For solving the limitations of the current self knowledge distillation including never fully utilizing the knowledge of shallow exits and neglecting the impact of auxiliary exits' structure on the performance of network, a novel self knowledge distillation framework via virtual teacher-students mutual learning named LOTH is proposed in this paper. A knowledgeable virtual teacher is constructed from the rich feature maps of each exit to help the learning of each exit. Meanwhile, the logit knowledges of each exit are incorporated to guide the learning of the virtual teacher. They learn mutually through the well-designed loss in LOTH. Moreover, two kinds of auxiliary building blocks are designed to balance the efficiency and effectiveness of network. Extensive experiments with diverse backbones on CIFAR-100 and Tiny-ImageNet validate the effectiveness of LOTH, which realizes superior performance with less resource by the comparison with the state-of-the-art distillation methods. The code of LOTH is available on Github https://github.com/cloak-s/LOTH.
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Recently, the multi-modal fusion of RGB, depth, and semantics has shown great potential in the domain of dense Simultaneous Localization and Mapping (SLAM), as known as dense semantic SLAM. Yet a prerequisite for generating consistent and continuous semantic maps is the availability of dense, efficient, and scalable scene representations. To date, existing semantic SLAM systems based on explicit scene representations (points/meshes/surfels) are limited by their resolutions and inabilities to predict unknown areas, thus failing to generate dense maps. Contrarily, a few implicit scene representations (Neural Radiance Fields) to deal with these problems rely on time-consuming ray tracing-based volume rendering technique, which cannot meet the real-time rendering requirements of SLAM. Fortunately, the Gaussian Splatting scene representation has recently emerged, which inherits the efficiency and scalability of point/surfel representations while smoothly represents geometric structures in a continuous manner, showing promise in addressing the aforementioned challenges. To this end, we propose GS3LAM, a Gaussian Semantic Splatting SLAM framework, which takes multimodal data as input and can render consistent, continuous dense semantic maps in real-time. To fuse multimodal data, GS3LAM models the scene as a Semantic Gaussian Field (SG-Field), and jointly optimizes camera poses and the field by establishing error constraints between observed and predicted data. Furthermore, a Depth-adaptive Scale Regularization (DSR) scheme is proposed to tackle the problem of misalignment between scale-invariant Gaussians and geometric surfaces within the SG-Field. To mitigate the forgetting phenomenon, we propose an effective Random Sampling-based Keyframe Mapping (RSKM) strategy, which exhibits notable superiority over local covisibility optimization strategies commonly utilized in 3DGS-based SLAM systems. Extensive experiments conducted on the benchmark datasets reveal that compared with state-of-the-art competitors, GS3 LAM demonstrates increased tracking robustness, superior real-time rendering quality, and enhanced semantic reconstruction precision. To make the results reproducible, the source code is available at https://github.com/lif314/GS3LAM.
This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates. Previous LIC methods based on transmitting quantized continuous features often yield blurry and noisy reconstruction due to the severe quantization loss. While previous LIC methods based on learned codebooks that discretize visual space usually give poor-fidelity reconstruction due to the insufficient representation power of limited codewords in capturing faithful details. We propose a novel dual-stream framework, HyrbidFlow, which combines the continuous-feature-based and codebook-based streams to achieve both high perceptual quality and high fidelity under extreme low bitrates. The codebook-based stream benefits from the high-quality learned codebook priors to provide high quality and clarity in reconstructed images. The continuous feature stream targets at maintaining fidelity details. To achieve the ultra low bitrate, a masked token-based transformer is further proposed, where we only transmit a masked portion of codeword indices and recover the missing indices through token generation guided by information from the continuous feature stream. We also develop a bridging correction network to merge the two streams in pixel decoding for final image reconstruction, where the continuous stream features rectify biases of the codebook-based pixel decoder to impose reconstructed fidelity details. Experimental results demonstrate superior performance across several datasets under extremely low bitrates, compared with existing single-stream codebook-based or continuous-feature-based LIC methods.
Generative image steganography has gained significant attention due to its ability to hide secret data during image generation. However, existing generative image steganography methods still face challenges in terms of controllability, usability, and robustness, making it difficult to apply real-world scenarios. We propose a practical and robust generative image steganography based on Latent Diffusion Models, called LDStega. LDStega takes controllable condition text as input and designs an encoding strategy in the reverse process of the Latent Diffusion Models to couple latent space generation with data hiding. The encoding strategy selects a sampling interval from a candidate pool of truncated Gaussian distributions guided by secret data to generate the stego latent space. Subsequently, the stego latent space is fed into the Decoder to generate the stego image. The receiver extracts the secret data from the globally Gaussian distribution of the lossy-reconstructed latent space in the reverse process. Experimental results demonstrate that LDStega achieves high extraction accuracy while controllably generating image content and saving the stego image in the widely used PNG and JPEG formats. Additionally, LDStega outperforms state-of-the-art techniques in resisting common image attacks.
Change detection identifies differences between images captured at different times. Real-world change detection faces challenges posed by the diverse and intricate nature of change areas, while current datasets and algorithms are often limited to simpler, consistent changes, reducing their effectiveness in practical applications. Existing dual-branch methods process images independently, risking the loss of change information due to insufficient early interaction. In contrast, single-stream approaches, though improving early integration, lack efficacy in capturing complex changes. To address these limitations, we introduce a novel single-stream framework, the Multi-scale Change-Aware Transformer (MCAT), which features the Dynamic Change-Aware Attention module and the Multi-scale Change-Enhanced Aggregator. The Dynamic Change-Aware Attention module, integrating local self-attention and cross-temporal attention, conducts dynamic iteration on images differences, thereby targeting feature extraction of change areas. The Multi-scale Change-Enhanced Aggregator enables the model to adapt to various scales and complex shapes through local change enhancement and multi-scale aggregation strategies. To overcome the limitations of existing datasets regarding the scale diversity and morphological complexity of change areas, we construct the Mining Area Change Detection dataset. The dataset offers a diverse array of change areas that span multiple scales and exhibit complex shapes, providing a robust benchmark for change detection. Extensive experiments demonstrate that our model outperforms existing methods, especially for irregular and multi-scale changes. Codes and dataset are available at https://github.com/chh11/MCAT.
Remote photoplethysmography (rPPG) is a promising technique for non-contact physiological signal measurement. It has great potential applications in human health monitoring and emotion analysis. However, existing methods for the rPPG task ignore the long-tail phenomenon of physiological signal data, especially on multi-domain joint training. In addition, we find that the long-tail problem of the physiological label (phys-label) exists in different datasets, and the long-tail problem of some domain exists under the same phys-label. To tackle these problems, we propose a hierarchical balanced framework, to mitigate the bias caused by domain and phys-label imbalance. Specifically, we propose anti-spurious domain center learning tailored to learning domain-balanced embeddings space. Then, we adopt compact-aware continuity regularization to estimate phys-label-wise imbalances and construct continuity between embeddings. Extensive experiments demonstrate that our method outperforms the state-of-the-art in cross-dataset and intra-dataset settings. Our code is available at https://github.com/pywin/HiBa.
The effective denoising demonstrated by the latent diffusion model poses a new threat to image watermarking, as attackers can erase the watermark by performing a forward diffusion, followed by backward denoising. While such denoising might introduce large distortion in the pixel domain, the image semantics remain similar. Unfortunately, most existing robust watermarking methods fail to tackle such an erasure attack since they are primarily designed for traditional channel distortions. To address such issue, this paper proposed DERO, a diffusion-model-erasure robust watermarking framework. Based on the frequency domain analysis of the diffusion model's denoising process, we designed a destruction and compensation noise layer (DCNL) to approximate the distortion effects caused by latent diffusion model erasure (LDE). In detail, DCNL consists of a multi-scale low-pass filtering and a white noise compensation process, where the high-frequency components of the image are first obliterated, and then full-frequency components are enriched with white noise. Such a process broadly simulates the LDE distortions. Besides, on the extraction side, we cascaded a pre-trained variational autoencoder before the decoder to extract the watermark in the latent domain, which closely adapts to the operation domain of the LDE process. Meanwhile, to improve the robustness of the decoder, we also design a latent feature augmentation (LFA) operation on the latent feature. Throughout the end-to-end training with the DCNL and LFA, DERO can successfully achieve robustness against LDE. Our experimental results demonstrate the effectiveness and the generalizability of the proposed framework. The LDE robustness is significantly improved from 75% with SOTA methods to an impressive 96% with DERO.
Volume electron microscopy (vEM) is becoming a prominent technique in three-dimensional (3D) cellular visualization. vEM collects a series of two-dimensional (2D) images and reconstructs ultrastructures at the nanometer scale by rational axial interpolation between neighboring sections. However, section damage inevitably occurs in the sample preparation and imaging process, suffering from manual operational errors or occasional mechanical failures. The damaged regions present blurry and contaminated structure information, even local blank holes. Despite significant progress in single-image inpainting, it is still a great challenge to recover missing biological structures, that satisfy 3D structural continuity among sections. In this paper, we propose an optical flow-based serial section inpainting architecture to effectively combine the 3D structure information from neighboring sections and 2D image features from surrounding regions. We design a two-stage reference generation strategy to predict a rational and detailed intermediate state image from coarse to fine. Then, a GAN-based inpainting network is adopted to integrate all reference information and guide the restoration of missing structures, while ensuring consistent distribution of pixel values across the 2D image. Extensive experimental results well demonstrate the superiority of our method over existing inpainting tools. Our code is available at https://github.com/chengyr1999/FlowInpaint/.
Although Federated Learning has been widely studied in recent years, there are still high overhead expenses in each communication round for large-scale models such as Vision Transformer. To lower the communication complexity, we propose a novel Federated Block Coordinate Gradient Descent (FedBCGD) method for communication efficiency. The proposed method splits model parameters into several blocks including a shared block and enables uploading a specific parameter block by each client, which can significantly reduce communication overhead. Moreover, we also develop an accelerated FedBCGD algorithm (called FedBCGD+) with client drift control and stochastic variance reduction. To the best of our knowledge, this paper is the first work on parameter block communication for training large-scale deep models. We also provide the convergence analysis for the proposed algorithms. Our theoretical results show that the communication complexities of our algorithms are a factor 1 /N lower than those of existing methods, where N is the number of parameter blocks, and they enjoy much faster convergence than their counterparts. Empirical results indicate the superiority of the proposed algorithms compared to state-of-the-art algorithms.
The growing interest in embodied intelligence has brought ego-centric perspectives to contemporary research. One significant challenge within this realm is the accurate localization and tracking of objects in ego-centric videos, primarily due to the substantial variability in viewing angles. Addressing this issue, this paper introduces a novel zero-shot approach for the 3D reconstruction and tracking of all objects from the ego-centric video. We present Ego3DT, a novel framework that initially identifies and extracts detection and segmentation information of objects within the ego environment. Utilizing information from adjacent video frames, Ego3DT dynamically constructs a 3D scene of the ego view using a pre-trained 3D scene reconstruction model. Additionally, we have innovated a dynamic hierarchical association mechanism for creating stable 3D tracking trajectories of objects in ego-centric videos. Moreover, the efficacy of our approach is corroborated by extensive experiments on two newly compiled datasets, with 1.04 × - 2.90× in HOTA, showcasing the robustness and accuracy of our method in diverse ego-centric scenarios.
Gaussian splatting, renowned for its exceptional rendering quality and efficiency, has emerged as a prominent technique in 3D scene representation. However, the substantial data volume of Gaussian splatting impedes its practical utility in real-world applications. Herein, we propose an efficient 3D scene representation, named Compressed Gaussian Splatting (CompGS), which harnesses compact Gaussian primitives for faithful 3D scene modeling with a remarkably reduced data size. To ensure the compactness of Gaussian primitives, we devise a hybrid primitive structure that captures predictive relationships between each other. Then, we exploit a small set of anchor primitives for prediction, allowing the majority of primitives to be encapsulated into highly compact residual forms. Moreover, we develop a rate-constrained optimization scheme to eliminate redundancies within such hybrid primitives, steering our CompGS towards an optimal trade-off between bitrate consumption and representation efficacy. Experimental results show that the proposed CompGS significantly outperforms existing methods, achieving superior compactness in 3D scene representation without compromising model accuracy and rendering quality. Our code will be released on GitHub for further research.
3D object detection plays a pivotal role in autonomous driving and robotics, demanding precise interpretation of Bird's Eye View (BEV) images. The dynamic nature of real-world environments necessitates the use of dynamic query mechanisms in 3D object detection to adaptively capture and process the complex spatio-temporal relationships present in these scenes. However, prior implementations of dynamic queries have often faced difficulties in effectively leveraging these relationships, particularly when it comes to integrating temporal information in a computationally efficient manner. Addressing this limitation, we introduce a framework utilizing dynamic query evolution strategy, harnesses K-means clustering and Top-K attention mechanisms for refined spatio-temporal data processing. By dynamically segmenting the BEV space and prioritizing key features through Top-K attention, our model achieves a real-time, focused analysis of pertinent scene elements. Our extensive evaluation on the nuScenes and Waymo dataset showcases a marked improvement in detection accuracy, setting a new benchmark in the domain of query-based BEV object detection. Our dynamic query evolution strategy has the potential to push the boundaries of current BEV methods with enhanced adaptability and computational efficiency. Project page: https://github.com/Jiawei-Yao0812/QE-BEV
In this paper, we investigate the challenge of spatio-temporal video prediction, which involves generating future videos based on historical data streams. Existing approaches typically utilize external information such as semantic maps to enhance video prediction, which often neglect the inherent physical knowledge embedded within videos. Furthermore, their high computational demands could impede their applications for high-resolution videos. To address these constraints, we introduce a novel approach called Physics-assisted Spatio-temporal Network (PastNet) for generating high-quality video prediction. The core of our PastNet lies in incorporating a spectral convolution operator in the Fourier domain, which efficiently introduces inductive biases from the underlying physical laws. Additionally, we employ a memory bank with the estimated intrinsic dimensionality to discretize local features during the processing of complex spatio-temporal signals, thereby reducing computational costs and facilitating efficient high-resolution video prediction. Extensive experiments on various widely-used datasets demonstrate the effectiveness and efficiency of the proposed PastNet compared with a range of state-of-the-art methods, particularly in high-resolution scenarios. Our code is available at https://github.com/easylearningscores/PastNet.
Recently, there has been a surge in machine-generated natural language content being misused by unauthorized parties. Watermarking is a well-recognized technique to address the issue by tracing the provenance of the text. However, we found that most existing watermarking systems for texts are subject to ad hoc design and thus suffer from fundamental vulnerabilities. We propose a principled design for text watermarking based on a theoretical information-hiding framework. The watermarking party and attacker play a rate-distortion-constrained capacity game to achieve the maximum rate of reliable transmission, i.e., watermark capacity. The capacity can be expressed by the mutual information between the encoding and the attacker's corrupted text, indicating how many watermark bits are effectively conveyed under distortion constraints. The system is realized by a learning-based framework with mutual information neural estimators. In the framework, we adopt the assumption of an omniscient attacker and let the watermarking party pit against the attacker who is fully aware of the watermarking strategy. The watermarking party thus achieves higher robustness against removal attacks. We further show that the incorporation of side information substantially enhances the efficacy and robustness of the watermarking system. Experimental results have shown the superiority of our watermarking system compared to the state-of-the-art in terms of capacity, robustness, and preserving text semantics.
Social video platforms have emerged as significant channels for information dissemination, facilitating lively public discussions that often give rise to controversies. However, existing approaches to controversy detection primarily focus on textual features, which raises three key concerns: it underutilizes the potential of visual information available on social media platforms; it is ineffective when faced with incomplete or absent textual information; and the existing datasets fail to adequately address the need for comprehensive multimodal resources on social media platforms. To address these challenges, we construct a large-scale Multimodal Controversial Dataset (MMCD) in Chinese. Additionally, we propose a novel framework named Multi-view Controversy Detection (MVCD) to effectively model controversies from multiple perspectives. Through extensive experiments using state-of-the-art models on the MMCD, we demonstrate MVCD's effectiveness and potential impact.
Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling for Long-Tailed Continual Learning
PDF ↗Even in the era of large models, one of the well-known issues in continual learning (CL) is catastrophic forgetting, which is significantly challenging when the continual data stream exhibits a long-tailed distribution, termed as Long-Tailed Continual Learning (LTCL). Existing LTCL solutions generally require the label distribution of the data stream to achieve re-balance training. However, obtaining such prior information is often infeasible in real scenarios since the model should learn without pre-identifying the majority and minority classes. To this end, we propose a novel Prior-free Balanced Replay (PBR) framework to learn from long-tailed data stream with less forgetting. Concretely, motivated by our experimental finding that the minority classes are more likely to be forgotten due to the higher uncertainty, we newly design an uncertainty-guided reservoir sampling strategy to prioritize rehearsing minority data without using any prior information, which is based on the mutual dependence between the model and samples. Additionally, we incorporate two prior-free components to further reduce the forgetting issue: (1) Boundary constraint is to preserve uncertain boundary supporting samples for continually re-estimating task boundaries. (2) Prototype constraint is to maintain the consistency of learned class prototypes along with training. Our approach is evaluated on three standard long-tailed benchmarks, demonstrating superior performance to existing CL methods and previous SOTA LTCL approach in both task- and class-incremental learning settings, as well as ordered- and shuffled-LTCL settings.
Audio super-resolution aims to improve the quality of acoustic signals and is able to reconstruct corresponding high-resolution acoustic signals from low-resolution acoustic signals. However, since acoustic signals can be divided into two forms: time-domain acoustic waves or frequency-domain spectrograms, most existing research focuses on data enhancement in a single field, which can only obtain partial or local features of the audio signal, resulting in limitations of data analysis. Therefore, this paper proposes a time-frequency domain fusion enhanced audio super-resolution method to mine the complementarity of the two representations of acoustic signals. Specifically, we propose an end-to-end audio super-resolution network. Including the variational autoencoder based sound wave super-resolution module, U-Net-based Spectrogram Super-Resolution Module, and attention-based Time-Frequency Domain Fusion Module. The first two modules can generate more high-frequency and low-frequency components for audio respectively. As a critical component of our method, time-frequency domain fusion module performs weighted fusion on the above two outputs to obtain a super-resolution audio signal. Compared with other methods, experimental results on the VCTK and Piano datasets in natural scenes show that the time-frequency domain fusion audio super-resolution model has a state-of-the-art bandwidth expansion effect. Furthermore, we perform super-resolution on the ShipsEar dataset containing underwater acoustic signals. The super-resolution results are used to test ship target recognition, and and the accuracy is improved by 12.66%. Therefore, the proposed super-resolution method has excellent signal enhancement effect and generalization ability.
Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancement (LLE) as the initial step followed by detection, LLE is primarily designed for human vision rather than machine vision and can accumulate errors. In this work, we propose an efficient and effective single-stage approach for localizing text in the dark that circumvents the need for LLE. We introduce a constrained learning module as an auxiliary mechanism during the training stage of the text detector. This module is designed to guide the text detector in preserving textual spatial features amidst feature map resizing, thus minimizing the loss of spatial information in texts under low-light visual degradations. Specifically, we incorporate spatial reconstruction and spatial semantic constraints within this module to ensure the text detector acquires essential positional and contextual range knowledge. Our approach enhances the original text detector's ability to identify text's local topological features using a dynamic snake feature pyramid network and adopts a bottom-up contour shaping strategy with a novel rectangular accumulation technique for accurate delineation of streamlined text features. In addition, we present a comprehensive low-light dataset for arbitrary-shaped text, encompassing diverse scenes and languages. Notably, our method achieves state-of-the-art results on this low-light dataset and exhibits comparable performance on standard normal light datasets. The code and dataset will be released.
Deep learning has made significant advancements and breakthroughs in medical image recognition. However, the clinical reality is complex and multifaceted, with patients often suffering from multiple intertwined diseases, not all of which are equally common, leading to medical datasets that are frequently characterized by multi-labels and a long-tailed distribution. In this paper, we propose a method involving label decoupling and reconstruction (LDRNet) to address these two specific challenges. The label decoupling utilizes the fusion of semantic information from both categories and images to capture the class-aware features across different labels. This process not only integrates semantic information from labels and images to improve the model's ability to recognize diseases, but also captures comprehensive features across various labels to facilitate a deeper understanding of disease characteristics within the dataset. Following this, our label reconstruction method uses the class-aware features to reconstruct the label distribution. This step generates a diverse array of virtual features for tail categories, promoting unbiased learning for the classifier and significantly enhancing the model's generalization ability and robustness. Extensive experiments conducted on three multi-label long-tailed medical image datasets, including the Axial Spondyloarthritis Dataset, NIH Chest X-ray 14 Dataset, and ODIR-5K Dataset, have demonstrated that our approach achieves state-of-the-art performance, showcasing its effectiveness in handling the complexities associated with multi-label and long-tailed distributions in medical image recognition.
Blind Face Restoration (BFR) aims to restore high-quality face images from low-quality images with unknown degradation. Previous GAN-based or ViT-based methods have shown promising results, but have identity details loss once degradation is severe; while recent diffusion-based methods work on image level and take a lot of time to infer. To restore images in any degradation types with high quality and spend less time compared to the classic diffusion-based method, we propose LD-BFR, a novel BFR framework that integrates both the strengths of vector quantization and latent diffusion. First, we employ a Dual Cross-Attention vector quantization to restore the degraded image in a global manner. Then we utilize the restored high-quality quantized feature as the guidance in our latent diffusion model to generate high-quality restored images with rich details. With the help of the proposed high-quality feature injection module, our LD-BFR effectively injects the high-quality feature as a condition to guide the generation of our latent diffusion model. Extensive experiments demonstrate the superior performance of our model over the SOTA BFR methods. The code is available at: https://github.com/YuzhenD/LD-BFR.git