Reviewerly/exHarmony huggingface dataset:
DuDoINet: Dual-Domain Implicit Network for Multi-Modality MR Image Arbitrary-scale Super-Resolution

Compared to single-modality magnetic resonance (MR) image super-resolution (SR) methods, multi-modality MR image methods can utilize high-resolution reference modality (e.g., T1 modality) to provide valuable complementary information for low-resolution target modality (e.g., T2 modality) in SR reconstruction, which can further improve the quality of the SR images. Although they have achieved impressive results, these methods still suffer from the following drawbacks: (1) They can only handle fixed integer upsampling factors, such as 2X, 3X, and 4X, and require training and storing corresponding models for each upsampling factor, which is infeasible in clinical practice; (2) They only perform feature extraction and reconstruction in the image domain. However, the aliasing artifacts produced in the image domain are structural and non-local. Therefore, using only the image domain cannot effectively reconstruct high-quality aliasing-free SR images. To address these issues, we develop a brand-new Dual-Domain Implicit Network (DuDoINet) for multi-modality MR image arbitrary-scale SR. Specifically, we propose a dual-domain learning scheme for multi-modality MR image SR, which allows the network to sufficiently exploit the frequency and image domain information in MR images. In addition, we design implicit attention to achieve arbitrary-scale upsampling of MR images, which utilizes a continuously differentiable function that generates pixel values from pixel coordinates. Furthermore, we designed a deformable cross-modality attention mechanism that can adaptively transfer high-frequency details from the T1 to the T2 modality, better integrating valuable complementary information from the T1 modality. Extensive and comprehensive experiments on healthy subjects and patient datasets demonstrate that our DuDoINet outperforms SOTA methods, demonstrating its great potential for clinical practice.
Localized and Balanced Efficient Incomplete Multi-view Clustering

In recent years, many incomplete multi-view clustering methods have been proposed to address the challenging unsupervised clustering issue on the multi-view data with missing views. However, most of the existing works are inapplicable to large-scale clustering task and their clustering results are unstable since these methods have high computational complexities and their results are produced by kmeans rather than their designed learning models. In this paper, we propose a new one-step incomplete multi-view clustering model, called Localized and Balanced Incomplete Multi-view Clustering (LBIMVC), to address these issues. Specifically, LBIMVC develops a new graph regularized incomplete multi-matrix-factorization model to obtain the unique clustering result by learning a consensus probability representation, where each element of the consensus representation can directly reflect the probability of the corresponding sample to the class. In addition, the proposed graph regularized model integrates geometric preserving and consensus representation learning into one term without introducing any extra constraint terms and parameters to explore the structure of data. Moreover, to avoid that samples are over divided into a few clusters, a balanced constraint is introduced to the model. Experimental results on four databases demonstrate that our method not only obtains competitive clustering performance, but also performs faster than some state-of-the-art methods.
GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstruction

Demystifying complex human-ground interactions is essential for accurate and realistic 3D human motion reconstruction from RGB videos, as it ensures consistency between the humans and the ground plane. Prior methods have modeled human-ground interactions either implicitly or in a sparse manner, often resulting in unrealistic and incorrect motions when faced with noise and uncertainty. In contrast, our approach explicitly represents these interactions in a dense and continuous manner. To this end, we propose a novel Ground-aware Motion Model for 3D Human Motion Reconstruction, named GraMMaR, which jointly learns the distribution of transitions in both pose and interaction between every joint and ground plane at each time step of a motion sequence. It is trained to explicitly promote consistency between the motion and distance change towards the ground. After training, we establish a joint optimization strategy that utilizes GraMMaR as a dual-prior, regularizing the optimization towards the space of plausible ground-aware motions. This leads to realistic and coherent motion reconstruction, irrespective of the assumed or learned ground plane. Through extensive evaluation on the AMASS and AIST++ datasets, our model demonstrates good generalization and discriminating abilities in challenging cases including complex and ambiguous human-ground interactions. The code will be available at https://github.com/xymsh/GraMMaR.
Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition

In this paper, we explore the potential of the Contrastive Language-Image Pretraining (CLIP) model in scene text recognition (STR), and establish a novel Symmetrical Linguistic Feature Distillation framework (named CLIP-OCR) to leverage both visual and linguistic knowledge in CLIP. Different from previous CLIP-based methods mainly considering feature generalization on visual encoding, we propose a symmetrical distillation strategy (SDS) that further captures the linguistic knowledge in the CLIP text encoder. By cascading the CLIP image encoder with the reversed CLIP text encoder, a symmetrical structure is built with an image-to-text feature flow that covers not only visual but also linguistic information for distillation. Benefiting from the natural alignment in CLIP, such guidance flow provides a progressive optimization objective from vision to language, which can supervise the STR feature forwarding process layer-by-layer. Besides, a new Linguistic Consistency Loss (LCL) is proposed to enhance the linguistic capability by considering second-order statistics during the optimization. Overall, CLIP-OCR is the first to design a smooth transition between image and text for the STR task. Extensive experiments demonstrate the effectiveness of CLIP-OCR with 93.8% average accuracy on six popular STR benchmarks. Code will be available at https://github.com/wzx99/CLIPOCR.
LHNet: A Low-cost Hybrid Network for Single Image Dehazing

Single image dehazing is a challenging task that requires both local detail and global distribution, and can be applied to various scenarios. However, physics-based dehazing algorithms perform well only in specific settings, while CNN-based algorithms struggle with capturing global information, and ViT-based approaches suffer from inadequate representation of local details. The shortcomings of the above three types of methods lead to issues such as imbalanced colors and incoherent details in the predicted haze-free image. To address these challenges, we propose a new Low-cost Hybrid Network called LHNet. The key insight of LHNet is the effective hybrid of different features, which can achieve better information fusion in the form of feature awareness at the cost of few parameters. This fusion approach narrows the gap between different features and enables LHNet to autonomously choose the fusion granularity to maximize the utilization of prior, local and global information. Extensive experiments are performed on the mainstream dehazing datasets, and the results show that LHNet achieves state-of-the-art performance in single image dehazing. By adopting our fusion approach, a better dehazing effect can be achieved than with other dehazing algorithms with more parameters, even when only CNN and ViT are used. The code is available at https://github.com/SHYuanBest/LHNet.
Alleviating Spatial Misalignment and Motion Interference for UAV-based Video Recognition

Recognizing activities with Unmanned Aerial Vehicles (UAVs) is essential for many applications, while existing video recognition methods are mainly designed for ground cameras and do not account for UAV changing attitudes and fast motion. This creates spatial misalignment of small objects between frames, leading to inaccurate visual movement in drone videos. Additionally, camera motion relative to objects in the video causes relative movements that visually affect object motion and can result in misunderstandings of video content. To address these issues, we present a novel framework named Attentional Spatial and Adaptive Temporal Relations Modeling. First, to mitigate the spatial misalignment of small objects between frames, we design an Attentional Patch-level Spatial Enrichment (APSE) module that models dependencies among patches and enhances patch-level features. Then, we propose a Multi-scale Temporal and Spatial Mixer (MTSM) module that is capable of adapting to disturbances caused by the UAV flight and modeling various temporal clues. By integrating APSE and MTSM into a single model, our network can effectively and accurately capture spatiotemporal relations for UAV videos. Extensive experiments on several benchmarks demonstrate the superiority of our method over state-of-the-art approaches. For instance, our network achieves a classification accuracy of 68.1% with an absolute gain of 1.3% compared to FuTH-Net on the ERA dataset.
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner

Large pre-trained multimodal models have demonstrated significant success in a range of downstream tasks, including image captioning, image-text retrieval, visual question answering (VQA), etc. However, many of these methods rely on image-text pairs collected from the web as pre-training data and unfortunately overlook the need for fine-grained feature alignment between vision and language modalities, which requires detailed understanding of images and language expressions. While integrating VQA and dense captioning (DC) into pre-training can address this issue, acquiring image-question-answer as well as image-location-caption triplets is challenging and time-consuming. Additionally, publicly available datasets for VQA and dense captioning are typically limited in scale due to manual data collection and labeling efforts. In this paper, we propose a novel method called Joint QA and DC GEneration (JADE), which utilizes a pre-trained multimodal model and easily-crawled image-text pairs to automatically generate and filter large-scale VQA and dense captioning datasets. We apply this method to the Conceptual Caption (CC3M) dataset to generate a new dataset called CC3M-QA-DC. Experiments show that when used for pre-training in a multi-task manner, CC3M-QA-DC can improve the performance with various backbones on various downstream tasks. Furthermore, our generated CC3M-QA-DC can be combined with larger image-text datasets (e.g., CC15M) and achieve competitive results compared with models using much more data. Code and dataset are available at https://github.com/johncaged/OPT_Questioner.
JAVP: Joint-Aware Video Processing with Edge-Cloud Collaboration for DNN Inference

Currently, massive video inference tasks are processed through edge-cloud collaboration. However, the diverse scenarios make it difficult to allocate the inference tasks efficiently, resulting in many wasted resources. In this paper, we propose a joint-aware video processing (JAVP) architecture for edge-cloud collaboration. First, we develop a multiscale complexity-aware model for predicting task complexity and determining its suitability for edge or cloud servers. The task is subsequently efficiently scheduled to the appropriate servers by integrating complexity with an adaptive resource-aware optimization algorithm. For input tasks, JAVP can dynamically and intelligently select the most appropriate server. The evaluation results on public datasets show that JAVP can improve the through-put by more than 70% compared to traditional cloud-only solutions while meeting accuracy requirements. And JAVP can improve the accuracy by 3%-5% and reduce delay and energy consumption by 16%-50% compared to state-of-the-art edge-cloud solutions.
Weakly-supervised Video Scene Graph Generation via Unbiased Cross-modal Learning

Video Scene Graph Generation (VidSGG), which aims to detect the relations between objects in a continuous spatio-temporal environment, has shown great potential in video understanding. Almost all prevailing VidSGG approaches are in a fully-supervised manner where expensive manual annotations are required. Therefore, we introduce a novel and challenging task named Weakly-supervised Video Scene Graph Generation (WS-VidSGG), in which a model is trained with only unlocalized scene graphs as supervisory information. Due to the imbalanced data distribution and the lack of fine-grained annotations, models learned in this setting is prone to be biased. Therefore, we propose an Unbiased Cross-Modal Learning (UCML) framework to address the WS-VidSGG task. Specifically, a cross-modal alignment module is firstly designed for allocating pseudo labels to unlabeled visual objects. We then extract unbiased knowledge from dataset statistics, and utilize prompt to make our model finely comprehend semantic concepts. The learned features that from the prompts and unbiased knowledge reinforced each other, resulting in discriminative textual representations. In order to better explore the relations between visual entities, we design a knowledge-guided attention graph to capture the cross-modal relations. Finally, the learned textual and visual features are integrated into a unified framework for relation prediction. Extensive ablation studies verify the effectiveness of our framework. Moreover, the comparison with state-of-the-art fully-supervised methods shows that our proposed framework also achieves comparable performance. Code https://github.com/ZiyueWu59/UCML is available.
Relative NN-Descent: A Fast Index Construction for Graph-Based Approximate Nearest Neighbor Search

Approximate Nearest Neighbor Search (ANNS) is the task of finding the database vector that is closest to a given query vector. Graph-based ANNS is the family of methods with the best balance of accuracy and speed for million-scale datasets. However, graph-based methods have the disadvantage of long index construction time. Recently, many researchers have improved the tradeoff between accuracy and speed during a search. However, there is little research on accelerating index construction. We propose a fast graph construction algorithm, Relative NN-Descent (RNN-Descent). RNN-Descent combines NN-Descent, an algorithm for constructing approximate K-nearest neighbor graphs (K-NN graphs), and RNG Strategy, an algorithm for selecting edges effective for search. This algorithm allows the direct construction of graph-based indexes without ANNS. Experimental results demonstrated that the proposed method had the fastest index construction speed, while its search performance is comparable to existing state-of-the-art methods such as NSG. For example, in experiments on the GIST1M dataset, the construction of the proposed method is 2x faster than NSG. Additionally, it was even faster than the construction speed of NN-Descent.
Chain of Propagation Prompting for Node Classification

Graph Neural Networks (GNN) are an effective technique for node classification, but their performance is easily affected by the quality of the primitive graph and the limited receptive field of message-passing. In this paper, we propose a new self-attention method, namely Chain of Propagation Prompting (CPP), to address the above issues as well as reduce dependence on label information when employing self-attention for node classification. To do this, we apply the self-attention framework to reduce the impact of a low-quality graph and to obtain a maximal receptive field for the message-passing. We also design a simple pattern of message-passing as the prompt to make self-attention capture complex patterns and reduce the dependence on label information. Comprehensive experimental results on real graph datasets demonstrate that CPP outperforms all relevant comparison methods.
SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection

Malicious Deepfakes have led to a sharp conflict over distinguishing between genuine and forged faces. Although many countermeasures have been developed to detect Deepfakes ex-post, undoubtedly, passive forensics has not considered any preventive measures for the pristine face before foreseeable manipulations. To complete this forensics ecosystem, we thus put forward the proactive solution dubbed SepMark, which provides a unified framework for source tracing and Deepfake detection. SepMark originates from encoder-decoder-based deep watermarking but with two separable decoders. For the first time the deep separable watermarking, SepMark brings a new paradigm to the established study of deep watermarking, where a single encoder embeds one watermark elegantly, while two decoders can extract the watermark separately at different levels of robustness. The robust decoder termed Tracer that resists various distortions may have an overly high level of robustness, allowing the watermark to survive both before and after Deepfake. The semi-robust one termed Detector is selectively sensitive to malicious distortions, making the watermark disappear after Deepfake. Only SepMark comprising of Tracer and Detector can reliably trace the trusted source of the marked face and detect whether it has been altered since being marked; neither of the two alone can achieve this. Extensive experiments demonstrate the effectiveness of the proposed SepMark on typical Deepfakes, including face swapping, expression reenactment, and attribute editing. Code will be available at https://github.com/sh1newu/SepMark.
ECENet: Explainable and Context-Enhanced Network for Muti-modal Fact verification

Recently, falsified claims incorporating both text and images have been disseminated more effectively than those containing text alone, raising significant concerns for multi-modal fact verification. Existing research makes contributions to multi-modal feature extraction and interaction, but fails to fully utilize and enhance the valuable and intricate semantic relationships between distinct features. Moreover, most detectors merely provide a single outcome judgment and lack an inference process or explanation. Taking these factors into account, we propose a novel Explainable and Context-Enhanced Network (ECENet) for multi-modal fact verification, making the first attempt to integrate multi-clue feature extraction, multi-level feature reasoning, and justification (explanation) generation within a unified framework. Specifically, we propose an Improved Coarse- and Fine-grained Attention Network, equipped with two types of level-grained attention mechanisms, to facilitate a comprehensive understanding of contextual information. Furthermore, we propose a novel justification generation module via deep reinforcement learning that does not require additional labels. In this module, a sentence extractor agent measures the importance between the query claim and all document sentences at each time step, selecting a suitable amount of high-scoring sentences to be rewritten as the explanation of the model. Extensive experiments demonstrate the effectiveness of the proposed method.
IS2Net: Intra-domain Semantic and Inter-domain Style Enhancement for Semi-supervised Medical Domain Generalization

Domain generalization (DG) demonstrates superior generalization ability in cross-center medical image segmentation. Despite its great success, existing fully supervised DG methods require collecting a large quantity of pixel-level annotations which is quite expensive and time-consuming. To address this challenge, several semi-supervised domain generalized (SSDG) methods have been proposed by simply coupling semi-supervised learning (SSL) with DG tasks, which give rise to two main concerns: (1) Intra-domain dubious semantic information: the quality of pseudo labels in each source domain suffers from the limited amount of labeled data and cross-domain discrepancy. (2) Inter-domain intangible style relationship: current models fail in integrating domain-level information and overlook the relationships among different domains, which degrades the generalization ability of model. In light of these two issues, we propose a novel SSDG framework, namely IS2Net, by arranging an inter-domain generalization branch and several intra-domain SSL branches in a parallel manner, powered by two appealing designs that build a positive interaction between them: (1) A style and semantic memory mechanism is designed to provide both high-quality class-wise representations for intra-domain semantic enhancement and stable domain-specific knowledge for inter-domain style relationship construction. (2) Confident pseudo labeling strategy aims at generating more reliable supervision for intra and inter domain branches, and thus facilitating the learning process of the whole framework. Extensive experiments show that IS2Net yields consistent improvements over the state-of- the-art methods in three public benchmarks.
Generalizable Label Distribution Learning

Label Distributed Learning (LDL) is an emerging machine learning paradigm that has received extensive research in recent years. Owing to the excellent capability in dealing with label ambiguity, LDL has been widely adopted in many real-world scenarios. Though remarkable progress has been achieved in various tasks, one limitation with existing LDL methods is that they are all based on the i.i.d. assumption that training and test data are identically and independently distributed. As a result, they suffer obvious performance degradation and are no longer applicable when tested in out-of-distribution scenarios, which severely limits the application of LDL in many tasks. In this paper, we identify and investigate the Generalizable Label Distribution Learning (GLDL) problem. To handle such a challenging problem, we delve into the characteristics of GLDL and find that feature-label correlation and label-label correlation are two essential subjects in GLDL. Inspired by this finding, we propose a simple yet effective model-agnostic framework named Domain-Invariant Correlation lEarning (DICE). DICE mines and utilizes the correlation between feature and label that are invariant across different domains to learn a generalizable feature-label correlation by introducing a prior alignment strategy. In the meantime, it leverages a label correlation alignment strategy to further retain the consistency of label-label correlation in different domains. Extensive experiments verify the superior performance of DICE. Our work fills the gap in benchmarks and techniques for practical GLDL problems.