reasoning capabilities of large multimodal models (LMMs). Starting with foreground segmentation tasks, specifically camouflaged object detection (COD) and salient object detection (SOD), our approach enables the LMM to generate point and bounding box prompts in the next-token fashion, which are then used to guide SAM2 in producing segmentation masks. We introduce Group Relative Policy Optimization (GRPO) into the segmentation domain, equipping the LMM with pixel-level comprehension through a carefully designed training strategy. Notably, Seg-R1 achieves remarkable performance with purely RL-based training, achieving .873 S-measure on COD10K without complex model modification. Moreover, we found that pure RL training demonstrates strong open-world generalization. Despite being trained solely on foreground segmentation image-mask pairs without text supervision, Seg-R1 achieves impressive zero-shot performance on referring segmentation and reasoning segmentation tasks, with 71.4 cIoU on RefCOCOg test and 56.7 gIoU on ReasonSeg test, outperforming models fully supervised on these datasets.
MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction
Authors: Steven E. Wilson, Sina Khanmohammadi
Date: 2025-04-24 00:00:00
Tasks: Link Prediction, Node Classification, Prediction

Graph Neural Networks (GNNs) have been widely used for various learning tasks, ranging from node classification to link prediction. They have demonstrated excellent performance in multiple domains involving graph-structured data. However, an important category of learning tasks, namely link weight prediction, has received less emphasis due to its increased complexity compared to binary link classification. Link weight prediction becomes even more challenging when considering multilayer networks, where nodes can be interconnected across multiple layers. To address these challenges, we propose a new method named Multiplex Spatial Graph Convolution Network (MSGCN), which spatially embeds information across multiple layers to predict interlayer link weights. The MSGCN model generalizes spatial graph convolution to multiplex networks and captures the geometric structure of nodes across multiple layers. Extensive experiments using data with known interlayer link information show that the MSGCN model has robust, accurate, and generalizable link weight prediction performance across a wide variety of multiplex network structures.
Energy-Based Transformers are Scalable Learners and Thinkers
Authors: Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam, Peixuan Han, Hyeonjeong Ha, Aman Chadha, Yilun Du, Heng Ji, Jundong Li, Tariq Iqbal
Date: 2025-07-02 00:00:00
Tasks: Denoising, Image Denoising, Math

Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are modality-specific (e.g., working only in text), problem-specific (e.g., verifiable domains like math and coding), or require additional supervision/training on top of unsupervised pretraining (e.g., verifiers or verifiable rewards). In this paper, we ask the question "Is it possible to generalize these System 2 Thinking approaches, and develop models that learn to think solely from unsupervised learning?" Interestingly, we find the answer is yes, by learning to explicitly verify the compatibility between inputs and candidate-predictions, and then re-framing prediction problems as optimization with respect to this verifier. Specifically, we train Energy-Based Transformers (EBTs) -- a new class of Energy-Based Models (EBMs) -- to assign an energy value to every input and candidate-prediction pair, enabling predictions through gradient descent-based energy minimization until convergence. Across both discrete (text) and continuous (visual) modalities, we find EBTs scale faster than the dominant Transformer++ approach during training, achieving an up to 35% higher scaling rate with respect to data, batch size, parameters, FLOPs, and depth. During inference, EBTs improve performance with System 2 Thinking by 29% more than the Transformer++ on language tasks, and EBTs outperform Diffusion Transformers on image denoising while using fewer forward passes. Further, we find that EBTs achieve better results than existing models on most downstream tasks given the same or worse pretraining performance, suggesting that EBTs generalize better than existing approaches. Consequently, EBTs are a promising new paradigm for scaling both the learning and thinking capabilities of models.
Hindsight-Guided Momentum (HGM) Optimizer: An Approach to Adaptive Learning Rate
Authors: Krisanu Sarkar
Date: 2025-06-22 00:00:00

We introduce Hindsight-Guided Momentum (HGM), a first-order optimization algorithm that adaptively scales learning rates based on the directional consistency of recent updates. Traditional adaptive methods, such as Adam or RMSprop , adapt learning dynamics using only the magnitude of gradients, often overlooking important geometric cues.Geometric cues refer to directional information, such as the alignment between current gradients and past updates, which reflects the local curvature and consistency of the optimization path. HGM addresses this by incorporating a hindsight mechanism that evaluates the cosine similarity between the current gradient and accumulated momentum. This allows it to distinguish between coherent and conflicting gradient directions, increasing the learning rate when updates align and reducing it in regions of oscillation or noise. The result is a more responsive optimizer that accelerates convergence in smooth regions of the loss surface while maintaining stability in sharper or more erratic areas. Despite this added adaptability, the method preserves the computational and memory efficiency of existing optimizers.By more intelligently responding to the structure of the optimization landscape, HGM provides a simple yet effective improvement over existing approaches, particularly in non-convex settings like that of deep neural network training.
FR-CapsNet: Enhancing Low-Resolution Image Classification via Frequency Routed Capsules
Authors: Hasindu Dewasurendra, Kunmin Yeo, Nhan Thi Cao, Taejoon Kim
Date: 2025-06-25 00:00:00
Proceeding: IEEE Access 2025 6
Tasks: image-classification, Image Classification

Very low-resolution (VLR) images present a significant challenge for deep classification networks due to their inherent lack of fine spatial detail. While capsule networks (CapsNets), which encode spatial and pose information, are robust to resolution changes, they often struggle to perform and scale effectively on complex datasets. In this study, we introduce a novel frequency routing-based CapsNet (FR-CapsNet) to replace the conventional spatial routing in CapsNets. Though VLR images lose fine grained features, they retain high-level features captured by the low-frequency components. By computing capsule activation and pose information in the frequency domain and subsequently encoding them in the spatial domain, FR-CapsNet improves robustness to resolution degradation. Furthermore, our method utilizes a global routing framework that considerably reduces computational demands, enabling FR-CapsNet to scale effectively to larger and more diverse datasets. FR-CapsNet outperforms state-of-the-art (SOTA) convolutional neural networks (CNNs), other CapsNets, Transformers, and other advanced architectures in real-world VLR digit and image classification tasks. Specifically, on the VLR CIFAR-10 dataset, FR-CapsNet surpasses the current benchmark by 4.77% while using 4 times fewer parameters. Similarly, on the VLR SVHN and CIFAR-100 datasets, it exceeds the benchmark by 0.27% and 1.55%, respectively. Extensive experiments further demonstrate the superior generalization and robustness of FR-CapsNet compared to other SOTA methods. The codes for our models are available at https://github.com/kdhasi/FR-CapsNet.git
Understanding and Improving Length Generalization in Recurrent Models
Authors: Ricardo Buitrago Ruiz, Albert Gu
Date: 2025-07-03 00:00:00
Tasks: 2k, State Space Models

Recently, recurrent models such as state space models and linear attention have become popular due to their linear complexity in the sequence length. Thanks to their recurrent nature, in principle they can process arbitrarily long sequences, but their performance sometimes drops considerably beyond their training context lengths-i.e. they fail to length generalize. In this work, we provide comprehensive empirical and theoretical analysis to support the unexplored states hypothesis, which posits that models fail to length generalize when during training they are only exposed to a limited subset of the distribution of all attainable states (i.e. states that would be attained if the recurrence was applied to long sequences). Furthermore, we investigate simple training interventions that aim to increase the coverage of the states that the model is trained on, e.g. by initializing the state with Gaussian noise or with the final state of a different input sequence. With only 500 post-training steps ($\sim 0.1\%$ of the pre-training budget), these interventions enable length generalization for sequences that are orders of magnitude longer than the training context (e.g. $2k\longrightarrow 128k$) and show improved performance in long context tasks, thus presenting a simple and efficient way to enable robust length generalization in general recurrent models.
Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World Denoising
Authors: Yuhui Quan; Tianxiang Zheng; Hui Ji
Date: 2025-06-05 00:00:00
Proceeding: The Annual Conference on Neural Information Processing Systems 2025 6
Tasks: Denoising, Image Denoising

Real-world image denoising remains a challenge task. This paper studies self-supervised image denoising, requiring only noisy images captured in a single shot. We revamping the blind-spot technique by leveraging the transformer’s capability for long-range pixel interactions, which is crucial for effectively removing noise dependence in relating pixel–a requirement for achieving great performance for the blind-spot technique. The proposed method integrates these elements with two key innovations: a directional self-attention (DSA) module using a halfplane grid for self-attention, creating a sophisticated blind-spot structure, and a Siamese architecture with mutual learning to mitigate the performance impacts from the restricted attention grid in DSA. Experiments on benchmark datasets demonstrate that our method outperforms existing self-supervised and clean-imagefree methods. This combination of blind-spot and transformer techniques provides a natural synergy for tackling real-world image denoising challenges.
Toward Cyclic A.I. Modelling of Self-Regulated Learning: A Case Study with E-Learning Trace Data
Authors: Andrew Schwabe, Özgür Akgün, Ella Haig
Date: 2025-06-25 00:00:00

Many e-learning platforms assert their ability or potential to improve students' self-regulated learning (SRL), however the cyclical and undirected nature of SRL theoretical models represent significant challenges for representation within contemporary machine learning frameworks. We apply SRL-informed features to trace data in order to advance modelling of students' SRL activities, to improve predictability and explainability regarding the causal effects of learning in an eLearning environment. We demonstrate that these features improve predictive accuracy and validate the value of further research into cyclic modelling techniques for SRL.
Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning
Authors: Mahavir Dabas, Si Chen, Charles Fleming, Ming Jin, Ruoxi Jia
Date: 2025-07-06 00:00:00
Tasks: Safety Alignment

Safety alignment is crucial for large language models (LLMs) to resist malicious instructions but often results in over-refusals, where benign prompts are unnecessarily rejected, impairing user experience and model utility. We introduce ACTOR (Activation-Based Training for Over-Refusal Reduction), a robust and compute- and data-efficient training framework that minimizes over-refusals by leveraging internal activation patterns from diverse queries. ACTOR precisely identifies and adjusts the activation components that trigger refusals, providing stronger control over the refusal mechanism. By fine-tuning only a single model layer, ACTOR effectively reduces over-refusals across multiple benchmarks while maintaining the model's ability to handle harmful queries and preserve overall utility.
Query-Based Adaptive Aggregation for Multi-Dataset Joint Training Toward Universal Visual Place Recognition
Authors: Jiuhong Xiao, Yang Zhou, Giuseppe Loianno
Date: 2025-07-04 00:00:00
Tasks: Visual Place Recognition

Deep learning methods for Visual Place Recognition (VPR) have advanced significantly, largely driven by large-scale datasets. However, most existing approaches are trained on a single dataset, which can introduce dataset-specific inductive biases and limit model generalization. While multi-dataset joint training offers a promising solution for developing universal VPR models, divergences among training datasets can saturate limited information capacity in feature aggregation layers, leading to suboptimal performance. To address these challenges, we propose Query-based Adaptive Aggregation (QAA), a novel feature aggregation technique that leverages learned queries as reference codebooks to effectively enhance information capacity without significant computational or parameter complexity. We show that computing the Cross-query Similarity (CS) between query-level image features and reference codebooks provides a simple yet effective way to generate robust descriptors. Our results demonstrate that QAA outperforms state-of-the-art models, achieving balanced generalization across diverse datasets while maintaining peak performance comparable to dataset-specific models. Ablation studies further explore QAA's mechanisms and scalability. Visualizations reveal that the learned queries exhibit diverse attention patterns across datasets. Code will be publicly released.
STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking
Authors: Tek Raj Chhetri, Yibei Chen, Puja Trivedi, Dorota Jarecka, Saif Haobsh, Patrick Ray, Lydia Ng, Satrajit S. Ghosh
Date: 2025-07-04 00:00:00
Tasks: Benchmarking, Navigate, scientific discovery

The ability to extract structured information from unstructured sources-such as free-text documents and scientific literature-is critical for accelerating scientific discovery and knowledge synthesis. Large Language Models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks, including structured information extraction. However, their effectiveness often diminishes in specialized, domain-specific contexts that require nuanced understanding and expert-level domain knowledge. In addition, existing LLM-based approaches frequently exhibit poor transferability across tasks and domains, limiting their scalability and adaptability. To address these challenges, we introduce StructSense, a modular, task-agnostic, open-source framework for structured information extraction built on LLMs. StructSense is guided by domain-specific symbolic knowledge encoded in ontologies, enabling it to navigate complex domain content more effectively. It further incorporates agentic capabilities through self-evaluative judges that form a feedback loop for iterative refinement, and includes human-in-the-loop mechanisms to ensure quality and validation. We demonstrate that StructSense can overcome both the limitations of domain sensitivity and the lack of cross-task generalizability, as shown through its application to diverse neuroscience information extraction tasks.
Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
Authors: Ha-Hieu Pham, Nguyen Lan Vi Vu, Thanh-Huy Nguyen, Ulas Bagci, Min Xu, Trung-Nghia Le, Huy-Hieu Pham
Date: 2025-07-05 00:00:00
Tasks: Prognosis, Segmentation

Accurate gland segmentation in histopathology images is essential for cancer diagnosis and prognosis. However, significant variability in Hematoxylin and Eosin (H&E) staining and tissue morphology, combined with limited annotated data, poses major challenges for automated segmentation. To address this, we propose Color-Structure Dual-Student (CSDS), a novel semi-supervised segmentation framework designed to learn disentangled representations of stain appearance and tissue structure. CSDS comprises two specialized student networks: one trained on stain-augmented inputs to model chromatic variation, and the other on structure-augmented inputs to capture morphological cues. A shared teacher network, updated via Exponential Moving Average (EMA), supervises both students through pseudo-labels. To further improve label reliability, we introduce stain-aware and structure-aware uncertainty estimation modules that adaptively modulate the contribution of each student during training. Experiments on the GlaS and CRAG datasets show that CSDS achieves state-of-the-art performance in low-label settings, with Dice score improvements of up to 1.2% on GlaS and 0.7% on CRAG at 5% labeled data, and 0.7% and 1.4% at 10%. Our code and pre-trained models are available at https://github.com/hieuphamha19/CSDS.
SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
Authors: Chun Xie, Yuichi Yoshii, Itaru Kitahara
Date: 2025-07-07 00:00:00
Tasks: Anatomy, Image Generation

X-ray imaging is a rapid and cost-effective tool for visualizing internal human anatomy. While multi-view X-ray imaging provides complementary information that enhances diagnosis, intervention, and education, acquiring images from multiple angles increases radiation exposure and complicates clinical workflows. To address these challenges, we propose a novel view-conditioned diffusion model for synthesizing multi-view X-ray images from a single view. Unlike prior methods, which are limited in angular range, resolution, and image quality, our approach leverages the Diffusion Transformer to preserve fine details and employs a weak-to-strong training strategy for stable high-resolution image generation. Experimental results demonstrate that our method generates higher-resolution outputs with improved control over viewing angles. This capability has significant implications not only for clinical applications but also for medical education and data extension, enabling the creation of diverse, high-quality datasets for training and analysis. Our code is available at GitHub.