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- Gated Fusion Network for Single Image Dehazing973
An efficient algorithm to directly restore a clear image from a hazy input using an end-to-end trainable neural network that consists of an encoder and a decoder is proposed.
- Prognostic modelling options for remaining useful life estimation by industry966
Business issues that need to be considered when selecting an appropriate modelling approach for trial are discussed and classification tables and process flow diagrams are presented to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment.
- Slake: A Semantically-Labeled Knowledge-Enhanced Dataset For Medical Visual Question Answering742
A large bilingual dataset, SLAKE, with comprehensive semantic labels annotated by experienced physicians and a new structural medical knowledge base for Med-VQA is presented, which includes richer modalities and covers more human body parts than the currently available dataset.
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- Deblurring by Realistic Blurring494
This paper proposes a new method which combines two GAN models, i.e., a learning-to-Blur GAN (BGAN) and learning- to-DeBlurGAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.
- Similarity Reasoning and Filtration for Image-Text Matching445
The superiority of the proposed Similarity Graph Reasoning and Attention Filtration network with achieving state-of-the-art performances on the Flickr30K and MSCOCO datasets is demonstrated, and the good interpretability of SGR and SAF with extensive qualitative experiments and analyses are demonstrated.
- Reconstruction Network for Video Captioning358
A reconstruction network with a novel encoder-decoder-reconstructor architecture, which leverages both the forward (video to sentence) and backward (sentence to video) flows for video captioning, and can boost the encoding models and leads to significant gains in video caption accuracy.
- Multimodal Convolutional Neural Networks for Matching Image and Sentence353
The m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities to significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.
- Temporally Grounding Natural Sentence in Video345
An effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences using a novel Temporal GroundNet to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence.
- Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos303
A novel semantic conditioned dynamic modulation mechanism, which leverages the sentence semantics to modulate the temporal convolution operations for better correlating and composing the sentence-relevant video contents over time.
- Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis292
A 3D body mesh recovery module is proposed to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape and is able to support a more flexible warping from multiple sources.
- Recurrent Fusion Network for Image Captioning286
This paper proposes a novel recurrent fusion network (RFNet) for the image captioning task, which can exploit the interactions among the outputs of the image encoders and generate new compact and informative representations for the decoder.
- Learning to Answer Questions from Image Using Convolutional Neural Network269
The proposed CNN provides an end-to-end framework with convolutional architectures for learning not only the image and question representations, but also their inter-modal interactions to produce the answer.
- Image Deformation Meta-Networks for One-Shot Learning258
This work combines a meta-learner with an image deformation sub-network that produces additional training examples, and optimize both models in an end-to-end manner to significantly outperform state-of-the-art approaches on widely used one-shot learning benchmarks.
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- PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images245
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations, and the proposed detector, termed as PromptDet, shows superior performance over existing approaches with fewer additional training images andzero manual annotations whatsoever.
- Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning239
This work proposes a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions, and proposes a novel context gating mechanism to balance the contributions from the current event and its surrounding contexts dynamically.
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- Unsupervised Image Captioning234
This paper makes the first attempt to train an image captioning model in an unsupervised manner, and requires an image set, a sentence corpus, and an existing visual concept detector.
- Multi-Granularity Generator for Temporal Action Proposal216
Through temporally adjusting the segment proposals with fine-grained information based on frame actionness, MGG achieves the superior performance over state-of-the-art methods on the public THUMOS-14 and ActivityNet-1.3 datasets.
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- Temporally Grounding Language Queries in Videos by Contextual Boundary-Aware Prediction212
This work proposes an end-to-end boundary-aware model, which uses a lightweight branch to predict semantic boundaries corresponding to the given linguistic information, and outperforms its competitors with a clear margin on three public datasets.
- Learning to Generate Time-Lapse Videos Using Multi-stage Dynamic Generative Adversarial Networks207
This work presents a generative adversarial network (GAN) based two-stage approach to generating realistic time-lapse videos of high resolution and demonstrates the superiority of this model over the state-of-the-art models.
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- Consensus-Aware Visual-Semantic Embedding for Image-Text Matching196
A Consensus-aware Visual-Semantic Embedding model is proposed to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching and learns the associations and alignments between image and text based on the exploited consensus.
- Position Focused Attention Network for Image-Text Matching194
This paper proposes a novel position focused attention network (PFAN) to investigate the relation between the visual and the textual views, and integrates the object position clue to enhance the visual-text joint-embedding learning.
- Controllable Video Captioning With POS Sequence Guidance Based on Gated Fusion Network188
A gating strategy is proposed to dynamically and adaptively incorporate the global syntactic POS information into the decoder for generating each word, which not only boosts the video captioning performance but also improves the diversity of the generated captions.
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- Self-Driving Database Management Systems.174
The architecture of Peloton is presented, the first selfdriving DBMS, which enables new optimizations that are important for modern high-performance DBMSs, but which are not possible today because the complexity of managing these systems has surpassed the abilities of human experts.
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- Towards Unsupervised Deep Image Enhancement With Generative Adversarial Network155
An unsupervised image enhancement generative adversarial network (UEGAN) is presented, which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an <italic>unsupervised> manner, rather than learning on a large number of paired images.
- Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection142
A novel Cross-modality Discrepant Interaction Network (CDINet) for RGB-D SOD is proposed, which differentially models the dependence of two modalities according to the feature representations of different layers, and outperforms state-of-the-art methods both quantitatively and qualitatively.
- Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network141
The proposed no-reference metric achieves the state-of-the-art performance for quality assessment of stereoscopic images, and is even competitive to existing full-reference quality metrics.
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- An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model135
A novel methodology to estimate and predict the functional reliability of a system using system functional indicators and condition indicators of components and how the external covariates are included in the presented methodology is presented.
- Parallel subgraph listing in a large-scale graph131
A novel parallel subgraph listing framework, named PSgL, which completely relies on the graph traversal, and avoids the explicit join operation, and proves the problem of partial subgraph instance distribution for workload balance is NP-hard, and carefully design a set of heuristic strategies.
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- Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video118
This paper localizes a spatio-temporal tube in the video that semantically corresponds to the given sentence, with no reliance on any spatio/temporal annotations during training, and proposes a new attentive interactor to strengthen the matching behaviors of reliable instance-sentence pairs and penalize the unreliable ones.
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- Multimodal learning for facial expression recognition108
The multimodal learning method makes the first attempt to learn the joint representation by considering the texture and landmark modality of facial images, which are complementary with each other, and demonstrates the superiority of the proposed method.
- Localizing Natural Language in Videos107
A localizing network (LNet), working in an end-to-end fashion, to tackle the NLVL task, which first match the natural sentence and video sequence by cross-gated attended recurrent networks to exploit their fine-grained interactions and generate a sentence-aware video representation.
- Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks101
Novel Stacked Cycle-Consistent Adversarial Networks (SCANs) are proposed by decomposing a single translation into multi-stage transformations, which not only boost the image translation quality but also enable higher resolution image-to-image translation in a coarse- to-fine fashion.
- Regularizing RNNs for Caption Generation by Reconstructing the Past with the Present97
A novel architecture, namely Auto-Reconstructor Network (ARNet), which, coupling with the conventional encoder-decoder framework, works in an end-to-end fashion to generate captions, and can help regularize the transition dynamics of recurrent neural networks (RNNs).
- Detection of a casting defect tracked by deep convolution neural network96
The experimental results show that the false rate and missed rate for detection of casting defects are less than 4%, and the accuracy of the defect detection is more than 96%, which proves the robustness of the proposed method.
- Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation96
The authors' analysis shows that the estimated decomposed matrices contain the most essential information of the input kernel, which ensures the proposed network to handle various blurs in a unified framework and generate high-quality deblurring results.
- An integrated safety prognosis model for complex system based on dynamic Bayesian network and ant colony algorithm93
An integrated safety prognosis model (ISPM) considering the randomness, complexity and uncertainty of fault propagation is proposed, developed based on dynamic Bayesian networks to model the propagation of faults in a complex system.
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- Machinery fault diagnosis based on fuzzy measure and fuzzy integral data fusion techniques89
The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.
- Cops-Ref: A New Dataset and Task on Compositional Referring Expression Comprehension87
A new dataset for visual reasoning in context of referring expression comprehension with two main features, and a novel expression engine rendering various reasoning logics that can be flexibly combined with rich visual properties to generate expressions with varying compositionality.
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- Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions85
The intelligent system combining PCA and BP-ANN algorithms, developed in this research, can be adopted by asset managers in different regions to develop suitable maintenance and repair strategies for transformer failure preventions.
- Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning82
A reconstruction network (RecNet) in a novel encoder-decoder-reconstructor architecture, which leverages both forward (video to sentence) and backward (sentence to video) flows for video captioning, is proposed, which significantly boosts the captioning performance.
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- MLLM-Tool: A Multimodal Large Language Model for Tool Agent Learning78
This paper proposes MLLM-Tool, a system incorporating open-source LLMs and multi-modal encoders so that the learned LLMs can be conscious of multi-modal input instruction and then select the function-matched tool correctly.
- Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior78
A Paired Rain Removal Network (PRRNet), which exploits both stereo images and semantic information is presented, and a Semantic-Aware Deraining Module (SADM) is developed which solves both tasks of semantic segmentation and deraining of scenes.
- Learning to Guide Decoding for Image Captioning78
An extension of the encoder-decoder framework by adding a component called guiding network, which models the attribute properties of input images, and its output is leveraged to compose the input of the decoder at each time step.
- Maintenance optimisation of a multi-state series–parallel system considering economic dependence and state-dependent inspection intervals78
The simulation result reveals that maintenance strategies with opportunistic maintenance and state-dependent inspection intervals are more cost-effective when the influence of economic dependence and inspection cost is significant.
- Context-Gated Convolution77
This work proposes one novel Context-Gated Convolution (CGC) to explicitly modify the weights of convolutional layers adaptively under the guidance of global context, which can better extract representative local patterns and compose discriminative features in CNNs.
- Video Re-localization73
This work proposes an innovative cross gated bilinear matching model such that every time-step in the reference video is matched against the attentively weighted query video, and the prediction of the starting and ending time is formulated as a classification problem based on the matching results.
- Degradation modeling and condition-based maintenance of boiler heat exchangers using gamma processes72
A novel degradation model is presented using a physical erosion model and a Gamma process to account for the uncertainties in the thickness degradation process and is applied to a real-world case study of a boiler operating in a real Australian sugar production facility.
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- Relation-aware Instance Refinement for Weakly Supervised Visual Grounding69
This paper proposes a novel context-aware weakly-supervised learning method that incorporates coarse-to-fine object refinement and entity relation modeling into a two-stage deep network, capable of producing more accurate object representation and matching.
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- A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis56
The objectives of this research are to review solar technology development progress and describe the innovation path that has evolved for the solar power domain, and develop a novel technology e-discovery methodology.
- Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding55
A Paired Rain Removal Network (PRRNet) is presented, which exploits both stereo images and semantic information, and a Semantic-Aware Deraining Module (SADM) which solves both tasks of semantic segmentation and deraining of scenes, and a Semantic-Fusion Network (SFNet) and a View-Fusion Network (VFNet) which fuse semantic information and multi-view information respectively.
- Multimodal deep learning for solar radio burst classification54
Experimental results have demonstrated that the proposed multimodal learning network can effectively learn the representation of the solar radio spectrum, and improve the classification accuracy.
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- Liquid Warping GAN With Attention: A Unified Framework for Human Image Synthesis53
This paper proposes to use a 3D body mesh recovery module to disentangle the pose and shape and builds a new dataset, namely Impersonator (iPER) dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis.
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- Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment53
Experimental results demonstrated that the statistical properties of the difference image can well represent the perceptual quality of the stereoscope image, which outperforms the representative RR quality metrics for stereoscopic image and even some full reference (FR) quality metrics.
- Intelligent diagnosis of rotating machinery faults - A review51
A review of a variety of diagnosis techniques that have had demonstrated success when applied to rotating machinery and highlights fault detection and identification techniques based mainly on artificial intelligence approaches.
- Extracting failure time data from industrial maintenance records using text mining50
A text mining approach is proposed to extract accurate failure time data from work orders and downtime data, which identifies downtime events whose descriptions are consistent with urgent unplanned WOs and DD.
- Best basis-based intelligent machine fault diagnosis50
A novel approach for integrated machine fault diagnosis based on localised wavelet packet bases of vibration signals is presented, which can greatly improve the accuracy ofdiagno sis.
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- Adaptive Block-size Transform based Just-Noticeable Difference model for images/videos48
A simple visual quality metric is designed by considering the ABT-based JND masking properties and confirms that the ABTs JND consists well with the HVS, which is comparable to the state-of-the-art metrics.
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- Reduced-reference image quality assessment in reorganized DCT domain47
Experimental results demonstrate that a small number of RR features is sufficient to represent the reference image for the perceptual quality analysis, and the proposed RR IQA can outperform the state-of-the-art RR IQAs, and even the full-reference PSNR and SSIM.
- Latent degradation indicators estimation and prediction: A Monte Carlo approach47
A state space model without linear and Gaussian assumptions is proposed that is used to process vibration and crack depth data from an accelerated test of a gearbox and shows a better fitness result.
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- Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement45
A novel Cycle-Interactive Generative Adversarial Network (CIGAN) is proposed for unsupervised low-light image enhancement, capable of not only better transferring illumination distributions between low/normal-light images but also manipulating detailed signals between two domains, e.g., suppressing/synthesizing realistic noise in the cyclic enhancement/degradation process.
- A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport45
A novel application of Bayesian complex system models as a management tool for decision making is demonstrated using a railway transport case study and customer satisfaction is modelled using data from customer surveys conducted by Queensland Rail, Australia.
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- LMEye: An Interactive Perception Network for Large Language Models43
LMEye is introduced, a human-like eye with a play-and-plug interactive perception network, designed to enable dynamic interaction between LLMs and external visual information, and its effectiveness and scalability on various language models and video understanding are verified.
- Coupled Network for Robust Pedestrian Detection With Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling43
The proposed pedestrian detection method with a couple-network achieves promising results on both two pedestrian datasets, especially on detecting small or occluded pedestrians.
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- Maintenance optimisation of a parallel-series system with stochastic and economic dependence under limited maintenance capacity43
To overcome the “curse of dimensionality” problem where the state space of a parallel-series system increases rapidly with the increased number of components in the system, the factored Markov decision process (FMDP) is employed for maintenance optimisation in this work.
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- SoccerNet 2022 Challenges Results42
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team, and tasks (3-6) were novel, including their underlying data and annotations.
- Non-local NetVLAD Encoding for Video Classification42
This paper describes the solution for the YouTube-8M video understanding challenge organized by Google AI and fuses six different sub-models into one single computational graph, which is categorized into three families, the most effective family is the model with non-local operations following the NetVLAD encoding.
- Quaternion representation based visual saliency for stereoscopic image quality assessment41
Experimental results demonstrate that theVisual saliency maps generated by the proposed method can help significantly boost the performance of SIQA, comparing with other visual saliency models proposed for stereoscopic images.
- Spatio-Temporal Video Re-Localization by Warp LSTM40
To accurately localize the desired tubelets in the reference video, a novel warp LSTM network is proposed, which propagates the spatio-temporal information for a long period and thereby captures the corresponding long-term dependencies.
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- An effective approach to reducing strategy space for maintenance optimisation of multistate series–parallel systems39
An alternative method based on linear programming is developed to overcome difficulties by reducing strategy space of maintenance optimisation of series–parallel systems by at least as effective as the existing methods for strategy space reduction.
- OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion38
A novel unified open-vocabulary detection method called OV-DINO is proposed, which is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework, and a Unified Data Integration pipeline is introduced to enable end-to-end training and eliminate noise from pseudo-label generation by unifying different data sources into detection-centric data format.
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- Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis37
It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process and the technique can be employed for the detection of a simulated exhaust valve fault in this study.
- Neural Stereoscopic Image Style Transfer36
A dual path network for view-consistent style transfer on stereoscopic images is proposed and a novel feature aggregation strategy is proposed to effectively share information between the two paths.
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- Sentence Specified Dynamic Video Thumbnail Generation35
A novel graph convolved video thumbnail pointer (GTP) that leverages a sentence specified video graph convolutional network to model both the sentence-video semantic interaction and the internal video relationships incorporated with the sentence information, based on which a temporal conditioned pointer network is introduced to sequentially generate the sentence specifying video thumbnails.
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- SoccerNet 2023 challenges results34
This report indicates performance trends across tasks: Action spotting is nearing saturation, while ball action spotting improved significantly with advanced end-to-end models and dense video captioning also saw substantial enhancements aligned with Large Language Models advancements.
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- Segmentation of casting defect regions for the extraction of microstructural properties34
A 3D convolutional neural network, a nonlinear topological dimension parameter and an empirical model are proposed for extracting the microstructural properties of casting defect regions efficiently and it is demonstrated that micro structural properties can be extracted via this method.
- Bidirectional image-sentence retrieval by local and global deep matching33
The proposed local and global deep matching model outperforms the state-of-the-art bidirectional retrieval approaches on the Flickr8K, Flickr30K, and MSCOCO datasets and the image and sentence representations exploited in local andglobal levels are demonstrated to play synergic and complementary roles for biddirectional image-sentence retrieval.
- Imaging and representation learning of solar radio spectrums for classification33
Experimental results have demonstrated that the employed network can effectively classify the solar radio image into the labeled categories, and the pre-training process can help improve the classification accuracy.
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- Two-stage Visual Cues Enhancement Network for Referring Image Segmentation31
Through the two-stage enhancement, the proposed TV-Net enjoys better performances in learning fine-grained matching behaviors between the natural language expression and image, especially when the visual information of the referent is inadequate, thus produces better segmentation results.
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- Every Moment Matters: Detail-Aware Networks to Bring a Blurry Image Alive24
A detail-aware network with three consecutive stages to improve the reconstruction quality by addressing specific aspects in the recovery process and produces sharp image sequences with rich details and subtle motion, outperforming the state-of-the-art methods.
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- Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse22
This paper proposes to transfer the knowledge of a network learned from natural images to a sketch network - a new deep net architecture which is term as cousin network, which guides a sketch-recognition network to extract more relevant features that are close to those of natural images, via adversarial training.
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- MARN: Multi-level Attentional Reconstruction Networks for Weakly Supervised Video Temporal Grounding14
- Feature Deformation Meta-Networks in Image Captioning of Novel Objects14
The feature deformation meta-networks (FDM-net), which is trained on source data, and learn to adapt to the novel object features detected by the auxiliary detection model, and the results show the effectiveness of the FDM-nets.
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- Neural Symbolic Representation Learning for Image Captioning11
The first attempt to investigate the ability of neural symbolic representation of the image for the image captioning task is made and it is demonstrated to better generalize to other domains with significant performance improvements compared with existing methods on the cross domain image captioned task.
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- A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues10
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- Fostering Team‐Level Idea Implementation: Leader's Upward Exchange Relationship as a Key Facilitator5
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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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