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Found 33 papers, 5 papers with code
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction
In this work, we propose a clustering-based loss correction framework named Feature Cluster Loss Correction (FCLC), to address these two problems.
360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network
To enhance the performance and effect of AR/VR applications and visual assistance and inspection systems, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics.
Flexible uniform-sampling foveated Fourier single-pixel imaging
At a sampling ratio of 0. 0084 referring to HR FSI with 1024*768 pixels, experimentally, by UFFSI with 255*341 cells of 89% reduction in data redundancy, the ROI has a significantly better imaging quality to meet imaging needs.
Uncovering Hidden Connections: Iterative Tracking and Reasoning for Video-grounded Dialog
In response to this gap, we present an iterative tracking and reasoning strategy that amalgamates a textual encoder, a visual encoder, and a generator.
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis
Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information from multiple sources (e. g., language, video, and audio), the potential sentiment-irrelevant and conflicting information across modalities may hinder the performance from being further improved.
Ranked #1 on
Multimodal Sentiment Analysis
on CMU-MOSEI
(Acc-7 metric)
ELIP: Efficient Language-Image Pre-training with Fewer Vision Tokens
Learning a versatile language-image model is computationally prohibitive under a limited computing budget.
SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture
To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN.
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
Fair comparisons based on nineteen famous large-scale instances (with 10, 000 to 10, 000, 000 cities) show that HDR is highly competitive against existing state-of-the-art TSP algorithms, in terms of both efficiency and solution quality.
DiffuseGAE: Controllable and High-fidelity Image Manipulation from Disentangled Representation
To further explore the latent space of Diff-AE and achieve a generic editing pipeline, we proposed a module called Group-supervised AutoEncoder(dubbed GAE) for Diff-AE to achieve better disentanglement on the latent code.
GenerTTS: Pronunciation Disentanglement for Timbre and Style Generalization in Cross-Lingual Text-to-Speech
Cross-lingual timbre and style generalizable text-to-speech (TTS) aims to synthesize speech with a specific reference timbre or style that is never trained in the target language.
Learning Variable Impedance Skills from Demonstrations with Passivity Guarantee
The proposed approach involves the estimation of full stiffness matrices from human demonstrations, which are then combined with sensed forces and motion information to create a model using the non-parametric method.
Noise-Resistant Multimodal Transformer for Emotion Recognition
To this end, we present a novel paradigm that attempts to extract noise-resistant features in its pipeline and introduces a noise-aware learning scheme to effectively improve the robustness of multimodal emotion understanding.
LiteG2P: A fast, light and high accuracy model for grapheme-to-phoneme conversion
As a key component of automated speech recognition (ASR) and the front-end in text-to-speech (TTS), grapheme-to-phoneme (G2P) plays the role of converting letters to their corresponding pronunciations.
Time flies by: Analyzing the Impact of Face Ageing on the Recognition Performance with Synthetic Data
The vast progress in synthetic image synthesis enables the generation of facial images in high resolution and photorealism.
A Survey on Computationally Efficient Neural Architecture Search
Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs).
Temporally and Spatially variant-resolution illumination patterns in computational ghost imaging
For the same number of measurements, the method using temporally variable-resolution illumination patterns has better imaging quality than CGI, but it is less robust to noise.
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding
To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.
Generation of Non-Deterministic Synthetic Face Datasets Guided by Identity Priors
Mated samples are generated by manipulating latent vectors, and more precisely, we exploit Principal Component Analysis (PCA) to define semantically meaningful directions in the latent space and control the similarity between the original and the mated samples using a pre-trained face recognition system.
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions
Our results show that our model can predict 4-hour future trends with high accuracy in the Forex dataset, which is crucial in realistic scenarios to assist foreign exchange trading decision making.
Expression Snippet Transformer for Robust Video-based Facial Expression Recognition
To this end, we propose to decompose each video into a series of expression snippets, each of which contains a small number of facial movements, and attempt to augment the Transformer's ability for modeling intra-snippet and inter-snippet visual relations, respectively, obtaining the Expression snippet Transformer (EST).
Ranked #14 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
On the Applicability of Synthetic Data for Face Recognition
Face verification has come into increasing focus in various applications including the European Entry/Exit System, which integrates face recognition mechanisms.
Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference
Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document.
VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware
In our evaluation, our implementation of VirtualFlow for TensorFlow achieved strong convergence guarantees across different hardware with out-of-the-box hyperparameters, up to 48% lower job completion times with resource elasticity, and up to 42% higher throughput with heterogeneous training.
From Federated Learning to Federated Neural Architecture Search: A Survey
Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern.
Distributed, Parallel, and Cluster Computing
MIPGAN -- Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN
Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset.
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
An alternative solution is to use out-of-core methods instead of, or in addition to, data parallelism.
Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection
\textit{(i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs?}
Hierarchy-Aware Global Model for Hierarchical Text Classification
Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy.
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search
Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms.
Recovering compressed images for automatic crack segmentation using generative models
We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method and demonstrate the remarkable performance of the method taking advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed.
Complex Question Decomposition for Semantic Parsing
We conduct experiments on COMPLEXWEBQUESTIONS which is a large scale complex question semantic parsing dataset, results show that our model achieves significant improvement compared to state-of-the-art methods.
Ranked #1 on
Semantic Parsing
on complexWebQuestions-V1.0
Pretraining-Based Natural Language Generation for Text Summarization
For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence.
Ranked #30 on
Abstractive Text Summarization
on CNN / Daily Mail
EmbedJoin: Efficient Edit Similarity Joins via Embeddings
Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems.
Databases
