OpenRaiser/Intern-Atlas huggingface dataset:
Depth-Assisted Network for Indiscernible Marine Object Counting with Adaptive Motion-Differentiated Feature Encoding
Venue: arXiv cs.CV 2025
Year: 2025

Indiscernible marine object counting encounters numerous challenges, including limited visibility in underwater scenes, mutual occlusion and overlap among objects, and the dynamic similarity in appearance, color, and texture between the background and foreground. These factors significantly complicate the counting process. To address the scarcity of video-based indiscernible object counting datasets, we have developed a novel dataset comprising 50 videos, from which approximately 800 frames have been extracted and annotated with around 40,800 point-wise object labels. This dataset accurately represents real underwater environments where indiscernible marine objects are intricately integrated with their surroundings, thereby comprehensively illustrating the aforementioned challenges in object counting. To address these challenges, we propose a depth-assisted network with adaptive motion-differentiated feature encoding. The network consists of a backbone encoding module and three branches: a depth-assisting branch, a density estimation branch, and a motion weight generation branch. Depth-aware features extracted by the depth-assisting branch are enhanced via a depth-enhanced encoder to improve object representation. Meanwhile, weights from the motion weight generation branch refine multi-scale perception features in the adaptive flow estimation module. Experimental results demonstrate that our method not only achieves state-of-the-art performance on the proposed dataset but also yields competitive results on three additional video-based crowd counting datasets. The pre-trained model, code, and dataset are publicly available at https://github.com/OUCVisionGroup/VIMOC-Net.
S2A: A Unified Framework for Parameter and Memory Efficient Transfer Learning
Venue: arXiv cs.CV 2025
Year: 2025

Parameter-efficient transfer learning (PETL) aims to reduce the scales of pretrained models for multiple downstream tasks. However, as the models keep scaling up, the memory footprint of existing PETL methods is not significantly reduced compared to the reduction of learnable parameters. This limitation hinders the practical deployment of PETL methods on memory-constrained devices. To this end, we proposed a new PETL framework, called Structure to Activation (S2A), to reduce the memory footprint of activation during fine-tuning. Specifically, our framework consists of: 1) Activation modules design(i.e., bias, prompt and side modules) in the parametric model structure, which results in a significant reduction of adjustable parameters and activation memory; 2) 4-bit quantization of activations based on their derivatives for non-parametric structures (e.g., nonlinear functions), which maintains accuracy while significantly reducing memory usage. Our S2A method consequently offers a lightweight solution in terms of both parameters and memory footprint. We evaluated S2A with different backbones and performed extensive experiments on various datasets to evaluate the effectiveness. The results show that our methods not only outperform existing PETL techniques, achieving a fourfold reduction in GPU memory footprint on average, but also shows competitive performance in accuracy with fewer tunable parameters. These demonstrate that our method is highly suitable for practical transfer learning on hardware-constrained devices.
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
Venue: arXiv cs.LG 2025
Year: 2025

Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction skills are increasing with advances in numerical weather prediction and artificial intelligence tools, extreme weather still present challenges. More specifically, identifying the precursors of such extreme weather events and how these precursors may evolve under climate change remain unclear. In this paper, we propose to use post-hoc interpretability methods to construct relevance weather maps that show the key extreme-weather precursors identified by deep learning models. We then compare this machine view with existing domain knowledge to understand whether deep learning models identified patterns in data that may enrich our understanding of extreme-weather precursors. We finally bin these relevant maps into different multi-year time periods to understand the role that climate change is having on these precursors. The experiments are carried out on Indochina heatwaves, but the methodology can be readily extended to other extreme weather events worldwide.
CQVPR: Landmark-aware Contextual Queries for Visual Place Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Visual Place Recognition (VPR) aims to estimate the location of the given query image within a database of geo-tagged images. To identify the exact location in an image, detecting landmarks is crucial. However, in some scenarios, such as urban environments, there are numerous landmarks, such as various modern buildings, and the landmarks in different cities often exhibit high visual similarity. Therefore, it is essential not only to leverage the landmarks but also to consider the contextual information surrounding them, such as whether there are trees, roads, or other features around the landmarks. We propose the Contextual Query VPR (CQVPR), which integrates contextual information with detailed pixel-level visual features. By leveraging a set of learnable contextual queries, our method automatically learns the high-level contexts with respect to landmarks and their surrounding areas. Heatmaps depicting regions that each query attends to serve as context-aware features, offering cues that could enhance the understanding of each scene. We further propose a query matching loss to supervise the extraction process of contextual queries. Extensive experiments on several datasets demonstrate that the proposed method outperforms other state-of-the-art methods, especially in challenging scenarios.
Towards Synthesized and Editable Motion In-Betweening Through Part-Wise Phase Representation
Venue: arXiv cs.CV 2025
Year: 2025

Styled motion in-betweening is crucial for computer animation and gaming. However, existing methods typically encode motion styles by modeling whole-body motions, often overlooking the representation of individual body parts. This limitation reduces the flexibility of infilled motion, particularly in adjusting the motion styles of specific limbs independently. To overcome this challenge, we propose a novel framework that models motion styles at the body-part level, enhancing both the diversity and controllability of infilled motions. Our approach enables more nuanced and expressive animations by allowing precise modifications to individual limb motions while maintaining overall motion coherence. Leveraging phase-related insights, our framework employs periodic autoencoders to automatically extract the phase of each body part, capturing distinctive local style features. Additionally, we effectively decouple the motion source from synthesis control by integrating motion manifold learning and conditional generation techniques from both image and motion domains. This allows the motion source to generate high-quality motions across various styles, with extracted motion and style features readily available for controlled synthesis in subsequent tasks. Comprehensive evaluations demonstrate that our method achieves superior speed, robust generalization, and effective generation of extended motion sequences.
Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in LLM-based role-playing language agents (RPLAs) have attracted broad attention in various applications. While chain-of-thought reasoning has shown importance in many tasks for LLMs, the internal thinking processes of RPLAs remain unexplored. Understanding characters' inner thoughts is crucial for developing advanced RPLAs. In this paper, we introduce ROLETHINK, a novel benchmark constructed from literature for evaluating character thought generation. We propose the task of inner thought reasoning, which includes two sets: the gold set that compares generated thoughts with original character monologues, and the silver set that uses expert synthesized character analyses as references. To address this challenge, we propose MIRROR, a chain-of-thought approach that generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations. Through extensive experiments, we demonstrate the importance of inner thought reasoning for RPLAs, and MIRROR consistently outperforms existing methods. Resources are available at https://github.com/airaer1998/RPA_Thought.
Investigating Execution-Aware Language Models for Code Optimization
Venue: arXiv cs.SE 2025
Year: 2025

Code optimization is the process of enhancing code efficiency, while preserving its intended functionality. This process often requires a deep understanding of the code execution behavior at run-time to identify and address inefficiencies effectively. Recent studies have shown that language models can play a significant role in automating code optimization. However, these models may have insufficient knowledge of how code execute at run-time. To address this limitation, researchers have developed strategies that integrate code execution information into language models. These strategies have shown promise, enhancing the effectiveness of language models in various software engineering tasks. However, despite the close relationship between code execution behavior and efficiency, the specific impact of these strategies on code optimization remains largely unexplored. This study investigates how incorporating code execution information into language models affects their ability to optimize code. Specifically, we apply three different training strategies to incorporate four code execution aspects -- line executions, line coverage, branch coverage, and variable states -- into CodeT5+, a well-known language model for code. Our results indicate that execution-aware models provide limited benefits compared to the standard CodeT5+ model in optimizing code.
Cost-driven prunings for iterative solving of constrained routing problem with SRLG-disjoint protection
Venue: arXiv cs.DS 2025
Year: 2025

The search for the optimal pair of active and protection paths in a network with Shared Risk Link Groups (SRLG) is a challenging but high-value problem in the industry that is inevitable in ensuring reliable connections on the modern Internet. We propose a new approach to solving this problem, with a novel use of statistical analysis of the distribution of paths with respect to their cost, which is an integral part of our innovation. The key idea in our algorithm is to employ iterative updates of cost bounds, allowing efficient pruning of suboptimal paths. This idea drives an efficacious exploration of the search space. We benchmark our algorithms against the state-of-the-art algorithms that exploit the alternative strategy of conflicting links exclusion, showing that our approach has the advantage of finding more feasible connections within a set time limit.
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows
Venue: arXiv cs.CR 2025
Year: 2025

The constant increase of devices connected to the Internet, and therefore of cyber-attacks, makes it necessary to analyze network traffic in order to recognize malicious activity. Traditional packet-based analysis methods are insufficient because in large networks the amount of traffic is so high that it is unfeasible to review all communications. For this reason, flows is a suitable approach for this situation, which in future 5G networks will have to be used, as the number of packets will increase dramatically. If this is also combined with unsupervised learning models, it can detect new threats for which it has not been trained. This paper presents a systematic review of the literature on unsupervised learning algorithms for detecting anomalies in network flows, following the PRISMA guideline. A total of 63 scientific articles have been reviewed, analyzing 13 of them in depth. The results obtained show that autoencoder is the most used option, followed by SVM, ALAD, or SOM. On the other hand, all the datasets used for anomaly detection have been collected, including some specialised in IoT or with real data collected from honeypots.
$^R$FLAV: Rolling Flow matching for infinite Audio Video generation
Venue: arXiv cs.CV 2025
Year: 2025

Joint audio-video (AV) generation is still a significant challenge in generative AI, primarily due to three critical requirements: quality of the generated samples, seamless multimodal synchronization and temporal coherence, with audio tracks that match the visual data and vice versa, and limitless video duration. In this paper, we present $^R$-FLAV, a novel transformer-based architecture that addresses all the key challenges of AV generation. We explore three distinct cross modality interaction modules, with our lightweight temporal fusion module emerging as the most effective and computationally efficient approach for aligning audio and visual modalities. Our experimental results demonstrate that $^R$-FLAV outperforms existing state-of-the-art models in multimodal AV generation tasks. Our code and checkpoints are available at https://github.com/ErgastiAlex/R-FLAV.
Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework
Venue: arXiv cs.AI 2025
Year: 2025

Multimodal large language models (MLLMs) show promise in tasks like visual question answering (VQA) but still face challenges in multimodal reasoning. Recent works adapt agentic frameworks or chain-of-thought (CoT) reasoning to improve performance. However, CoT-based multimodal reasoning often demands costly data annotation and fine-tuning, while agentic approaches relying on external tools risk introducing unreliable output from these tools. In this paper, we propose Seeing and Reasoning with Confidence (SRICE), a training-free multimodal reasoning framework that integrates external vision models with uncertainty quantification (UQ) into an MLLM to address these challenges. Specifically, SRICE guides the inference process by allowing MLLM to autonomously select regions of interest through multi-stage interactions with the help of external tools. We propose to use a conformal prediction-based approach to calibrate the output of external tools and select the optimal tool by estimating the uncertainty of an MLLM's output. Our experiment shows that the average improvement of SRICE over the base MLLM is 4.6% on five datasets and the performance on some datasets even outperforms fine-tuning-based methods, revealing the significance of ensuring reliable tool use in an MLLM agent.
Uni-Gaussians: Unifying Camera and Lidar Simulation with Gaussians for Dynamic Driving Scenarios
Venue: arXiv cs.RO 2025
Year: 2025

Ensuring the safety of autonomous vehicles necessitates comprehensive simulation of multi-sensor data, encompassing inputs from both cameras and LiDAR sensors, across various dynamic driving scenarios. Neural rendering techniques, which utilize collected raw sensor data to simulate these dynamic environments, have emerged as a leading methodology. While NeRF-based approaches can uniformly represent scenes for rendering data from both camera and LiDAR, they are hindered by slow rendering speeds due to dense sampling. Conversely, Gaussian Splatting-based methods employ Gaussian primitives for scene representation and achieve rapid rendering through rasterization. However, these rasterization-based techniques struggle to accurately model non-linear optical sensors. This limitation restricts their applicability to sensors beyond pinhole cameras. To address these challenges and enable unified representation of dynamic driving scenarios using Gaussian primitives, this study proposes a novel hybrid approach. Our method utilizes rasterization for rendering image data while employing Gaussian ray-tracing for LiDAR data rendering. Experimental results on public datasets demonstrate that our approach outperforms current state-of-the-art methods. This work presents a unified and efficient solution for realistic simulation of camera and LiDAR data in autonomous driving scenarios using Gaussian primitives, offering significant advancements in both rendering quality and computational efficiency.
i-WiViG: Interpretable Window Vision GNN
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning models based on graph neural networks have emerged as a popular approach for solving computer vision problems. They encode the image into a graph structure and can be beneficial for efficiently capturing the long-range dependencies typically present in remote sensing imagery. However, an important drawback of these methods is their black-box nature which may hamper their wider usage in critical applications. In this work, we tackle the self-interpretability of the graph-based vision models by proposing our Interpretable Window Vision GNN (i-WiViG) approach, which provides explanations by automatically identifying the relevant subgraphs for the model prediction. This is achieved with window-based image graph processing that constrains the node receptive field to a local image region and by using a self-interpretable graph bottleneck that ranks the importance of the long-range relations between the image regions. We evaluate our approach to remote sensing classification and regression tasks, showing it achieves competitive performance while providing inherent and faithful explanations through the identified relations. Further, the quantitative evaluation reveals that our model reduces the infidelity of post-hoc explanations compared to other Vision GNN models, without sacrificing explanation sparsity.
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data
Venue: arXiv cs.LG 2025
Year: 2025

Wind farms, typically in high-latitude regions, face a high risk of blade icing. Traditional centralized training methods raise serious privacy concerns. To enhance data privacy in detecting wind turbine blade icing, traditional federated learning (FL) is employed. However, data heterogeneity, resulting from collections across wind farms in varying environmental conditions, impacts the model's optimization capabilities. Moreover, imbalances in wind turbine data lead to models that tend to favor recognizing majority classes, thus neglecting critical icing anomalies. To tackle these challenges, we propose a federated prototype learning model for class-imbalanced data in heterogeneous environments to detect wind turbine blade icing. We also propose a contrastive supervised loss function to address the class imbalance problem. Experiments on real data from 20 turbines across two wind farms show our method outperforms five FL models and five class imbalance methods, with an average improvement of 19.64\% in \( mF_β \) and 5.73\% in \( m \)BA compared to the second-best method, BiFL.
Online Conformal Compression for Zero-Delay Communication with Distortion Guarantees
Venue: arXiv cs.IT 2025
Year: 2025

We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression scheme that, under a 0-1 loss distortion function, ensures a deterministic, per-sequence upper bound on the distortion (outage) level for any time instant. The outage guarantees apply irrespective of any assumption on the distribution of the sequences to be encoded or on the quality of the predictor at the encoder and decoder. The proposed method, referred to as online conformal compression (OCC), is built upon online conformal prediction--a novel method for constructing confidence intervals for arbitrary predictors. Numerical results show that OCC achieves a compression rate comparable to that of an idealized scheme in which the encoder, with hindsight, selects the optimal subset of symbols to describe to the decoder, while satisfying the overall outage constraint.
Design and Implementation of FourCropNet: A CNN-Based System for Efficient Multi-Crop Disease Detection and Management
Venue: arXiv cs.CV 2025
Year: 2025

Plant disease detection is a critical task in agriculture, directly impacting crop yield, food security, and sustainable farming practices. This study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops, including CottonLeaf, Grape, Soybean, and Corn. The model leverages an advanced architecture comprising residual blocks for efficient feature extraction, attention mechanisms to enhance focus on disease-relevant regions, and lightweight layers for computational efficiency. These components collectively enable FourCropNet to achieve superior performance across varying datasets and class complexities, from single-crop datasets to combined datasets with 15 classes. The proposed model was evaluated on diverse datasets, demonstrating high accuracy, specificity, sensitivity, and F1 scores. Notably, FourCropNet achieved the highest accuracy of 99.7% for Grape, 99.5% for Corn, and 95.3% for the combined dataset. Its scalability and ability to generalize across datasets underscore its robustness. Comparative analysis shows that FourCropNet consistently outperforms state-of-the-art models such as MobileNet, VGG16, and EfficientNet across various metrics. FourCropNet's innovative design and consistent performance make it a reliable solution for real-time disease detection in agriculture. This model has the potential to assist farmers in timely disease diagnosis, reducing economic losses and promoting sustainable agricultural practices.
Ev-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking
Venue: arXiv cs.GR 2025
Year: 2025

This paper presents Ev-Layout, a novel large-scale event-based multi-modal dataset designed for indoor layout estimation and tracking. Ev-Layout makes key contributions to the community by: Utilizing a hybrid data collection platform (with a head-mounted display and VR interface) that integrates both RGB and bio-inspired event cameras to capture indoor layouts in motion. Incorporating time-series data from inertial measurement units (IMUs) and ambient lighting conditions recorded during data collection to highlight the potential impact of motion speed and lighting on layout estimation accuracy. The dataset consists of 2.5K sequences, including over 771.3K RGB images and 10 billion event data points. Of these, 39K images are annotated with indoor layouts, enabling research in both event-based and video-based indoor layout estimation. Based on the dataset, we propose an event-based layout estimation pipeline with a novel event-temporal distribution feature module to effectively aggregate the spatio-temporal information from events. Additionally, we introduce a spatio-temporal feature fusion module that can be easily integrated into a transformer module for fusion purposes. Finally, we conduct benchmarking and extensive experiments on the Ev-Layout dataset, demonstrating that our approach significantly improves the accuracy of dynamic indoor layout estimation compared to existing event-based methods.