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- Visual saliency based on multiscale deep features1,361
This paper discovers that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
- Proceedings of the 29th ACM International Conference on Multimedia922
A robust deep learning framework for real-world SR that randomly erases potential adversarial noises in the frequency domain of input images or features and is more insensitive to adversarial attacks and presents more stable SR results than existing models and defenses is proposed.
- Deep Contrast Learning for Salient Object Detection774
This paper proposes an end-to-end deep contrast network that significantly improves the state of the art in salient object detection and extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries.
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- Visual Saliency Detection Based on Multiscale Deep CNN Features394
This paper discovers that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
- Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI384
This survey gives a comprehensive exploration of the latest advancements in Embodied AI, exploring the complexities of MLMs in virtual and real embodied agents, highlighting their significance in facilitating interactions in digital and physical environments.
- Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering348
Extensive experiments demonstrate the superiority of the CMCIR in discovering visual-linguistic causal structures and achieving robust event-level visual question answering.
- Adaptive Context Selection for Polyp Segmentation344
This work proposes an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM), which shows outstanding performance compared with other state-of-the-art methods.
- Multi-label Image Recognition by Recurrently Discovering Attentional Regions338
This work achieves the interpretable and contextualized multi-label image classification by developing a recurrent memorized-attention module that demonstrates superior performances over other existing state-of-the-arts in both accuracy and efficiency.
- Instance-Level Salient Object Segmentation297
This paper presents a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image, and proposes a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours.
- Crowd Counting With Deep Structured Scale Integration Network289
A novel Deep Structured Scale Integration Network (DSSINet) for crowd counting is proposed, which addresses the scale variation of people by using structured feature representation learning and hierarchically structured loss function optimization.
- Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction279
A novel contextualized spatial–temporal network (CSTN) for taxi origin-destination demand prediction, which consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC), and global correlation context (GCC), respectively.
- Dynamic Graph Attention for Referring Expression Comprehension263
This paper proposes a dynamic graph attention network to perform multi-step reasoning by modeling both the relationships among the objects in the image and the linguistic structure of the expression, and constructs a graph for the image with the nodes and edges corresponding to the objects and their relationships respectively.
- Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining262
A non-locally enhanced encoder-decoder network framework that can effectively remove rain-streaks on rainy image of various densities while well preserving the image details, which achieves significant improvements over the recent state-of-the-art methods.
- A Real-Time Cross-Modality Correlation Filtering Method for Referring Expression Comprehension253
A novel Realtime Cross-modality Correlation Filtering method (RCCF) that reformulates the referring expression comprehension as a correlation ï¬ltering process and achieves leading performance in RefClef, RefCOCO, Ref COCO+ and RefC OCOg benchmarks.
- Referring Image Segmentation via Cross-Modal Progressive Comprehension250
A Cross-Modal Progressive Comprehension (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task of referring image segmentation.
- Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules247
A thyroid region prior guided feature enhancement network (TRFE+) is designed for accurate thyroid nodule segmentation and a normalization approach with respect to the channel dimension is applied to alleviate the domain gap during the training process.
- Attention-Aware Face Hallucination via Deep Reinforcement Learning235
A novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image.
- Motion Guided Attention for Video Salient Object Detection216
A multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using twoSub-networks, one sub-network for salient object Detection in still images and the other for motion saliency detection in optical flow images, which significantly outperforms existing state-of-the-art algorithms on a wide range of benchmarks.
- DreamEditor: Text-Driven 3D Scene Editing with Neural Fields213
This paper proposes DreamEditor, a novel framework that enables users to perform controlled editing of neural fields using text prompts by representing scenes as mesh-based neural fields, and generates highly realistic textures and geometry.
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- Linguistic Structure Guided Context Modeling for Referring Image Segmentation211
A "gather-propagate-distribute" scheme to model multimodal context by cross-modal interaction and implement this scheme as a novel Linguistic Structure guided Context Modeling (LSCM) module.
- Cross-Modal Progressive Comprehension for Referring Segmentation202
This paper proposes a cross-modal progressive comprehension (CMPC) scheme to effectively mimic human behaviors and implements it as a CMPC-I (Image) module and aCMPC-V (Video) module to improve referring image and video segmentation models.
- SAM2-UNet: segment anything 2 makes strong encoder for natural and medical image segmentation198
It is proved that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models, and a simple but effective framework is proposed, termed SAM2-UNet, for versatile image segmentation.
- Crowd Counting using Deep Recurrent Spatial-Aware Network198
A unified neural network framework, named Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues in a learnable spatial transform module with a region-wise refinement process, and achieves an improvement on the largest dataset WorldExpo’10 and the most challenging dataset UCF-CC.
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- Improved Distribution Matching for Dataset Condensation189
This paper identifies two important shortcomings of naive distribution matching and addresses them with three novel techniques and outperforms most previous optimization-oriented methods with much fewer computational resources, thereby scaling data condensation to larger datasets and models.
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- Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting184
It is found that incorporating optical and thermal information can greatly help to recognize pedestrians and the proposed approach is universal for multimodal crowd counting and is also capable to achieve superior performance on the ShanghaiTechRGBD dataset.
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- Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition182
This paper investigates how to integrate the semantic relationship propagation between AUs in a deep neural network framework to enhance the feature representation of facial regions, and proposes an AU semantic relationship embedded representation learning (SRERL) framework.
- Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition175
A recurrent attention reinforcement learning framework to iteratively discover a sequence of attentional and informative regions that are related to different semantic objects and further predict label scores conditioned on these regions to facilitate multi-label recognition.
- Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation171
To achieve both inter-domain and intra-domain adaptation, an adversarial adaptive clustering loss is introduced to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains.
- Human-Centric Spatio-Temporal Video Grounding With Visual Transformers163
This work introduces a novel task – Human-centric Spatio-Temporal Video Grounding (HC-STVG), which aims to localize a spatio-temporal tube of the target person from an untrimmed video based on a given textural description.
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- Identity-Preserving Talking Face Generation with Landmark and Appearance Priors160
A novel Transformer-based landmark generator is devised to infer lip and jaw landmarks from the audio and a video rendering model is built to translate the generated landmarks into face images, which helps generate realistic and identity-preserving visual content.
- Lesion-Aware Dynamic Kernel for Polyp Segmentation146
A lesion-aware dynamic network (LDNet) for polyp segmentation is proposed, which is a traditional u-shape encoder-decoder structure incorporated with a dynamic kernel generation and updating scheme that endows the model with powerful segmentation performance and generalization capability.
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- Large multimodal agents: a survey136
This paper conducts a systematic review of LLM-driven multimodal agents, introducing the essential components involved in developing LMAs and categorizing the current body of research into four distinct types.
- Semi-Supervised Video Salient Object Detection Using Pseudo-Labels136
This paper presents an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module and proposes a novel method for generating pixel-level pseudo-labels from sparsely annotated frames.
- Weakly Supervised Salient Object Detection Using Image Labels135
This paper proposes to use the combination of a coarse salient object activation map from the classification network and saliency maps generated from unsupervised methods as pixel-level annotation, and develops a simple yet very effective algorithm to train fully convolutional networks for salient object detection supervised by these noisy annotations.
- Collaborative Training Between Region Proposal Localization and Classification for Domain Adaptive Object Detection131
This paper is the first to reveal that the region proposal network (RPN) and region proposal classifier~(RPC) in the endemic two-stage detectors demonstrate significantly different transferability when facing large domain gap.
- Tree-Structured Policy Based Progressive Reinforcement Learning for Temporally Language Grounding in Video127
A novel Tree-Structured Policy based Progressive Reinforcement Learning (TSP-PRL) framework to sequentially regulate the temporal boundary by an iterative refinement process, which contributes to efficiently decomposing complex policies into an interpretable primitive action.
- X -Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning122
This study investigates a cross-modal knowledge transfer using Transformer for 3D dense captioning through the knowledge distillation enabled by a teacher-student framework and proposes X-Trans2Cap, which outperforms previous state-of-the-art models by a large margin.
- TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts121
TIP-Editor is proposed, a 3D scene editing framework that accepts both text and image prompts and a 3D bounding box to specify the editing region, and employs a stepwise 2D personalization strategy to better learn the representation of the existing scene and the reference image.
- Graph-Structured Referring Expression Reasoning in the Wild119
A scene graph guided modular network (SGMN), which performs reasoning over a semantic graph and a scene graph with neural modules under the guidance of the linguistic structure of the expression, which significantly outperforms existing state-of-the-art algorithms on the new Ref-Reasoning dataset.
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- Cross-Modal Self-Attention with Multi-Task Pre-Training for Medical Visual Question Answering110
This work reformulates image feature pre-training as a multi-task learning paradigm and witness its extraordinary superiority, forcing it to take into account the applicability of features for the specific image comprehension task.
- Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid107
A novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales, and achieves considerable improvements on all empirical settings.
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- Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation102
A novel adapter called Bridger is proposed to facilitate cross-modal information exchange and inject task-specific information into the pre-trained model and design a lightweight decoder for image segmentation.
- An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation101
This work proposes to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations and achieves the state-of-the-art performance on two challenging domain adaptation tasks for semantic segmentation.
- Peeking into Occluded Joints: A Novel Framework for Crowd Pose Estimation94
This work proposes a novel OPEC-Net framework together with a new Occluded Pose (OCPose) dataset with 9k annotated images, which is the most complex occlusion dataset with respect to average IoU between adjacent instances.
- Contrast-Oriented Deep Neural Networks for Salient Object Detection92
This paper develops hybrid contrast-oriented deep neural networks that can significantly outperform the state of the art in terms of all popular evaluation metrics.
- SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training91
An asymmetric graph-based encoder-decoder pre-training architecture named SkeletonMAE is built, which embeds skeleton joint sequence into graph convolutional network and reconstructs the masked skeleton joints and edges based on the prior human topology knowledge.
- Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video88
A dual-branch network is proposed which takes as input the proposals with multi-granularities in both spatial-temporal domains and progressively refines the corresponding branch and the whole framework, set up to employ dual-path mutual guidance in a recurrent manner.
- Semi-DETR: Semi-Supervised Object Detection with Detection Transformers87
This work proposes a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one- to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage.
- Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning81
This work proposes a novel cross-modal matching strategy to detect OOD samples that substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
- Efficient Crowd Counting via Structured Knowledge Transfer81
A novel Structured Knowledge Transfer (SKT) framework, which fully exploits the structured knowledge of a well-trained teacher network to generate a lightweight but still highly effective student network that can derive the layer-wise and cross-layer knowledge from the teacher networkto learn compact yet effective features.
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- Causal Reasoning Meets Visual Representation Learning: A Prospective Study76
This paper conducts a comprehensive review of existing causal reasoning methods for visual representation learning, covering fundamental theories, models, and datasets, and proposes some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms inVisual representation learning.
- Relationship-Embedded Representation Learning for Grounding Referring Expressions76
A CMRE to adaptively highlight objects and relationships related to the given expression with a cross-modal attention mechanism, and represent the extracted information as a language-guided visual relation graph and a Gated Graph Convolutional Network to compute multimodal semantic contexts.
- Attentive Crowd Flow Machines75
A unified neural network module, called Attentive Crowd Flow Machine~ (ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism, and achieves significant improvements over the state-of-the-art methods.
- Scene-Intuitive Agent for Remote Embodied Visual Grounding71
This paper focuses on the Remote Embodied Visual Referring Expression in Real Indoor Environments task, called REVERIE, where an agent is asked to correctly localize a remote target object specified by a concise high-level natural language instruction, and proposes a two-stage training pipeline.
- Diffusion-Based Data Augmentation for Nuclei Image Segmentation68
The first diffusion-based augmentation method for nuclei segmentation is introduced and results show that by augmenting 10% labeled real dataset with synthetic samples, one can achieve comparable segmentation results with the fully-supervised baseline.
- Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos68
A Boundary Adaptive Refinement framework that resorts to reinforcement learning (RL) to guide the process of progressively refining the temporal boundary and is offered the first attempt to extend RL to temporal localization task with weak supervision.
- Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks68
Non-local context encoder (NLCE) is proposed to model short- and long-range spatial dependencies and encode global contexts for strengthening feature activations by channel-wise attention and improves the robustness of other CNN-based biomedical image segmentation methods.
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- Being Comes from Not-Being: Open-Vocabulary Text-to-Motion Generation with Wordless Training65
This paper pretrain a motion generator that learns to reconstruct the full motion from the masked motion, inspired by the prompt learning in NLP, and proposes the first text-pose alignment model for measuring the alignment between texts and 3D poses.
- Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation65
A novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM) is proposed, which fully exploits heterogeneous information of historical data to jointly learn the evolutionary patterns of OD and DO ridership and develops a unified Seq2Seq network to forecast the future OD andDO ridership simultaneously.
- Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation61
This work proposes a collaborative spatial-temporal encoder-decoder framework which contains a 3D temporal encoder over the video clip to recognize the queried actions, and a 2D spatial encoding over the target frame to accurately segment the querying actors.
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- Cross-Level Contrastive Learning and Consistency Constraint for Semi-Supervised Medical Image Segmentation59
This work proposes a cross-level contrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image segmentation and devise a novel consistency constraint that compares the predictions of patches against those of the full image.
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- Cross-Modal Causal Representation Learning for Radiology Report Generation56
A two-stage framework named Cross-Modal Causal Representation Learning (CMCRL), consisting of the Radiological Cross-modal Alignment and Reconstruction Enhanced (RadCARE) pre-training and the Visual-Linguistic Causal Intervention (VLCI) fine-tuning, which establishes robust cross-modal correlations even with incomplete data.
- Context-Aware Semantic Inpainting52
An improved GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context.
- Person Re-Identification Using Multiple Experts with Random Subspaces52
This paper presents a simple and effective multi- expert approach based on random subspaces for person re- identification across non-overlapping camera views that is able to significantly improve the performance of existing state-of-the-art techniques.
- Visual Causal Scene Refinement for Video Question Answering50
To discover critical video segments and frames that serve as the visual causal scene for generating reliable answers, a causal analysis of VideoQA is presented and a framework for cross-modal causal relational reasoning, named Visual Causal Scene Refinement (VCSR), is proposed.
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- Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework48
A novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentsation in a semi-supervised manner is proposed.
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- Weighted attentional blocks for probabilistic object tracking47
Experimental results show that the proposed algorithm is able to efficiently track salient objects and is better accounted for partial occlusions and large variations in appearance.
- A Causal Inference Look at Unsupervised Video Anomaly Detection46
A causal graph is proposed to analyze the confounding effect of the pseudo label generation process and a simple yet effective causal inference based framework is introduced to disentangle the noisy pseudo label's impact to make final anomaly detection.
- Artificial Intelligence Assisting the Early Detection of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study45
The ResNet algorithm-based AI diagnosis system provided accurate TB diagnosis, which could have broad prospects in clinical application forTB diagnosis, especially in poor regions with high TB incidence.
- Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation43
A novel end-to-end network is proposed, Globally Guided Progressive Fusion Network, as an effective and efficient solution to volumetric segmentation, which involves both global features and complicated 3D geometric information.
- Visual Question Reasoning on General Dependency Tree43
A novel reasoning network called Adversarial Composition Modular Network (ACMN), capable of building an interpretable VQA system that gradually dives the image cues following a question-driven reasoning route and makes global reasoning by incorporating the learned knowledge of all attention modules in a principled manner.
- Unsupervised Domain Adaptive Salient Object Detection through Uncertainty-Aware Pseudo-Label Learning42
This paper proposes to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations, and outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets, and is even comparable to fully-supervised ones.
- ROSA: Robust Salient Object Detection Against Adversarial Attacks42
This paper is the first one that mounts successful adversarial attacks on salient object detection models and verifies that adversarial samples are effective on a wide range of existing methods and proposes a novel end-to-end trainable framework to enhance the robustness for arbitrary FCN-based salient object Detection models against adversarial attack.
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- Self-supervised Correction Learning for Semi-supervised Biomedical Image Segmentation41
This work adopts a coarse-to-fine strategy and proposes a self-supervised correction learning paradigm for semi- supervised biomedical image segmentation, and designs a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting.
- AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning39
A novel framework, termed AlignSAM, designed for automatic prompting for aligning SAM to an open context through reinforcement learning, which enables the generality of the SAM model across diverse downstream tasks while keeping its parameters frozen.
- Divide and Adapt: Active Domain Adaptation via Customized Learning39
This work presents Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target instances into four categories with stratified transferable properties and proposes an informativeness score that unifies the data partitioning criteria.
- Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning39
A novel attention-aware face hallucination (Attention-FH) framework is developed, which recurrently learns to attend a sequence of patches and performs facial part enhancement by fully exploiting the global interdependency of the image.
- Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction38
A novel neural network framework termed cross-modal message propagation network (CMMPNet) is introduced, which fully benefits the complementary different modal data (i.e., aerial images and crowdsourced trajectories), and outperforms current state-of-the-art methods by large margins.
- A Causal Debiasing Framework for Unsupervised Salient Object Detection38
This paper uses causal intervention to perform de-confounded model training to minimize the contrast distribution bias and proposes an image-level weighting strategy that softly weights each image's importance according to the spatial distribution bias map.
- Inter-domain mixup for semi-supervised domain adaptation36
This paper introduces a cross-domain feature alignment strategy, Inter-domain Mixup, that incorporates label information into model adaptation and outperforms existing state-of-the-art methods, achieving significant accuracy improvements on popular SSDA benchmarks, including DomainNet, Office-Home, and Office-31.
- PointMatch: A consistency training framework for weakly supervised semantic segmentation of 3D point clouds36
This work proposes a novel framework, PointMatch, that stands on both data and label, by applying consistency regularization to sufficiently probe information from data itself and leveraging weak labels as assistance at the same time, which achieves the state-of-the-art performance under various weakly-supervised schemes on both ScanNet-v2 and S3DIS datasets.
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- Depthwise Nonlocal Module for Fast Salient Object Detection Using a Single Thread35
A novel depthwise nonlocal module (DNL) is proposed, which implicitly models contrast via harvesting intrachannel and interchannel correlations in a self-attention manner and achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
- Facial Landmark Machines: A Backbone-Branches Architecture With Progressive Representation Learning35
This paper proposes a novel cascaded backbone-branches fully convolutional neural network (BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings and confirms that high-quality facial landmarks localized with the proposed network can also improve the precision and recall of face detection.
- ODMixer: Fine-Grained Spatial-Temporal MLP for Metro Origin-Destination Prediction33
This work learns traffic evolution from the perspective of all OD pairs and proposes a fine-grained spatial-temporal MLP architecture for metro OD prediction, namely ODMixer, which has double-branch structure and involves the Channel Mixer, the Multi-view Mixer, and the Bidirectional Trend Learner.
- BoxPolyp: Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations32
A boosted BoxPolyp model to make full use of both accurate mask and extra coarse box annotations is proposed, which outperforms previous state-of-the-art methods by a large margin.
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- A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning31
To improve the efficiency of HMC, a new regime to automatically control the length of trajectories is proposed, which allows the algorithm to move with adaptive step sizes along the search direction at different positions and revisit the reason for high computational cost of adversarial training under the view of MCMC and design a new generative method called Contrastive Adversarial Training (CAT).
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- ECC-PolypDet: Enhanced CenterNet With Contrastive Learning for Automatic Polyp Detection19
The Enhanced CenterNet with Contrastive Learning (ECC-PolypDet) is proposed, a two-stage training & end-to-end inference framework that leverages images and bounding box annotations to train a general model and fine-tune it based on the inference score to obtain a final robust model.
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- Gradient-Rebalanced Uncertainty Minimization for Cross-Site Adaptation of Medical Image Segmentation1
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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