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Found 36 papers, 19 papers with code
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning.
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning.
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results.
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously.
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning
To systematically measure the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i. e., Charades-CG and ActivityNet-CG.
Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution
Convolutional neural networks based single-image super-resolution (SISR) has made great progress in recent years.
MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning
We propose the Multimodal relAtional Graph adversarIal inferenCe (MAGIC) framework for diverse and unpaired TextCap.
From Beginner to Master: A Survey for Deep Learning-based Single-Image Super-Resolution
In this survey, we give an overview of DL-based SISR methods and group them according to their targets, such as reconstruction efficiency, reconstruction accuracy, and perceptual accuracy.
FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation
Specifically, FBSNet employs a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch.
Transformer for Single Image Super-Resolution
LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA).
Structure-Preserving Deraining with Residue Channel Prior Guidance
Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures.
Adaptive Hierarchical Graph Reasoning with Semantic Coherence for Video-and-Language Inference
Secondly, we introduce semantic coherence learning to explicitly encourage the semantic coherence of the adaptive hierarchical graph network from three hierarchies.
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result.
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network
Recently, the single image super-resolution (SISR) approaches with deep and complex convolutional neural network structures have achieved promising performance.
Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing
To achieve this, we propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales.
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background
Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge.
Robust Meta-learning with Noise via Eigen-Reptile
In particular, we first cast the meta-overfitting problem (overfitting on sampling and label noise) as a gradient noise problem since few available samples cause meta-learner to overfit on existing examples (clean or corrupted) of an individual task at every gradient step.
Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations
In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of the mean representation.
MDCN: Multi-scale Dense Cross Network for Image Super-Resolution
Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model.
Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling
In this paper, we focus on enhancing the generalization ability of the VIST model by considering the few-shot setting.
Disentangle Perceptual Learning through Online Contrastive Learning
Perceptual learning approaches like perceptual loss are empirically powerful for such tasks but they usually rely on the pre-trained classification network to provide features, which are not necessarily optimal in terms of visual perception of image transformation.
Universal Phone Recognition with a Multilingual Allophone System
Multilingual models can improve language processing, particularly for low resource situations, by sharing parameters across languages.
Towards Zero-shot Learning for Automatic Phonemic Transcription
The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes.
Fast Loop Closure Detection via Binary Content
Our method is compared with the state-of-the-art loop closure detection methods and the results show that it outperforms the traditional methods at both recall rate and speed.
HighEr-Resolution Network for Image Demosaicing and Enhancing
However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing.
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation
Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e. g., television) using only visual observations.
reinforcement-learning
Unsupervised Reinforcement Learning
+1
Walking with MIND: Mental Imagery eNhanceD Embodied QA
The experimental results and further analysis prove that the agent with the MIND module is superior to its counterparts not only in EQA performance but in many other aspects such as route planning, behavioral interpretation, and the ability to generalize from a few examples.
Adversarial camera stickers: A physical camera-based attack on deep learning systems
In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself?
A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling
This paper compares five types of pooling functions both theoretically and experimentally, with special focus on their performance of localization.
Sound Audio and Speech Processing
Progressive Feature Fusion Network for Realistic Image Dehazing
Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium.
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks
Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers.
Multi-scale Residual Network for Image Super-Resolution
Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB).
Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval
Constructing a joint representation invariant across different modalities (e. g., video, language) is of significant importance in many multimedia applications.
Ranked #15 on
Video Retrieval
on MSR-VTT
A Comparison of deep learning methods for environmental sound
On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector.
Learning Filter Banks Using Deep Learning For Acoustic Signals
Based on the procedure of log Mel-filter banks, we design a filter bank learning layer.
Very Deep Convolutional Neural Networks for Raw Waveforms
Our CNNs, with up to 34 weight layers, are efficient to optimize over very long sequences (e. g., vector of size 32000), necessary for processing acoustic waveforms.
