PairingNet: A Learning-based Pair-searching and -matching Network for Image Fragments

In this paper, we propose a learning-based image fragment pair-searching and -matching approach to solve the challenging restoration problem. Existing works use rule-based methods to match similar contour shapes or textures, which are always difficult to tune hyperparameters for extensive data and computationally time-consuming. Therefore, we propose a neural network that can effectively utilize neighbor textures with contour shape information to fundamentally improve performance. First, we employ a graph-based network to extract the local contour and texture features of fragments. Then, for the pair-searching task, we adopt a linear transformer-based module to integrate these local features and use contrastive loss to encode the global features of each fragment. For the pair-matching task, we design a weighted fusion module to dynamically fuse extracted local contour and texture features, and formulate a similarity matrix for each pair of fragments to calculate the matching score and infer the adjacent segment of contours. To faithfully evaluate our proposed network, we created a new image fragment dataset through an algorithm we designed that tears complete images into irregular fragments. The experimental results show that our proposed network achieves excellent pair-searching accuracy, reduces matching errors, and significantly reduces computational time. Details, sourcecode, and data are available in our supplementary material.

Universal Incomplete-View CT Reconstruction with Prompted Contextual Transformer

Despite the reduced radiation dose, suitability for objects with physical constraints, and accelerated scanning procedure, incomplete-view computed tomography (CT) images suffer from severe artifacts, hampering their value for clinical diagnosis. The incomplete-view CT can be divided into two scenarios depending on the sampling of projection, sparse-view CT and limited-angle CT, each encompassing various settings for different clinical requirements. Existing methods tackle with these settings separately and individually due to their significantly different artifact patterns; this, however, gives rise to high computational and storage costs, hindering its flexible adaptation to new settings. To address this challenge, we present the first-of-its-kind all-in-one incomplete-view CT reconstruction model with PROmpted Contextual Transformer, termed ProCT. More specifically, we first devise the projection view-aware prompting to provide setting-discriminative information, enabling a single model to handle diverse incomplete-view CT settings. Then, we propose artifact-aware contextual learning to provide the contextual guidance of image pairs from either CT phantom or publicly available datasets, making ProCT capable of accurately removing the complex artifacts from the incomplete-view CT images. Extensive experiments demonstrate that ProCT can achieve superior performance on a wide range of incomplete-view CT settings using a single model. Remarkably, our model with only image-domain information surpasses the state-of-the-art dual-domain methods that require the access to raw data. The code is available at: https://github.com/Masaaki-75/proct

FedReverse: Multiparty Reversible Deep Neural Network Watermarking

The proliferation of Deep Neural Networks (DNN) in commercial applications is expanding rapidly. Simultaneously, the increasing complexity and cost of training DNN models have intensified the urgency surrounding the protection of intellectual property associated with these trained models. In this regard, DNN watermarking has emerged as a crucial safeguarding technique. This paper presents FedReverse, a novel multiparty reversible watermarking approach for robust copyright protection while minimizing performance impact. Unlike existing methods, FedReverse enables collaborative watermark embedding from multiple parties after model training, ensuring individual copyright claims. In addition, FedReverse is reversible, enabling complete watermark removal with unanimous client consent. FedReverse demonstrates perfect covering, ensuring that observations of watermarked content do not reveal any information about the hidden watermark. Additionally, it showcases resistance against Known Original Attacks (KOA), making it highly challenging for attackers to forge watermarks or infer the key. This paper further evaluates FedReverse through comprehensive simulations involving Multi-layer Perceptron (MLP) and Convolutional Neural Networks (CNN) trained on the MNIST dataset. The simulations demonstrate FedReverse's robustness, reversibility, and minimal impact on model accuracy across varying embedding parameters and multiple client scenarios.

Computing an Entire Solution Path of a Nonconvexly Regularized Convex Sparse Model

The generalized minimax concave (GMC) penalty is a nonconvex sparse regularizer which can preserve the overall-convexity of the sparse least squares problem. In this paper, we study the solution path of a special but important instance of the GMC model termed the scaled GMC (sGMC) model. We show that despite the nonconvexity of the regularizer, there exists a solution path of the sGMC model which is piecewise linear as a function of the tuning parameter, and we propose an efficient algorithm for computing a solution path of this type. Our algorithm is an extension of the well-known least angle regression (LARS) algorithm for LASSO, hence we term the proposed algorithm LARS-sGMC. Under suitable conditions, we provide a proof of the correctness and finite termination of the proposed LARS-sGMC algorithm. This article also serves as an appendix for the short paper titled ``COMPUTING AN ENTIRE SOLUTION PATH OF A NONCONVEXLY REGULARIZED CONVEX SPARSE MODEL", and addresses proofs and technical derivations that were omitted in the original paper due to space limitation.

Point, Segment and Count: A Generalized Framework for Object Counting

Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, \emph{a.k.a} few-shot and zero-shot counting. Current state-of-the-art methods highly rely on density maps to predict object counts, which lacks model interpretability. In this paper, we propose a generalized framework for both few-shot and zero-shot object counting based on detection. Our framework combines the superior advantages of two foundation models without compromising their zero-shot capability: (\textbf{i}) SAM to segment all possible objects as mask proposals, and (\textbf{ii}) CLIP to classify proposals to obtain accurate object counts. However, this strategy meets the obstacles of efficiency overhead and the small crowded objects that cannot be localized and distinguished. To address these issues, our framework, termed PseCo, follows three steps: point, segment, and count. Specifically, we first propose a class-agnostic object localization to provide accurate but least point prompts for SAM, which consequently not only reduces computation costs but also avoids missing small objects. Furthermore, we propose a generalized object classification that leverages CLIP image/text embeddings as the classifier, following a hierarchical knowledge distillation to obtain discriminative classifications among hierarchical mask proposals. Extensive experimental results on FSC-147 dataset demonstrate that PseCo achieves state-of-the-art performance in both few-shot/zero-shot object counting/detection, with additional results on large-scale COCO and LVIS datasets. The source code is available at \url{https://github.com/Hzzone/PseCo}.

Positional Description Matters for Transformers Arithmetic

Transformers, central to the successes in modern Natural Language Processing, often falter on arithmetic tasks despite their vast capabilities --which paradoxically include remarkable coding abilities. We observe that a crucial challenge is their naive reliance on positional information to solve arithmetic problems with a small number of digits, leading to poor performance on larger numbers. Herein, we delve deeper into the role of positional encoding, and propose several ways to fix the issue, either by modifying the positional encoding directly, or by modifying the representation of the arithmetic task to leverage standard positional encoding differently. We investigate the value of these modifications for three tasks: (i) classical multiplication, (ii) length extrapolation in addition, and (iii) addition in natural language context. For (i) we train a small model on a small dataset (100M parameters and 300k samples) with remarkable aptitude in (direct, no scratchpad) 15 digits multiplication and essentially perfect up to 12 digits, while usual training in this context would give a model failing at 4 digits multiplication. In the experiments on addition, we use a mere 120k samples to demonstrate: for (ii) extrapolation from 10 digits to testing on 12 digits numbers while usual training would have no extrapolation, and for (iii) almost perfect accuracy up to 5 digits while usual training would be correct only up to 3 digits (which is essentially memorization with a training set of 120k samples).

Energizing Federated Learning via Filter-Aware Attention

Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity. Personalized parameter generation through a hypernetwork proves effective, yet existing methods fail to personalize local model structures. This leads to redundant parameters struggling to adapt to diverse data distributions. To address these limitations, we propose FedOFA, utilizing personalized orthogonal filter attention for parameter recalibration. The core is the Two-stream Filter-aware Attention (TFA) module, meticulously designed to extract personalized filter-aware attention maps, incorporating Intra-Filter Attention (IntraFa) and Inter-Filter Attention (InterFA) streams. These streams enhance representation capability and explore optimal implicit structures for local models. Orthogonal regularization minimizes redundancy by averting inter-correlation between filters. Furthermore, we introduce an Attention-Guided Pruning Strategy (AGPS) for communication efficiency. AGPS selectively retains crucial neurons while masking redundant ones, reducing communication costs without performance sacrifice. Importantly, FedOFA operates on the server side, incurring no additional computational cost on the client, making it advantageous in communication-constrained scenarios. Extensive experiments validate superior performance over state-of-the-art approaches, with code availability upon paper acceptance.

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things

With the rapid development of Artificial Intelligent Internet of Things (AIoT), the image data from AIoT devices has been witnessing the explosive increasing. In this paper, a novel deep image semantic communication model is proposed for the efficient image communication in AIoT. Particularly, at the transmitter side, a high-precision image semantic segmentation algorithm is proposed to extract the semantic information of the image to achieve significant compression of the image data. At the receiver side, a semantic image restoration algorithm based on Generative Adversarial Network (GAN) is proposed to convert the semantic image to a real scene image with detailed information. Simulation results demonstrate that the proposed image semantic communication model can improve the image compression ratio and recovery accuracy by 71.93% and 25.07% on average in comparison with WebP and CycleGAN, respectively. More importantly, our demo experiment shows that the proposed model reduces the total delay by 95.26% in the image communication, when comparing with the original image transmission.

PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning

Self-supervised learning has been actively studied in time series domain recently, especially for masked reconstruction. Most of these methods follow the "Pre-training + Fine-tuning" paradigm in which a new decoder replaces the pre-trained decoder to fit for a specific downstream task, leading to inconsistency of upstream and downstream tasks. In this paper, we first point out that the unification of task objectives and adaptation for task difficulty are critical for bridging the gap between time series masked reconstruction and forecasting. By reserving the pre-trained mask token during fine-tuning stage, the forecasting task can be taken as a special case of masked reconstruction, where the future values are masked and reconstructed based on history values. It guarantees the consistency of task objectives but there is still a gap in task difficulty. Because masked reconstruction can utilize contextual information while forecasting can only use historical information to reconstruct. To further mitigate the existed gap, we propose a simple yet effective prompt token tuning (PT-Tuning) paradigm, in which all pre-trained parameters are frozen and only a few trainable prompt tokens are added to extended mask tokens in element-wise manner. Extensive experiments on real-world datasets demonstrate the superiority of our proposed paradigm with state-of-the-art performance compared to representation learning and end-to-end supervised forecasting methods.