Existing LiDAR point cloud (LPC) data coding methods primarily focus on balancing compression efficiency and reconstruction quality according to the human vision system (HVS). However, these methods rarely consider the requirements of downstream scene understanding tasks from the perspective of the machine vision system (MVS). To address this challenge, we explore the maximum degree of LPC compression that has negligible impact on perception accuracy, called LPC-based just recognizable compression distortion (lpcJRCD). Specifically, we introduce a novel point-wise quantization approach for constructing a MVS-based LiDAR dataset and present a new lpcJRCD-guided intelligent compression framework tailored for MVS applications. To enhance MVS-based LPC compression efficiency, we develop a dual-feature interaction (DFI) module that fuses point and voxel features. Additionally, we propose a mask-based loss function to ensure accurate point-wise quality level prediction. Experimental results demonstrate the effectiveness of our proposed model in reducing the average bit rate by up to 94.98% while preserving perception accuracy in autonomous vehicles.

Just recognizable distortion (JRD) has been introduced for image compression for machines, aiming to quantify the maximum coding distortion that can be tolerated by a specific perception model, thereby defining the upper bound of machine vision redundancy (MVR). However, existing JRD-based redundancy estimation methods face three key challenges: limited dataset annotation accuracy, low prediction efficiency, and insufficient perception accuracy, all of which hinder their practical deployment. To address these limitations, we propose a new MVR-Net, a frame-wise efficient JRD prediction method that generates the optimal encoding quantization map in a single inference pass. Furthermore, we refine the annotation standard for JRD datasets based on experimental insights, enhancing the precision of recognizable redundancy measurement. Compared to stateof-the-art methods, MVR-Net achieves a superior balance between bitrate reduction and perception accuracy in JRD-guided compression, while offering up to a 40,000× speed improvement, demonstrating its practicality and efficiency for real-world applications.

Just recognizable distortion (JRD) has emerged as a promising paradigm for machine-centric video coding. However, existing JRD-guided coding methods are limited by coarse annotation granularity and high computational cost, which hinder their deployment. In this paper, we first investigate the impact of different JRD annotation strategies on downstream task performance. By incorporating both instance-level and contextual information, we construct a new JRD dataset with fine-grained annotations compatible with object detection and instance segmentation tasks. To enhance quantization parameter (QP) map prediction while maintaining computational efficiency, we propose a novel spiking neural network (SNN)-based framework that decomposes video frames into spatial structures, channel interactions, and temporal patterns. Furthermore, we introduce a spiking attention mechanism to aggregate task-relevant features and employ adaptive scaling vectors to suppress machine-perceived redundancy, enabling targeted bitrate allocation aligned with task-critical content. Extensive experiments on multiple datasets and backbones demonstrate that our approach consistently outperforms state-of-the-art codec-based and JRD-guided methods in maintaining task performance at ultra-low bitrates, while significantly reducing computational overhead.

Zheng Jia, Shengbin Yue, Wei Chen, Siyuan Wang, Yidong Liu, Zejun Li, Yun Song, Zhongyu Wei. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

With the rapid development of generative models, the visual quality of generated images has become almost indistinguishable from real images, which poses a huge challenge to content authenticity verification. A key limitation of existing detectors is their reliance on model-specific cues, resulting in poor generalization to unseen models. Based on the observation of local differences in the generated images, we found that the generated images lack device-specific sensor noise and unnatural pixel intensity variations caused by the oversimplified generation process. These discrepancies provide important forensic cues for distinguishing between real and generated images. We propose the Feature Aggregation for Localized Context and Noise Network (FALCON-Net), which leverages these discrepancies to enhance detection capabilities. FALCON-Net integrates two complementary modules to enhance detection capabilities: the Intrinsic Noise Pattern Isolation (INP) module isolates device-specific noise patterns by analyzing high-frequency features in the frequency domain, while the Local Variation Pattern (LVP) module models the complex relationships between local pixels to capture directional intensity variations and reveal unnatural regularities in generated images. By combining these sensor-level and local structural cues, FALCON-Net identifies fundamental generative inconsistencies, ensuring robustness to post-processing and strong generalization to unseen models. Extensive experimental results show that FALCON-Net achieves the state-of-the-art performance in detecting generated images and shows good generalization ability to unseen generative models. The code is available at https://github.com/humiaomiaohaha/FALCON-Net.

Conventional tampering localization only extracts features in the image domain, which makes it hard to capture the subtle tampering traces. In this paper, we propose a wavelet convolution and multi-scale attention network (WCMA-Net) for image tampering detection and localization, in which a wavelet convolution module (WCM) branch and a multi-scale attention module (MSAM) branch are integrated following the backbone. In the WCM branch, wavelet decomposition is utilized to enhance high-frequency details and enhance the detection of subtle tampering traces. In the MSAM branch, a multi-scale attention operation is employed to extract global and local features, which are then combined according to their similarity to capture long-range dependencies among pixels. Finally, an adaptive weight strategy is employed to fuse the features from both branches for binary pixel-level tampering mask prediction. Experimental results on various public datasets demonstrate that the proposed method achieves superior precise pixel-level image tampering localization over state-of-the-art methods. Codes and models are available at https://github.com/csust-sonie/WCMA-Net.

LiDAR sensors are widely used in autonomous driving, and the growing storage and transmission demands have made LiDAR point cloud compression (LPCC) a hot research topic. To address the challenges posed by the large-scale and uneven-distribution (spatial and categorical) of LiDAR point data, this paper presents a new multimodal-driven scalable LPCC framework. For the large-scale challenge, we decouple the original LiDAR data into multi-layer point subsets, compress and transmit each layer separately, so as to ensure the reconstruction quality requirement under different scenarios. For the uneven-distribution challenge, we extract, align, and fuse heterologous feature representations, including point modality with position information, depth modality with spatial distance information, and segmentation modality with category information. Extensive experimental results on the benchmark SemanticKITTI database validate that our method outperforms 14 recent representative LPCC methods.

Vision Transformers (ViTs) pose challenges in training and deploying deep models due to their lack of inductive biases. Previous literature integrated the key ingredients (e.g., long-range relations or input-adaptive weights) of ViTs into convolutional neural networks (CNNs) to address these bias issues on large-scale datasets like ImageNet-1K. However, the performance of these key ingredients on small-scale datasets has received little attention. In this paper, we have decomposed large-kernel convolution in a serial-parallel manner to extract multi-scale image features. By integrating them into a gated convolutional architecture, we have constructed a network backbone for the image classification in the small-scale dataset scenario, called SPGNet. Experiments on public small classification benchmark datasets show that SPGNet achieves a Top-1 accuracy of 86.62% on the CIFAR-100 and 76.57% on the Tiny ImageNet. Moreover, we have conducted experiments on the semantic segmentation task, and our method also achieves promising results under the similar architectures and training configurations.

Amino acid substitution rate matrices are fundamental to statistical phylogenetics and evolutionary biology. Estimating them typically requires reconstructed trees for massive amounts of aligned proteins, which poses a major computational bottleneck. In this paper, we develop a near-linear time method to estimate these rate matrices from multiple sequence alignments (MSAs) alone, thereby speeding up computation by orders of magnitude. Our method relies on a near-linear time cherry reconstruction algorithm which we call FastCherries and it can be easily applied to MSAs with millions of sequences. On both simulated and real data, we demonstrate the speed and accuracy of our method as applied to the classical model of protein evolution. By leveraging the unprecedented scalability of our method, we develop a new, rich phylogenetic model called SiteRM, which can estimate a general site-specific rate matrix for each column of an MSA. Remarkably, in variant effect prediction for both clinical and deep mutational scanning data in ProteinGym, we show that despite being an independent-sites model, our SiteRM model outperforms large protein language models that learn complex residue-residue interactions between different sites. We attribute our increased performance to conceptual advances in our probabilistic treatment of evolutionary data and our ability to handle extremely large MSAs. We anticipate that our work will have a lasting impact across both statistical phylogenetics and computational variant effect prediction. FastCherries and SiteRM are implemented in the CherryML package https://github.com/songlab-cal/CherryML.

In versatile video coding (VVC), the quadtree with nested multi-type tree (QTMT) partition module significantly improves encoding performance compared to other coding tools. However, it also introduces notable computational complexity in intra-frame coding, occupying over 90% of the encoding time. This paper presents a fast coding unit (CU) partition method based on texture complexity to achieve a balance between compression efficiency and computational complexity for VVC intra-frame coding. In particular, the texture complexity of CUs is quantitatively measured by the ratio of horizontal to vertical gradient and that of sub-block variances. Firstly, directions with higher texture complexity are identified as unlikely coding modes and eliminated from the candidate set. Next, the subblock variances of binary and ternary partitions are compared to determine a fine-grained CU partition pattern, avoiding unlikely partition modes. Experimental results show that our method is simple but efficient, and achieves higher computation efficiency compared to recent machine learning-based and handcraftedbased methods. The implementation of the proposed method is publicly available athttps://github.com/csust-sonie/fastCU.

Face forgery detection has attracted much attention due to the ever-increasing social concerns caused by facial manipulation techniques. Recently, identity-based detection methods have made considerable progress, which is especially suitable in the celebrity protection scenario. However, they still suffer from two main limitations: (a) generic identity extractor is not specifically designed for forgery detection, leading to nonnegligibleIdentity Representation Biasto forged images. (b) existing methods only analyze the identity representation of each image individually, but ignores the query-reference interaction for inconsistency exploiting. To address these issues, a novelInconsistency Exploiting based Identity Rectification Network(IEIRNet) is proposed in this paper. Firstly, for the identity bias rectification, the IEIRNet follows an effective two-branches structure. Besides theGeneric Identity Extractor(GIE) branch, an essentialBias Diminishing Module(BDM) branch is proposed to eliminate the identity bias through a novelAttention-based Bias Rectification(ABR) component, accordingly acquiring the ultimate discriminative identity representation. Secondly, for query-reference inconsistency exploiting, anInconsistency Exploiting Module(IEM) is applied in IEIRNet to comprehensively exploit the inconsistency clues from both spatial and channel perspectives. In the spatial aspect, an innovative region-aware kernel is derived to activate the local region inconsistency with deep spatial interaction. Afterward in the channel aspect, a coattention mechanism is utilized to model the channel interaction meticulously, and accordingly highlight the channel-wise inconsistency with adaptive weight assignment and channel-wise dropout. Our IEIRNet has shown effectiveness and superiority in various generalization and robustness experiments.

We present CELL-E 2, a novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and vice versa). Protein localization is a challenging problem that requires integrating sequence and image information, which most existing methods ignore. CELL-E 2 extends the work of CELL-E, not only capturing the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling de novo protein design. We train and finetune CELL-E 2 on two large-scale datasets of human proteins. We also demonstrate how to use CELL-E 2 to create hundreds of novel nuclear localization signals (NLS). Results and interactive demos are featured at https://bohuanglab.github.io/CELL-E_2/.

With the prevalence of big-data-driven applications, such as face recognition on smartphones and tailored recommendations from Google Ads, we are on the road to a lifestyle with significantly more intelligence than ever before. Various neural network powered models are running at the back end of their intelligence to enable quick responses to users. Supporting those models requires lots of cloud-based computational resources, e.g., CPUs and GPUs. The cloud providers charge their clients by the amount of resources that they occupy. Clients have to balance the budget and quality of experiences (e.g., response time). The budget leans on individual business owners, and the required Quality of Experience (QoE) depends on usage scenarios of different applications. For instance, an autonomous vehicle requires an real-time response, but unlocking your smartphone can tolerate delays. However, cloud providers fail to offer a QoE-based option to their clients. In this paper, we proposeDQoES, differentiated quality of experience scheduler for deep learning inferences.DQoESaccepts clients’ specifications on targeted QoEs, and dynamically adjusts resources to approach their targets. Through the extensive cloud-based experiments,DQoESdemonstrates that it can schedule multiple concurrent jobs with respect to various QoEs and achieve up to 8x times more satisfied models when compared to the existing system.

Video frame interpolation (VFI) is of great importance for many video applications, yet it is still challenging even in the era of deep learning. Some existing VFI models directly exploit existing lightweight network frameworks, thus making synthesized in-between frames blurry and creating artifacts due to imprecise motion representation. The other existing VFI models typically depend on heavy model architectures with a large number of parameters, preventing them from being deployed on small terminals. To address these issues, we propose a local lightweight VFI network ( L 2 BEC 2 ) that leverages bidirectional encoding structure with channel attention cascade. Specifically, we improve visual quality by introducing a forward and backward encoding structure with channel attention cascade to better characterize motion information. Furthermore, we introduce a local lightweight strategy into the state-of-the-art Adaptive Collaboration of Flows (AdaCoF) model to simplify its model parameters. Compared with the original AdaCoF model, the proposed L 2 BEC 2 obtains performance gain at the cost of only one-third of the number of parameters and performs favorably against the state-of-the-art works on public datasets. Our source code is available at https://github.com/Pumpkin123709/LBEC.git .

Musical genres are labels created to categorize the universe of music. A music genre is characterized by its unique form or style, including instrumentation, rhythmic structure, and harmonic content. It is conventional for a large collection of music to be structured using genre hierarchies. Automatic music genre classification is gaining attention in recent years due to the large amount of available digital music on the web and the latest advances in artificial intelligence. In particular, various deep learning-based approaches have delivered promising results in this domain. In this paper, we present a case study of the PRCNN framework (2017), which parallelizes CNN and bi-directional GRU to capture both spatial and temporal signals from music spectrograms. In our study, we designed our model based on the proposed concept but with a different model structure. Furthermore, we trained and evaluated our model using a more comprehensive dataset (FMA) with 8,252 pieces of music and 17 genres. We further validated our model on a curated dataset of 15 songs. Our model achieves an overall accuracy of 88% on the FMA dataset with above 90% accuracies in four genre categories. For the curated dataset, the model correctly classified 11 out of the 15 songs. Our experimental results provide convincing support for utilizing parallelized deep neural networks to model the concurrent spatial and temporal characteristics of music data.

With the increasing number of electric vehicles (EVs), temporary charging demands grow rapidly. Unlike charging at home or workplace, temporary charging requires less waiting time. In this article, a mobile edge computing (MEC)-enabled charging and discharging networking system algorithm (CDNSA) is proposed to minimize the waiting time for EVs in charging stations (CSs). A software-defined network (SDN) paradigm is adopted to enhance the data transmission efficiency for MEC servers. In CDNSA, the optimization problem is formulated as a mixed-integer nonlinear programming (MINLP). A heuristic algorithm is proposed to solve the optimal CS selection variables for EVs that needs to be charged (EVCs) and EVs that can be discharged (EVDs), and then a remaining problem nonlinear programming (NLP) is obtained. By verifying the convexity of each continuous variable, the NLP is solved by adopting the block coordinate descent (BCD) method. In simulation, the optimality of CDNSA is verified by comparing with the exhaustive algorithm in terms of minimizing maximal waiting time (MMWT) of CSs. We also compare CDNSA with other benchmarks to illustrate its advantage.

Multiauthority ciphertext-policy attribute-based encryption (MA-CP-ABE) is a promising technique for secure data sharing in cloud storage. As multiple users with same attributes have same decryption privilege in MA-CP-ABE, the identity of the decryption key owner cannot be accurately traced by the exposed decryption key. This will lead to the key abuse problem, for example, the malicious users may sell their decryption keys to others. In this paper, we first present a traceable MA-CP-ABE scheme supporting fast access and malicious users’ accountability. Then, we prove that the proposed scheme is adaptively secure under the symmetric external Diffie–Hellman assumption and fully traceable under the q -Strong Diffie–Hellman assumption. Finally, we design a traceable and revocable MA-CP-ABE system for secure and efficient cloud storage from the proposed scheme. When a malicious user leaks his decryption key, our proposed system can not only confirm his identity but also revoke his decryption privilege. Extensive efficiency analysis results indicate that our system requires only constant number of pairing operations for ciphertext data access.

In this study, a perceptual encryption algorithm is proposed for H.264/AVC video to enhance the scrambling effect and encryption space. Six new scan orders are designed for H.264/AVC encoder by analysing the energy distribution of discrete cosine transform coefficients. They are proven to have similar performance as the conventional zigzag scan order and its symmetrical scan order. These six new scan orders are combined with two existing scan orders to design a scan‐order based perceptual encryption algorithm. Specifically, video encryption is achieved more specifically by randomly selecting one scan order from the eight scan orders with a security key, and the sign bit flipping of DC coefficients is also incorporated to further increase the encryption space. Experimental results show that the proposed approach has the advantages of both low bitrate increase and low computational cost. Furthermore, it is more flexible and has stronger security than the existing scan‐order based video encryption schemes.

ABSTRACT There are several methods to construct multi‐secret sharing schemes, one of which is based on coding theory. Generally, however, it is very hard to determine the minimal access structures of the schemes based on linear codes. In this paper, we first propose the concept of minimal linear codes so as to make it easier to determine the access structures of the schemes based on the duals of minimal linear codes. It is proved that the shortening codes of minimal linear codes are also minimal ones. Then we present the algorithm to determine whether a class of linear codes are minimal. On the basis of our aforementioned studies, we further devise a new multi‐use multi‐secret sharing scheme based on the dual code of a minimal linear code, where each participant has to carry only one share. Furthermore, we study the minimal access structures of the multi‐secret sharing scheme and present specific examples through programming. Copyright © 2014 John Wiley & Sons, Ltd.

The full search scheme employed in H.264/AVC significantly improves the coding performance, but it also introduces a very high computational complexity which limits the applications in resource‐constrained mobile devices. In this study, the authors firstly present a discretisation total variation and orientation gradient‐based hierarchical intra‐prediction mode decision method for mobile video applications. By shrinking the candidate mode set in the rate–distortion optimisation (RDO) process, the proposed algorithm reduces the computational complexity and power consumption of the encoder. Furthermore, they extend the hierarchical algorithm to a complexity scalable version in which the coding complexity is measured on five levels by reserving various numbers of modes for RDO. Experimental results demonstrate that the proposed mode decision algorithm reduces the coding complexity significantly with negligible performance degradation and the proposed complexity scalable algorithm is effective and efficient for mobile video application.