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Found 48 papers, 27 papers with code
Transformer Scale Gate for Semantic Segmentation
Effectively encoding multi-scale contextual information is crucial for accurate semantic segmentation.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Learning Enriched Features for Fast Image Restoration and Enhancement
In the former case, spatial details are preserved but the contextual information cannot be precisely encoded.
Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization
Due to its high societal impact, deepfake detection is getting active attention in the computer vision community.
Semantic-Aware Domain Generalized Segmentation
In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data.
Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds
With that, we develop a large-scale synthetic scene flow dataset GTA-SF.
Transformers in Medical Imaging: A Survey
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators.
Unpaired Referring Expression Grounding via Bidirectional Cross-Modal Matching
To avoid the laborious annotation in conventional referring grounding, unpaired referring grounding is introduced, where the training data only contains a number of images and queries without correspondences.
ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues
Firstly, we analyze CLIP for unsupervised open-category proposal generation and design an objectness score based on our empirical analysis on proposal selection.
A Volumetric Transformer for Accurate 3D Tumor Segmentation
This paper presents a Transformer architecture for volumetric medical image segmentation.
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Ranked #1 on
Single Image Deraining
on Test1200
MTGLS: Multi-Task Gaze Estimation with Limited Supervision
Our proposed framework outperforms the unsupervised state-of-the-art on CAVE (by 6. 43%) and even supervised state-of-the-art methods on Gaze360 (by 6. 59%) datasets.
Automatic Gaze Analysis: A Survey of Deep Learning based Approaches
Eye gaze analysis is an important research problem in the field of Computer Vision and Human-Computer Interaction.
Intriguing Properties of Vision Transformers
We show and analyze the following intriguing properties of ViT: (a) Transformers are highly robust to severe occlusions, perturbations and domain shifts, e. g., retain as high as 60% top-1 accuracy on ImageNet even after randomly occluding 80% of the image content.
On Generating Transferable Targeted Perturbations
To this end, we propose a new objective function that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains.
Orthogonal Projection Loss
The CE loss encourages features of a class to have a higher projection score on the true class-vector compared to the negative classes.
Multi-Stage Progressive Image Restoration
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Ranked #3 on
Single Image Deraining
on Rain100H
Low Light Image Enhancement via Global and Local Context Modeling
These contexts can be crucial towards inferring several image enhancement tasks, e. g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent.
Transformers in Vision: A Survey
Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems.
D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations
The proposed formulation comprises a discriminative and a denoising loss term for enhancing temporal action localization.
Ranked #2 on
Weakly Supervised Action Localization
on THUMOS’14
Meta-learning the Learning Trends Shared Across Tasks
This demonstrates their ability to acquire transferable knowledge, a capability that is central to human learning.
Synthesizing the Unseen for Zero-shot Object Detection
The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference.
Ranked #1 on
Zero-Shot Object Detection
on PASCAL VOC'07
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Stylized Adversarial Defense
In contrast to existing adversarial training methods that only use class-boundary information (e. g., using a cross entropy loss), we propose to exploit additional information from the feature space to craft stronger adversaries that are in turn used to learn a robust model.
Self-supervised Knowledge Distillation for Few-shot Learning
Our experiments show that, even in the first stage, self-supervision can outperform current state-of-the-art methods, with further gains achieved by our second stage distillation process.
Ranked #10 on
Few-Shot Image Classification
on FC100 5-way (5-shot)
A Self-supervised Approach for Adversarial Robustness
Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems e. g., for classification, segmentation and object detection.
iTAML: An Incremental Task-Agnostic Meta-learning Approach
In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks.
CycleISP: Real Image Restoration via Improved Data Synthesis
This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline.
Ranked #9 on
Image Denoising
on DND
(using extra training data)
Learning Enriched Features for Real Image Restoration and Enhancement
With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing.
Ranked #4 on
Image Denoising
on DND
Towards Robust and Reproducible Active Learning Using Neural Networks
Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions.
Random Path Selection for Continual Learning
In order to maintain an equilibrium between previous and newly acquired knowledge, we propose a simple controller to dynamically balance the model plasticity.
Ranked #7 on
Continual Learning
on F-CelebA (10 tasks)
Gaussian Affinity for Max-Margin Class Imbalanced Learning
Real-world object classes appear in imbalanced ratios.
Unsupervised Primitive Discovery for Improved 3D Generative Modeling
Using the primitive parts for shapes as attributes, a parameterized 3D representation is modeled in the first stage.
An Adaptive Random Path Selection Approach for Incremental Learning
In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage.
Ranked #7 on
Incremental Learning
on ImageNet100 - 10 steps
(Average Incremental Accuracy Top-5 metric)
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection
Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images (FGAI).
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Deep neural networks are vulnerable to adversarial attacks, which can fool them by adding minuscule perturbations to the input images.
Ranked #4 on
Adversarial Defense
on CIFAR-10
Max-margin Class Imbalanced Learning with Gaussian Affinity
Real-world object classes appear in imbalanced ratios.
Striking the Right Balance with Uncertainty
Rare classes tend to get a concentrated representation in the classification space which hampers the generalization of learned boundaries to new test examples.
Image Super-Resolution as a Defense Against Adversarial Attacks
The proposed scheme is simple and has the following advantages: (1) it does not require any model training or parameter optimization, (2) it complements other existing defense mechanisms, (3) it is agnostic to the attacked model and attack type and (4) it provides superior performance across all popular attack algorithms.
Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation
Our model simultaneously learns to match the data, reconstruction loss and the latent distributions of real and fake images to improve the quality of generated samples.
Regularization of Deep Neural Networks with Spectral Dropout
We cast the proposed approach in the form of regular Convolutional Neural Network (CNN) weight layers using a decorrelation transform with fixed basis functions.
Scene Categorization With Spectral Features
To the best of our knowledge, this is the first attempt to use deep learning based spectral features explicitly for image classification task.
Joint Registration and Representation Learning for Unconstrained Face Identification
We validate the proposed scheme on template based unconstrained face identification.
Contractive Rectifier Networks for Nonlinear Maximum Margin Classification
The contractive constraints ensure that the achieved separating margin in the input space is larger than or equal to the separating margin in the output layer.
Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data
Class imbalance is a common problem in the case of real-world object detection and classification tasks.
A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification
This paper introduces a new learnable feature descriptor called "spatial layout and scale invariant convolutional activations" to deal with these challenges.
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
To this end, we introduce a new large-scale dataset of 1300 object categories which are commonly present in indoor scenes.
Learning Non-Linear Reconstruction Models for Image Set Classification
We propose a deep learning framework for image set classification with application to face recognition.
