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- Recent advances in convolutional neural networks6,499
This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing.
- Deep learning for event-driven stock prediction866
This work proposes a deep learning method for event-driven stock market prediction that can achieve nearly 6% improvements on S&P 500 index prediction and individual stock prediction, respectively, compared to state-of-the-art baseline methods.
- Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation707
For the first time, a bottom-up approach could deliver state-of-the-art results on panoptic segmentation, and performs on par with several top-down approaches on the challenging COCO dataset.
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- Weakly Supervised Action Localization by Sparse Temporal Pooling Network400
This work proposes a weakly supervised temporal action localization algorithm on untrimmed videos using convolutional neural networks that attains state-of-the-art results on the THUMOS14 dataset and outstanding performance on ActivityNet1.3 even with its weak supervision.
- A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding299
A novel framework for SLU to better incorporate the intent information, which further guiding the slot filling is proposed, which achieves the state-of-the-art performance and outperforms other previous methods by a large margin.
- Color image encryption by using Arnold transform and color-blend operation in discrete cosine transform domains229
In this encryption scheme the operations mentioned above are performed twice continuously, so the parameters of Arnold transform and the random angle serve as the key of the color image encryption method.
- Remote sensing image fusion via wavelet transform and sparse representation152
The proposed fusion method can well preserve the spectral information and the spatial detail information of the original images, and could well maintain the spectral characteristics of fusion images with a high spatial resolution.
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- Knowledge-Driven Event Embedding for Stock Prediction130
A joint model to combine knowledge graph information into the objective function of an event embedding learning model is proposed, which is more capable of obtaining better event embeddings and making more accurate prediction on stock market volatilities.
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- Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association111
A novel and efficient way to obtain discriminative appearance-based tracklet affinity models that jointly learns the convolutional neural networks (CNNs) and temporally constrained metrics under a unified framework is proposed.
- GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling107
This paper proposes a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate performance while being 11.5 times faster.
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- Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog99
This work investigates methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge and proposes a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain.
- Learning Invariant Color Features for Person Reidentification90
This paper proposes an approach for learning color patterns from pixels sampled from images across two camera views using color as the only cue, and compares it with all the photometric invariant color spaces and shows superior performance over all of them.
- Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network89
The results show that CIMM offers a powerful paradigm for effectively identifying users’ consumption intention based on their social media data, and suggest that a great potential for the model to significantly increase effectiveness of product recommendations and targeted advertising.
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- Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding56
A hybrid Hessian/intensity based method to segment and quantify shape and diameter of the blood vessels innervating capillary beds that are imaged by functional OCT in vivo is proposed and believes that this method will play an important role in the quantification of micro-angiograms for microcirculation research in ophthalmology and diagnosing retinal eye diseases involved with microcirculation.
- Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications55
A review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications is proposed, with the intention of inspiring future research endeavors.
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- Carrier‐Free ATP‐Activated Nanoparticles for Combined Photodynamic Therapy and Chemotherapy under Near‐Infrared Light38
This study considered adenosine triphosphate (ATP), which is highly upregulated in tumor cells, as a biomarker and developed ingenious ATP-activated nanoparticles (CDNPs) that are directly self-assembled from near-infrared photosensitizer (Cy-I) and amphiphilic Cd(II) complex (DPA-Cd).
- Is Graph Structure Necessary for Multi-hop Question Answering?37
A strong baseline model is constructed to establish that, with the proper use of pre-trained models, graph structure may not be necessary for textual multi-hop reasoning, and graph-attention can be considered as a special case of self-att attention.
- Theory and method of genetic-neural optimizing cut-off grade and grade of crude ore37
Genetic algorithm and neural networks nesting method are used in this research to simulate the highly complexity and highly non-linear relationship between variables in mining system, to optimize the cut-off grade and grade of crude ore.
- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
- Gadolinium Sulfate Modified by Formate To Obtain Optimized Magneto-Caloric Effect35
Three new Gd(III) based coordination polymers obtained by modifying gadolinium sulfate are beneficial for the magneto-caloric effect (MCE) and magnetic studies reveal that with the more formate anions present, the larger the negative value of magnetic entropy change is.
- Learning from Semantic Alignment between Unpaired Multiviews for Egocentric Video Recognition29
Semantics-based Unpaired Multiview Learning (SUM-L) is proposed, the key idea is to build cross-view pseudopairs and do view-invariant alignment by leveraging the semantic information of videos.
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- A Joint Segmentation and Classification Framework for Sentiment Analysis23
A log-linear model is used to score each segmentation candidate, and exploit the phrasal information of top-ranked segmentations as features to build the sentiment classifier.
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- Neural Natural Logic Inference for Interpretable Question Answering19
This paper investigates a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models.
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- Progressive Neighborhood Aggregation for Semantic Segmentation Refinement8
A progressive neighborhood aggregation (PNA) framework to refine the semantic segmentation prediction, resulting in an end-to-end solution that can perform the coarse prediction and refinement in a unified network.
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- Clustering Product Aspects Using Two Effective Aspect Relations for Opinion Mining7
A hierarchical clustering algorithm is designed to cluster these aspects into different groups, in which aspect similarity computation is conducted with the relevant aspect set and irrelevant aspect set of each product aspect.
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- Aspect-Object Alignment with Integer Linear Programming in Opinion Mining6
A novel and important sentiment analysis task, termed aspect-object alignment, to solve the "object neglect" problem and designs a two-step framework for this task, which incorporates three sets of features, namely, the basic, relational, and special target features.
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- One-shot In-context Part Segmentation3
This framework offers a novel approach to part segmentation that is training-free, flexible, and data-efficient, requiring only a single in-context example for precise segmentation with superior generalization ability.
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- Incremental Structured Prediction Using a Global Learning and Beam-Search Framework1
This tutorial discusses a framework for incremental left-to-right structured predication, which makes use of global discriminative learning and beam-search decoding, and gives an introduction to the algorithms and efficient implementations.
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- Hierarchical Grafting Network With Structural Alignment for Ultra-High Resolution Image Segmentation–
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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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