OpenRaiser/Intern-Atlas huggingface dataset:
Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing
Venue: arXiv cs.CV 2025
Year: 2025

Visual Commonsense Reasoning, which is regarded as one challenging task to pursue advanced visual scene comprehension, has been used to diagnose the reasoning ability of AI systems. However, reliable reasoning requires a good grasp of the scene's details. Existing work fails to effectively exploit the real-world object relationship information present within the scene, and instead overly relies on knowledge from training memory. Based on these observations, we propose a novel scene-graph-enhanced visual commonsense reasoning generation method named \textit{\textbf{G2}}, which first utilizes the image patches and LLMs to construct a location-free scene graph, and then answer and explain based on the scene graph's information. We also propose automatic scene graph filtering and selection strategies to absorb valuable scene graph information during training. Extensive experiments are conducted on the tasks and datasets of scene graph constructing and visual commonsense answering and explaining, respectively. Experimental results and ablation analysis demonstrate the effectiveness of our proposed framework.
Inferring Transition Dynamics from Value Functions
Venue: arXiv cs.LG 2025
Year: 2025

In reinforcement learning, the value function is typically trained to solve the Bellman equation, which connects the current value to future values. This temporal dependency hints that the value function may contain implicit information about the environment's transition dynamics. By rearranging the Bellman equation, we show that a converged value function encodes a model of the underlying dynamics of the environment. We build on this insight to propose a simple method for inferring dynamics models directly from the value function, potentially mitigating the need for explicit model learning. Furthermore, we explore the challenges of next-state identifiability, discussing conditions under which the inferred dynamics model is well-defined. Our work provides a theoretical foundation for leveraging value functions in dynamics modeling and opens a new avenue for bridging model-free and model-based reinforcement learning.
Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of molecular activities is crucial for efficient drug discovery, yet remains challenging due to limited and noisy datasets. We introduce Similarity-Quantized Relative Learning (SQRL), a learning framework that reformulates molecular activity prediction as relative difference learning between structurally similar pairs of compounds. SQRL uses precomputed molecular similarities to enhance training of graph neural networks and other architectures, and significantly improves accuracy and generalization in low-data regimes common in drug discovery. We demonstrate its broad applicability and real-world potential through benchmarking on public datasets as well as proprietary industry data. Our findings demonstrate that leveraging similarity-aware relative differences provides an effective paradigm for molecular activity prediction.
Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product
Venue: arXiv cs.CV 2025
Year: 2025

Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts. Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent time-series images of the ground regardless of weather or illumination conditions. Despite SAR's potential for disturbance mapping, processing SAR data to an analysis-ready format requires expertise and significant compute resources, particularly for large-scale global analysis. In October 2023, NASA's Observational Products for End-Users from Remote Sensing Analysis (OPERA) project released the near-global Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) dataset, providing publicly available, analysis-ready SAR imagery. In this work, we utilize this new dataset to systematically analyze land surface disturbances. As labeling SAR data is often prohibitively time-consuming, we train a self-supervised vision transformer - which requires no labels to train - on OPERA RTC-S1 data to estimate a per-pixel distribution from the set of baseline imagery and assess disturbances when there is significant deviation from the modeled distribution. To test our model's capability and generality, we evaluate three different natural disasters - which represent high-intensity, abrupt disturbances - from three different regions of the world. Across events, our approach yields high quality delineations: F1 scores exceeding 0.6 and Areas Under the Precision-Recall Curve exceeding 0.65, consistently outperforming existing SAR disturbance methods. Our findings suggest that a self-supervised vision transformer is well-suited for global disturbance mapping and can be a valuable tool for operational, near-global disturbance monitoring, particularly when labeled data does not exist.
Towards Understanding Extrapolation: a Causal Lens
Venue: arXiv cs.LG 2025
Year: 2025

Canonical work handling distribution shifts typically necessitates an entire target distribution that lands inside the training distribution. However, practical scenarios often involve only a handful of target samples, potentially lying outside the training support, which requires the capability of extrapolation. In this work, we aim to provide a theoretical understanding of when extrapolation is possible and offer principled methods to achieve it without requiring an on-support target distribution. To this end, we formulate the extrapolation problem with a latent-variable model that embodies the minimal change principle in causal mechanisms. Under this formulation, we cast the extrapolation problem into a latent-variable identification problem. We provide realistic conditions on shift properties and the estimation objectives that lead to identification even when only one off-support target sample is available, tackling the most challenging scenarios. Our theory reveals the intricate interplay between the underlying manifold's smoothness and the shift properties. We showcase how our theoretical results inform the design of practical adaptation algorithms. Through experiments on both synthetic and real-world data, we validate our theoretical findings and their practical implications.
Detecting Vulnerabilities in Encrypted Software Code while Ensuring Code Privacy
Venue: arXiv cs.SE 2025
Year: 2025

Software vulnerabilities continue to be the main cause of occurrence for cyber attacks. In an attempt to reduce them and improve software quality, software code analysis has emerged as a service offered by companies specialising in software testing. However, this service requires software companies to provide access to their software's code, which raises concerns about code privacy and intellectual property theft. This paper presents a novel approach to Software Quality and Privacy, in which testing companies can perform code analysis tasks on encrypted software code provided by software companies while code privacy is preserved. The approach combines Static Code Analysis and Searchable Symmetric Encryption in order to process the source code and build an encrypted inverted index that represents its data and control flows. The index is then used to discover vulnerabilities by carrying out static analysis tasks in a confidential way. With this approach, this paper also defines a new research field -- Confidential Code Analysis --, from which other types of code analysis tasks and approaches can be derived. We implemented the approach in a new tool called CoCoA and evaluated it experimentally with synthetic and real PHP web applications. The results show that the tool has similar precision as standard (non-confidential) static analysis tools and a modest average performance overhead of 42.7%.
3D Printed Maps and Icons for Inclusion: Testing in the Wild by People who are Blind or have Low Vision
Venue: arXiv cs.HC 2025
Year: 2025

The difficulty and consequent fear of travel is one of the most disabling consequences of blindness and severe vision impairment, affecting confidence and quality of life. Traditional tactile graphics are vital in the Orientation and Mobility training process, however 3D printing may have the capacity to enable production of more meaningful and inclusive maps. This study explored the use of 3D printed maps on site at a public event to examine their suitability and to identify guidelines for the design of future 3D maps. An iterative design process was used in the production of the 3D maps, with feedback from visitors who are blind or have low vision informing the recommendations for their design and use. For example, it was found that many representational 3D icons could be recognised by touch without the need for a key and that such a map helped form mental models of the event space. Complex maps, however, require time to explore and should be made available before an event or at the entrance in a comfortable position. The maps were found to support the orientation and mobility process, and importantly to also promote a positive message about inclusion and accessibility.
Surgical Visual Understanding (SurgVU) Dataset
Venue: arXiv cs.CV 2025
Year: 2025

Owing to recent advances in machine learning and the ability to harvest large amounts of data during robotic-assisted surgeries, surgical data science is ripe for foundational work. We present a large dataset of surgical videos and their accompanying labels for this purpose. We describe how the data was collected and some of its unique attributes. Multiple example problems are outlined. Although the dataset was curated for a particular set of scientific challenges (in an accompanying paper), it is general enough to be used for a broad range machine learning questions. Our hope is that this dataset exposes the larger machine learning community to the challenging problems within surgical data science, and becomes a touchstone for future research. The videos are available at https://storage.googleapis.com/isi-surgvu/surgvu24_videos_only.zip, the labels at https://storage.googleapis.com/isi-surgvu/surgvu24_labels_updated_v2.zip, and a validation set for tool detection problem at https://storage.googleapis.com/isi-surgvu/cat1_test_set_public.zip.
Provenance Guided Rollback Suggestions
Venue: arXiv cs.LO 2025
Year: 2025

Advances in incremental Datalog evaluation strategies have made Datalog popular among use cases with constantly evolving inputs such as static analysis in continuous integration and deployment pipelines. As a result, new logic programming debugging techniques are needed to support these emerging use cases. This paper introduces an incremental debugging technique for Datalog, which determines the failing changes for a \emph{rollback} in an incremental setup. Our debugging technique leverages a novel incremental provenance method. We have implemented our technique using an incremental version of the Soufflé Datalog engine and evaluated its effectiveness on the DaCapo Java program benchmarks analyzed by the Doop static analysis library. Compared to state-of-the-art techniques, we can localize faults and suggest rollbacks with an overall speedup of over 26.9$\times$ while providing higher quality results.
Practical Spoofing Attacks on Galileo Open Service Navigation Message Authentication
Venue: arXiv cs.CR 2025
Year: 2025

This paper examines the Galileo Open Service Navigation Message Authentication (OSNMA) and, for the first time, discovers two critical vulnerabilities, namely artificially-manipulated time synchronization (ATS) and interruptible message authentication (IMA). ATS allows attackers falsify a receiver's signals and/or local reference time (LRT) while still fulfilling the time synchronization (TS) requirement. IMA allows temporary interruption of the navigation data authentication process due to the reception of a broken message (probably caused by spoofing attacks) and restores the authentication later. By exploiting the ATS vulnerability, we propose a TS-comply replay (TSR) attack with two variants (real-time and non-real-time), where attackers replay signals to a victim receiver while strictly complying with the TS rule. We further propose a TS-comply forgery (TSF) attack, where attackers first use a previously-disclosed key to forge a message based on the OSNMA protocol, then tamper with the vitcim receiver's LRT correspondingly to comply with the TS rule and finally transmit the forged message to the receiver. Finally, we propose a concatenating replay (CR) attack based on the IMA vulnerability, where attackers concatenate replayed signals to the victim receiver's signals in a way that still enables correct verification of the navigation data in the replayed signals. To validate the effectiveness of the proposed attacks, we conduct real-world experiments with a commercial Galileo receiver manufactured by Septentrio, two software-defined radio (SDR) devices, open-source Galileo-SDR-SIM and OSNMAlib software. The results showed that all the attacks can successfully pass the OSNMA scheme and the TSF attack can spoof receivers to arbitrary locations.
OpticFusion: Multi-Modal Neural Implicit 3D Reconstruction of Microstructures by Fusing White Light Interferometry and Optical Microscopy
Venue: arXiv cs.CV 2025
Year: 2025

White Light Interferometry (WLI) is a precise optical tool for measuring the 3D topography of microstructures. However, conventional WLI cannot capture the natural color of a sample's surface, which is essential for many microscale research applications that require both 3D geometry and color information. Previous methods have attempted to overcome this limitation by modifying WLI hardware and analysis software, but these solutions are often costly. In this work, we address this challenge from a computer vision multi-modal reconstruction perspective for the first time. We introduce OpticFusion, a novel approach that uses an additional digital optical microscope (OM) to achieve 3D reconstruction with natural color textures using multi-view WLI and OM images. Our method employs a two-step data association process to obtain the poses of WLI and OM data. By leveraging the neural implicit representation, we fuse multi-modal data and apply color decomposition technology to extract the sample's natural color. Tested on our multi-modal dataset of various microscale samples, OpticFusion achieves detailed 3D reconstructions with color textures. Our method provides an effective tool for practical applications across numerous microscale research fields. The source code and our real-world dataset are available at https://github.com/zju3dv/OpticFusion.
Text-guided Synthetic Geometric Augmentation for Zero-shot 3D Understanding
Venue: arXiv cs.CV 2025
Year: 2025

Zero-shot recognition models require extensive training data for generalization. However, in zero-shot 3D classification, collecting 3D data and captions is costly and laborintensive, posing a significant barrier compared to 2D vision. Recent advances in generative models have achieved unprecedented realism in synthetic data production, and recent research shows the potential for using generated data as training data. Here, naturally raising the question: Can synthetic 3D data generated by generative models be used as expanding limited 3D datasets? In response, we present a synthetic 3D dataset expansion method, Textguided Geometric Augmentation (TeGA). TeGA is tailored for language-image-3D pretraining, which achieves SoTA in zero-shot 3D classification, and uses a generative textto-3D model to enhance and extend limited 3D datasets. Specifically, we automatically generate text-guided synthetic 3D data and introduce a consistency filtering strategy to discard noisy samples where semantics and geometric shapes do not match with text. In the experiment to double the original dataset size using TeGA, our approach demonstrates improvements over the baselines, achieving zeroshot performance gains of 3.0% on Objaverse-LVIS, 4.6% on ScanObjectNN, and 8.7% on ModelNet40. These results demonstrate that TeGA effectively bridges the 3D data gap, enabling robust zero-shot 3D classification even with limited real training data and paving the way for zero-shot 3D vision application.
SEAL: Entangled White-box Watermarks on Low-Rank Adaptation
Venue: arXiv cs.AI 2025
Year: 2025

Recently, LoRA and its variants have become the de facto strategy for training and sharing task-specific versions of large pretrained models, thanks to their efficiency and simplicity. However, the issue of copyright protection for LoRA weights, especially through watermark-based techniques, remains underexplored. To address this gap, we propose SEAL (SEcure wAtermarking on LoRA weights), the universal whitebox watermarking for LoRA. SEAL embeds a secret, non-trainable matrix between trainable LoRA weights, serving as a passport to claim ownership. SEAL then entangles the passport with the LoRA weights through training, without extra loss for entanglement, and distributes the finetuned weights after hiding the passport. When applying SEAL, we observed no performance degradation across commonsense reasoning, textual/visual instruction tuning, and text-to-image synthesis tasks. We demonstrate that SEAL is robust against a variety of known attacks: removal, obfuscation, and ambiguity attacks.
Creating Virtual Environments with 3D Gaussian Splatting: A Comparative Study
Venue: arXiv cs.CV 2025
Year: 2025

3D Gaussian Splatting (3DGS) has recently emerged as an innovative and efficient 3D representation technique. While its potential for extended reality (XR) applications is frequently highlighted, its practical effectiveness remains underexplored. In this work, we examine three distinct 3DGS-based approaches for virtual environment (VE) creation, leveraging their unique strengths for efficient and visually compelling scene representation. By conducting a comparable study, we evaluate the feasibility of 3DGS in creating immersive VEs, identify its limitations in XR applications, and discuss future research and development opportunities.
Shape-Based Single Object Classification Using Ensemble Method Classifiers
Venue: arXiv cs.CV 2025
Year: 2025

Nowadays, more and more images are available. Annotation and retrieval of the images pose classification problems, where each class is defined as the group of database images labelled with a common semantic label. Various systems have been proposed for content-based retrieval, as well as for image classification and indexing. In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-category image classification. A well known pre-processing and post-processing method was used and applied to three problems; image segmentation, object identification and image classification. The method was applied to classify single object images from Amazon and Google datasets. The classification was tested for four different classifiers; BayesNetwork (BN), Random Forest (RF), Bagging and Vote. The estimated classification accuracies ranged from 20% to 99% (using 10-fold cross validation). The Bagging classifier presents the best performance, followed by the Random Forest classifier.
Jodes: Efficient Oblivious Join in the Distributed Setting
Venue: arXiv cs.CR 2025
Year: 2025

Trusted execution environment (TEE) has provided an isolated and secure environment for building cloud-based analytic systems, but it still suffers from access pattern leakages caused by side-channel attacks. To better secure the data, computation inside TEE enclave should be made oblivious, which introduces significant overhead and severely slows down the computation. A natural way to speed up is to build the analytic system with multiple servers in the distributed setting. However, this setting raises a new security concern -- the volumes of the transmissions among these servers can leak sensitive information to a network adversary. Existing works have designed specialized algorithms to address this concern, but their supports for equi-join, one of the most important but non-trivial database operators, are either inefficient, limited, or under a weak security assumption. In this paper, we present Jodes, an efficient oblivious join algorithm in the distributed setting. Jodes prevents the leakage on both the network and enclave sides, supports a general equi-join operation, and provides a high security level protection that only publicizes the input sizes and the output size. Meanwhile, it achieves both communication cost and computation cost asymptotically superior to existing algorithms. To demonstrate the practicality of Jodes, we conduct experiments in the distributed setting comprising 16 servers. Empirical results show that Jodes achieves up to a sixfold performance improvement over state-of-the-art join algorithms.
Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning
Venue: arXiv cs.CV 2025
Year: 2025

Few-shot class incremental learning implies the model to learn new classes while retaining knowledge of previously learned classes with a small number of training instances. Existing frameworks typically freeze the parameters of the previously learned classes during the incorporation of new classes. However, this approach often results in suboptimal class separation of previously learned classes, leading to overlap between old and new classes. Consequently, the performance of old classes degrades on new classes. To address these challenges, we propose a novel feature augmentation driven contrastive learning framework designed to enhance the separation of previously learned classes to accommodate new classes. Our approach involves augmenting feature vectors and assigning proxy labels to these vectors. This strategy expands the feature space, ensuring seamless integration of new classes within the expanded space. Additionally, we employ a self-supervised contrastive loss to improve the separation between previous classes. We validate our framework through experiments on three FSCIL benchmark datasets: CIFAR100, miniImageNet, and CUB200. The results demonstrate that our Feature Augmentation driven Contrastive Learning framework significantly outperforms other approaches, achieving state-of-the-art performance.
PICE: A Semantic-Driven Progressive Inference System for LLM Serving in Cloud-Edge Networks
Venue: arXiv cs.DC 2025
Year: 2025

Large language models (LLMs), while driving a new wave of interactive AI applications across numerous domains, suffer from high inference costs and heavy cloud dependency. Motivated by the redundancy phenomenon in linguistics, we propose a progressive inference paradigm over cloud and edge, i.e., firstly generating the sketch of the answer by LLMs at cloud, and then conducting parallel extension to fill in details by small models (SLMs) at edge. Progressive inference offers potential benefits to improve throughput and reduce inference latency while facing key implementation challenges, including decreased response quality from SLMs, a tradeoff between the brevity and comprehensiveness of sketches, as well as increased latency caused by network transmission and edge inference. In this work, we propose and implement PICE, an LLM serving system with semantic-level cloud-edge collaboration, enhancing inference throughput and quality through dynamic inference task scheduling, ensemble learning, and parallel edge inference. Extensive testbed experiments illustrate that our approach achieves $1.5-2\times$ throughput enhancement and up to 43% latency reduction, while also potentially enhancing the quality compared to SOTA systems.