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Found 22 papers, 6 papers with code
Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models
However, modelling the visual and semantic features in a high dimensional (joint embedding) space is computationally expensive, and more complex models often result in trivial improvements in the VQA accuracy.
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from Multi-Vendor X-ray Scans
In the last two decades, luggage scanning has globally become one of the prime aviation security concerns.
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)
Question-Agnostic Attention for Visual Question Answering
Visual Question Answering (VQA) models employ attention mechanisms to discover image locations that are most relevant for answering a specific question.
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.
Cross-Domain Transferability of Adversarial Perturbations
To this end, we propose a framework capable of launching highly transferable attacks that crafts adversarial patterns to mislead networks trained on wholly different domains.
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.
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts
To evaluate our model, we propose a new split for VQA, separating Unknown visual and semantic concepts from the training set.
Task-generalizable Adversarial Attack based on Perceptual Metric
Deep neural networks (DNNs) can be easily fooled by adding human imperceptible perturbations to the images.
Local Gradients Smoothing: Defense against localized adversarial attacks
Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, i. e., carefully perturbed inputs designed to mislead the network at inference time.
Reciprocal Attention Fusion for Visual Question Answering
Existing attention mechanisms either attend to local image grid or object level features for Visual Question Answering (VQA).
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.
Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey
With the availability of low-cost and compact 2. 5/3D visual sensing devices, computer vision community is experiencing a growing interest in visual scene understanding of indoor environments.
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.
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning
Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class, and opposite to the other classes.
Learning deep structured network for weakly supervised change detection
We apply a constrained mean-field algorithm to estimate the pixel-level labels, and use the estimated labels to update the parameters of the CNN in an iterative EM framework.
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.
Separating Objects and Clutter in Indoor Scenes
Objects' spatial layout estimation and clutter identification are two important tasks to understand indoor scenes.
