论文检索

Baoyue Hu, Yang Wei, Junhao Xiao, Wendong Huang, Xiuli Bi, Bin Xiao

To defend against personalized generation, a new form of infringement that is more concealed and destructive, the existing copyright protection methods is to add adversarial perturbations in images. However, these methods focus solely on countering illegal personalization, neglecting the requirement for legitimate personalization. Moreover, none of these methods are capable of directly verifying and tracing the copyright from adversarial examples. In response to these limitations, we propose a traceable and authorizable copyright traceability method that embeds the copyright watermark into images through a series of invertible compound coupling modules. We introduce a novel information exchange mechanism for invertible neural network and design a contrastive learning-based optimization strategy tailored to address personalized infringement issues. Our method effectively mitigates the malicious use of unauthorized personalized generation models by inducing watermark-like artifacts and obscuring privacy details in generated images. Additionally, it facilitates copyright traceability and supports authorized legitimate personalization, thereby offering broader practical applicability. Experimental results demonstrate that our method can almost losslessly restore original image and extract copyright watermark, achieving FID exceeding 300 and causing visually noticeable artifacts in unauthorized personalized images. Furthermore, it exhibits consistent robustness against adversarial purification and text prompt modifications.

Zhiyuan Fang, Rengan Xie, Xuancheng Jin, Qi Ye, Wei Chen, Wenting Zheng, Rui Wang, Yuchi Huo

Recently, the field of 3D scene stylization has attracted considerable attention, particularly for applications in the metaverse. A key challenge is rapidly transferring the style of an arbitrary reference image to a 3D scene while faithfully preserving its content structure and spatial layout. Works leveraging implicit representations with gradient-based optimization achieve impressive style transfer results, yet the lengthy processing time per individual style makes rapid switching impractical. In this paper, we propose A^3GS, a novel feed-forward neural network for zero-shot 3DGS stylization that enables transferring any image style to arbitrary 3D scenes in just 10 seconds without the need for per-style optimization. Our work introduces a Graph Convolutional Network (GCN)-based autoencoder aimed at efficient feature aggregation and decoding of spatially structured 3D Gaussian scenes. The encoder converts 3DGS scenes into a latent space. Furthermore, for the latent space, we utilize Adaptive Instance Normalization (AdaIN) to inject features from the target style image into the 3D Gaussian scene. Finally, we constructed a 3DGS dataset using a generative model and proposed a two-stage training strategy for A^3GS. Owing to the feed-forward design, our framework can perform fast style transfer on large-scale 3DGS scenes, which poses a severe challenge to the memory consumption of optimization-based methods. Extensive experiments demonstrate that our approach achieves high-quality, consistent 3D stylization in seconds.

Mingxuan Wu, Huang Huang, Justin Kerr, Chung Min Kim, Anthony Zhang, Brent Yi, Angjoo Kanazawa

Whether snipping with scissors or opening a box, humans can quickly understand the 3D configurations of familiar objects. For novel objects, we can resort to long-form inspection to build intuition. The more we observe the object, the better we get at predicting its 3D state immediately. Existing systems, however, are limited to either optimizing underlying representations from multi-view observations or training a feed-forward predictor from supervised datasets. We introduce Predict-Optimize-Distill (POD), a self-improving framework that interleaves prediction and optimization in a mutually reinforcing cycle to achieve better 4D object understanding with increasing observation time. Given a multi-view object scan and a long-form monocular video of human-object interaction, POD iteratively trains a neural network to predict local part poses from RGB frames, uses this predictor to initialize a global optimization which refines output poses through inverse rendering, then finally distills the results of optimization back into the model by generating synthetic self-labeled training data from novel viewpoints. Each iteration improves both the predictive model and the optimized motion trajectory, creating a virtuous cycle that bootstraps its own training data to learn about the pose configurations of an object. We also introduce a quasi-multiview mining strategy for reducing depth ambiguity by leveraging long video. We evaluate POD on 14 real-world and 5 synthetic objects with various joint types, including revolute and prismatic joints as well as multi-body configurations where parts detach or reattach independently. POD demonstrates significant improvement over a pure optimization baseline which gets stuck in local minima, particularly for longer videos. We also find that POD's performance improves with both video length and successive iterations of the self-improving cycle, highlighting its ability to scale performance with additional observations and compute.

Nam Duong Tran, Nam Nguyen Phuong, Hieu H. Pham, Phi Le Nguyen, My T. Thai

Deep neural networks often suffer performance drops when test data distribution differs from training data. Domain Generalization (DG) aims to address this by focusing on domain-invariant features or augmenting data for greater diversity. However, these methods often struggle with limited training domains or significant gaps between seen (training) and unseen (test) domains. To enhance DG robustness, we hypothesize that it is essential for the model to be trained on data from domains that closely resemble unseen test domains--an inherently difficult task due to the absence of prior knowledge about the unseen domains. Accordingly, we propose ConstStyle, a novel approach that leverages a unified domain to capture domain-invariant features and bridge the domain gap with theoretical analysis. During training, all samples are mapped onto this unified domain, optimized for seen domains. During testing, unseen domain samples are projected similarly before predictions. By aligning both training and testing data within this unified domain, ConstStyle effectively reduces the impact of domain shifts, even with large domain gaps or few seen domains. Extensive experiments demonstrate that ConstStyle consistently outperforms existing methods across diverse scenarios. Notably, when only a limited number of seen domains are available, ConstStyle can boost accuracy up to 19.82% compared to the next best approach.

Hailong Yan, Ao Li, Xiangtao Zhang, Zhe Liu, Zenglin Shi, Ce Zhu, Le Zhang

Recent advancements in deep neural networks have driven significant progress in image enhancement (IE). However, deploying deep learning models on resource-constrained platforms, such as mobile devices, remains challenging due to high computation and memory demands. To address these challenges and facilitate real-time IE on mobile, we introduce an extremely lightweight Convolutional Neural Network (CNN) framework with around 4K parameters. Our approach integrates re-parameterization with an Incremental Weight Optimization strategy to ensure efficiency. Additionally, we enhance performance with a Feature Self-Transform module and a Hierarchical Dual-Path Attention mechanism, optimized with a Local Variance-Weighted loss. With this efficient framework, we are the first to achieve real-time IE inference at up to 1,100 frames per second (FPS) while delivering competitive image quality, achieving the best trade-off between speed and performance across multiple IE tasks. The code will be available at https://github.com/AVC2-UESTC/MobileIE.git.

Ziyun Wang, Ruijun Zhang, Zi-Yan Liu, Yufu Wang, Kostas Daniilidis

This paper addresses the challenges of estimating a continuous-time field from a stream of events. Existing Human Mesh Recovery (HMR) methods rely predominantly on frame-based approaches, which are prone to aliasing and inaccuracies due to limited temporal resolution and motion blur. In this work, we predict a continuous-time human motion field from events caused by human motion. Prior state-of-the-art methods rely on computationally intensive optimization across a fixed number of poses at high frame rates, which becomes prohibitively expensive as we increase the temporal resolution. In comparison, our model leverages a recurrent feed-forward neural network to predict human motion in the latent space of possible human motions. We present the first work that replaces traditional event volume-based discrete-time pre-dictions with a continuous human motion field represented as a time-implicit function, enabling parallel pose queries at arbitrary temporal resolutions. To advance the evaluation of continuous-time human pose estimation, we introduce the Beam-splitter Event Agile Human Motion Dataset--a hardware-synchronized high-speed human dataset tailored for this purpose. EvHuman improves joint errors by 23.8 % compared to previous event human methods, while reducing the computational time by 69%.

Yooshin Cho, Hanbyel Cho, Janghyeon Lee, HyeongGwon Hong, Jaesung Ahn, Junmo Kim

As the use of artificial intelligence rapidly increases, the development of trustworthy artificial intelligence has become important. However, recent studies have shown that deep neural networks are susceptible to learn spurious correlations present in datasets. To improve the reliability, we propose a simple yet effective framework called controllable feature whitening. We quantify the linear correlation between the target and bias features by the covariance matrix, and eliminate it through the whitening module. Our results systemically demonstrate that removing the linear correlations between features fed into the last linear classifier significantly mitigates the bias, while avoiding the need to model intractable higher-order dependencies. A particular advantage of the proposed method is that it does not require regularization terms or adversarial learning, which often leads to unstable optimization in practice. Furthermore, we show that two fairness criteria, demographic parity and equalized odds, can be effectively handled by whitening with the re-weighted covariance matrix. Consequently, our method controls the trade-off between the utility and fairness of algorithms by adjusting the weighting coefficient. Finally, we validate that our method outperforms existing approaches on four benchmark datasets: Corrupted CIFAR-10, Biased FFHQ, WaterBirds, and Celeb-A.

Xu Cao, Takafumi Taketomi

We propose a neural inverse rendering approach that jointly reconstructs geometry, spatially varying reflectance, and lighting conditions from multi-view images captured under varying directional lighting. Unlike prior multi-view photometric stereo methods that require light calibration or intermediate cues such as per-view normal maps, our method jointly optimizes all scene parameters from raw images in a single stage. We represent both geometry and reflectance as neural implicit fields and apply shadow-aware volume rendering. A spatial network first predicts the signed distance and a reflectance latent code for each scene point. A reflectance network then estimates reflectance values conditioned on the latent code and angularly encoded surface normal, view, and light directions. The proposed method outperforms state-of-the-art normal-guided approaches in shape and lighting estimation accuracy, generalizes to view-unaligned multi-light images, and handles objects with challenging geometry and reflectance.

Tianrui Lou, Xiaojun Jia, Siyuan Liang, Jiawei Liang, Ming Zhang, Yanjun Xiao, Xiaochun Cao

Physical adversarial attack methods expose the vulnerabilities of deep neural networks and pose a significant threat to safety-critical scenarios such as autonomous driving. Camouflage-based physical attack is a more promising approach compared to the patch-based attack, offering stronger adversarial effectiveness in complex physical environments. However, most prior work relies on mesh priors of the target object and virtual environments constructed by simulators, which are time-consuming to obtain and inevitably differ from the real world. Moreover, due to the limitations of the backgrounds in training images, previous methods often fail to produce multi-view robust adversarial camouflage and tend to fall into sub-optimal solutions. Due to these reasons, prior work lacks adversarial effectiveness and robustness across diverse viewpoints and physical environments. We propose a physical attack framework based on 3D Gaussian Splatting (3DGS), named PGA, which provides rapid and precise reconstruction with few images, along with photo-realistic rendering capabilities. Our framework further enhances cross-view robustness and adversarial effectiveness by preventing mutual and self-occlusion among Gaussians and employing a min-max optimization approach that adjusts the imaging background of each viewpoint, helping the algorithm filter out non-robust adversarial features. Extensive experiments validate the effectiveness and superiority of PGA. Our code is available at: https://github.com/TRLou/PGA.

Peng Liao, Xilu Wang, Yaochu Jin, Wenli Du, Han Hu

Neural Architecture Search (NAS) has gained significant attention in personalized federated learning (PFL) due to its ability to automatically design tailored models for individual clients. While most existing NAS approaches for PFL perform architecture search on the server side, client-side NAS--where architectures are optimized locally on clients--offers stronger privacy protection by eliminating the need to transmit sensitive model information. However, this paradigm remains underexplored and often suffers from suboptimal average client performance, primarily due to two limitations: (1) Inefficient client-side search strategies caused by data isolation and restricted access to local architectures across clients, and (2) slow supernet convergence arising from server aggregation and local supernet training. To address these challenges, we propose a Personalized Federated Stochastic Differential Equation-based NAS (PerFedSDE-NAS). To achieve effective local search, each client employs a guided diffusion model to generate promising personalized architectures tailored to local data characteristics, while a performance predictor based on radial basis functions is used to select only the most promising subset of architectures for evaluation. To accelerate supernet convergence, each client maintains a supernet with an archive-driven training mechanism, and a novel model aggregation strategy is proposed to further enhance weight convergence during FL rounds. We validate PerFedSDE-NAS across three NAS search spaces, including convolutional neural networks and transformers, demonstrating its broad applicability. Compared to traditional fixed-model and NAS-based PFL approaches, our method achieves the state-of-the-art performance.

Yujia Tong, Yuze Wang, Jingling Yuan, Chuang Hu

Model quantization enables efficient deployment of deep neural networks on edge devices through low-bit parameter representation, yet raises critical challenges for implementing machine unlearning (MU) under data privacy regulations. Existing MU methods designed for full-precision models fail to address two fundamental limitations in quantized networks: 1) Noise amplification from label mismatch during data processing, and 2) Gradient imbalance between forgotten and retained data during training. These issues are exacerbated by quantized models' constrained parameter space and discrete optimization. We propose Q-MUL, the first dedicated unlearning framework for quantized models. Our method introduces two key innovations: 1) Similar Labels assignment replaces random labels with semantically consistent alternatives to minimize noise injection, and 2) Adaptive Gradient Reweighting dynamically aligns parameter update contributions from forgotten and retained data. Through systematic analysis of quantized model vulnerabilities, we establish theoretical foundations for these mechanisms. Extensive evaluations on benchmark datasets demonstrate Q-MUL's superiority over existing approaches.

Seokho Han, Seoyeon Yoon, Jinhee Kim, Dongwei Wang, Kang Eun Jeon, Huanrui Yang, Jong Hwan Ko

As deep neural networks (DNNs) see increased deployment on mobile and edge devices, optimizing model efficiency has become crucial. Mixed-precision quantization is widely favored, as it offers a superior balance between efficiency and accuracy compared to uniform quantization. However, finding the optimal precision for each layer is challenging. Recent studies utilizing bit-level sparsity have shown promise, yet they often introduce substantial training complexity and high GPU memory requirements. In this paper, we propose Memory-Efficient Bit Sparsification Quantization (MSQ), a novel approach that addresses these limitations. MSQ applies a round-clamp quantizer to enable differentiable computation of the least significant bits (LSBs) from model weights. It further employs regularization to induce sparsity in these LSBs, enabling effective precision reduction without explicit bit-level parameter splitting. Additionally, MSQ incorporates Hessian information, allowing the simultaneous pruning of multiple LSBs to further enhance training efficiency. Experimental results show that MSQ achieves up to 8.00x reduction in trainable parameters and up to 86% reduction in training time compared to previous bit-level quantization, while maintaining competitive accuracy and compression rates. This makes it a practical solution for training efficient DNNs on resource-constrained devices.

Jingyang Li, Kuangyu Ding, Kim-Chuan Toh, Pan Zhou

Preconditioned stochastic optimization algorithms, exemplified by Shampoo, outperform first-order optimizers by offering theoretical convergence benefits and practical gains in large-scale neural network training. However, they incur substantial memory overhead due to the storage demands of non-diagonal preconditioning matrices. To address this, we introduce 4-bit quantization for Shampoo's preconditioners. We introduce two key methods: First, we apply Cholesky decomposition followed by quantization of the Cholesky factors, reducing memory usage by leveraging their lower triangular structure while better preserving spectral properties to minimize information loss. To our knowledge, this is the first quantization approach applied to Cholesky factors of preconditioners. Second, we incorporate error feedback in the quantization process, efficiently storing Cholesky factor and error state in the lower and upper triangular parts of the same matrix. Through extensive experiments, we demonstrate that combining Cholesky quantization with error feedback enhances memory efficiency and algorithm performance in large-scale deep-learning tasks. Theoretically, we also provide convergence proofs for quantized Shampoo under both smooth and non-smooth stochastic optimization settings.

Jialiang Wang, Xianming Liu, Xiong Zhou, Gangfeng Hu, Deming Zhai, Junjun Jiang, Xiangyang Ji

Learning with noisy labels is a crucial task for training accurate deep neural networks. To mitigate label noise, prior studies have proposed various robust loss functions, particularly symmetric losses. Nevertheless, symmetric losses usually suffer from the underfitting issue due to the overly strict constraint. To address this problem, the Active Passive Loss (APL) jointly optimizes an active and a passive loss to mutually enhance the overall fitting ability. Within APL, symmetric losses have been successfully extended, yielding advanced robust loss functions. Despite these advancements, emerging theoretical analyses indicate that asymmetric losses, a new class of robust loss functions, possess superior properties compared to symmetric losses. However, existing asymmetric losses are not compatible with advanced optimization frameworks such as APL, limiting their potential and applicability. Motivated by this theoretical gap and the prospect of asymmetric losses, we extend the asymmetric loss to the more complex passive loss scenario and propose the Asymetric Mean Square Error (AMSE), a novel asymmetric loss. We rigorously establish the necessary and sufficient condition under which AMSE satisfies the asymmetric condition. By substituting the traditional symmetric passive loss in APL with our proposed AMSE, we introduce a novel robust loss framework termed Joint Asymmetric Loss (JAL). Extensive experiments demonstrate the effectiveness of our method in mitigating label noise. Code available at: https://github.com/cswjl/joint-asymmetric-loss

Jiacheng Li, Feiran Li, Daisuke Iso

In recent years, neural networks have achieved significant progress in offline image processing. However, in online scenarios, particularly in on-chip implementations, memory usage emerges as a critical bottleneck due to the limited memory resources of integrated image processors. In this study, we focus on reducing the memory footprint of neural networks for on-chip image processing by optimizing network design for efficient memory utilization. Specifically, we consider a typical scenario in which images outputted from an image sensor are processed sequentially using line buffers in a line-by-line manner. This setting necessitates the modeling of both intra-line and inter-line correlations--capturing dependencies among pixels within a single line group and across different line groups, respectively.To model intra-line correlations, we propose a progressive feature enhancement strategy, where line pixels are processed with expanding strip convolutions in multiple stages.For inter-line correlation modeling, we introduce a hierarchical line buffer formulation, where features extracted from previous lines are incrementally reused and compressed across multiple hierarchical levels.Comprehensive experiments on various image processing tasks, including RAW denoising, Gaussian denoising, and super-resolution, demonstrate that the proposed method achieves a superior trade-off between performance and memory efficiency than previous solutions, e.g., up to 1dB PSNR gain in RAW denoising at one-fifth of peak memory usage.

Paweł Skierś, Kamil Deja

In this work, we introduce JDCL - a new method for continual learning with generative rehearsal based on joint diffusion models. Neural networks suffer from catastrophic forgetting defined as abrupt loss in the model's performance when retrained with additional data coming from a different distribution. Generative-replay-based continual learning methods try to mitigate this issue by retraining a model with a combination of new and rehearsal data sampled from a generative model. In this work, we propose to extend this idea by combining a continually trained classifier with a diffusion-based generative model into a single - jointly optimized neural network. We show that such shared parametrization, combined with the knowledge distillation technique allows for stable adaptation to new tasks without catastrophic forgetting. We evaluate our approach on several benchmarks, where it outperforms recent state-of-the-art generative replay techniques. Additionally, we extend our method to the semi-supervised continual learning setup, where it outperforms competing buffer-based replay techniques, and evaluate, in a self-supervised manner, the quality of trained representations.

Daqian Shi, Xiaolei Diao, Xu Chen, Cédric M John

Deep Neural Networks (DNNs) have significantly advanced the field of computer vision. To improve DNN training process, knowledge distillation methods demonstrate their effectiveness in accelerating network training by introducing a fixed learning direction from the teacher network to student networks. In this context, several distillation-based optimization strategies are proposed, e.g., deep mutual learning and self-distillation, as an attempt to achieve generic training performance enhancement through the cooperative training of multiple networks. However, such strategies achieve limited improvements due to the poor understanding of the impact of learning directions among networks across different iterations. In this paper, we propose a novel competitive distillation strategy that allows each network in a group to potentially act as a teacher based on its performance, enhancing the overall learning performance. Competitive distillation organizes a group of networks to perform a shared task and engage in competition, where competitive optimization is proposed to improve the parameter updating process. We further introduce stochastic perturbation in competitive distillation, aiming to motivate networks to induce mutations to achieve better visual representations and global optimum. The experimental results show that competitive distillation achieves promising performance in diverse tasks and datasets.

Daniel DeAlcala, Aythami Morales, Julian Fierrez, Gonzalo Mancera, Ruben Tolosana, Javier Ortega-Garcia

Active Membership Inference Test (aMINT) is a method designed to detect whether given data were used during the training of machine learning models. In Active MINT, we propose a novel multitask learning process that involves training simultaneously two models: the original or Audited Model, and a secondary model, referred to as the MINT Model, responsible for identifying the data used for training the Audited Model. This novel multi-task learning approach has been designed to incorporate the auditability of the model as an optimization objective during the training process of neural networks. The proposed approach incorporates intermediate activation maps as inputs to the MINT layers, which are trained to enhance the detection of training data. We present results using a wide range of neural networks, from lighter architectures such as MobileNet to more complex ones such as Vision Transformers, evaluated in 5 public benchmarks. Our proposed Active MINT achieves over 80% accuracy in detecting if given data was used for training, significantly outperforming previous approaches in the literature. Our aMINT and related methodological developments contribute to increasing transparency in AI models, facilitating stronger safeguards in AI deployments to achieve proper security, privacy, and copyright protection (Code available in https://github.com/DanieldeAlcala/Membership-Inference-Test.git)

Ruiqi Du, Xu Tang, Xiangrong Zhang, Jingjing Ma

Since real-world multi-label data often exhibit significant label imbalance, long-tailed multi-label image classification has emerged as a prominent research area in computer vision. Traditionally, it is considered that deep neural networks' classifiers are vulnerable to long-tailed distributions, whereas the feature extraction backbone remains relatively robust. However, our analysis from the feature learning perspective reveals that the backbone struggles to maintain high sensitivity to sample-scarce categories but retains the ability to localize specific areas effectively. Based on this observation, we propose a new model for long-tailed multi-label image classification named category-specific selective feature enhancement (CSSFE). First, it utilizes the retained localization capability of the backbone to capture label-dependent class activation maps. Then, a progressive attention enhancement mechanism, updating from head to medium to tail categories, is introduced to address the low-confidence issue in medium and tail categories. Finally, visual features are extracted according to the optimized class activation maps and combined with semantic information to perform the classification task. Extensive experiments on two benchmark datasets highlight our findings' generalizability and the proposed CSSFE's superior performance.

Tianfang Zhu, Hongyang Zhou, Anan Li

Capturing the spatial patterns of neurons and generating high-fidelity morphological data remain critical challenges in developing biologically realistic large-scale brain network models. Existing methods fail to reconcile anatomical complexity with diversity and computational scalability. We propose MorphoGen, a hierarchical framework integrating global structure prediction through denoising diffusion probabilistic models (DDPMs) with local neurites optimization. The pipeline initiates with DDPM-generated coarse-grained neuronal point clouds, followed by skeletonization and growth-guided linking to derive plausible tree-like structures, and culminates in natural neural fibers refinement via a pragmatic smoothing network. Comprehensive evaluations across three distinct long-range projection neuron datasets demonstrate that the proposed method improves 1-Nearest Neighbor Accuracy by approximately 12% on average compared to state-of-the-art baseline, reduces average training time by around 55%, and aligns the distributions of several morphometrics with real data. This work establishes a novel global-to-local paradigm for neuronal morphology generation, offering a more direct and efficient approach compared to current branch-sequential modeling methods. Code is available at https://github.com/Brainsmatics/MorphoGen.