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Found 26 papers, 13 papers with code
Lasagna: Layered Score Distillation for Disentangled Object Relighting
Although generative editing methods now enable some forms of image editing, relighting is still beyond today's capabilities; existing methods struggle to keep other aspects of the image -- colors, shapes, and textures -- consistent after the edit.
Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing
We hypothesize that this power to ignore out-of-context information (which we name $\textit{patch selectivity}$), while integrating in-context information in a non-local manner in early layers, allows ViTs to more easily handle occlusion.
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness issues with respect to specific scene variations.
Temporal Relevance Analysis for Video Action Models
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature.
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
We also show how the $\ell_2$ norm and other metrics do not correlate with human perceptibility in a linear fashion, thus making these norms suboptimal at measuring adversarial attack perceptibility.
Simulated Adversarial Testing of Face Recognition Models
In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes
Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets.
NBDT: Neural-Backed Decision Tree
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.
SegNBDT: Visual Decision Rules for Segmentation
To address this, prior work combines neural networks with decision trees.
Protecting Against Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks
In this work, we develop efficient disruptions of black-box image translation deepfake generation systems.
NBDT: Neural-Backed Decision Trees
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.
Explainable Deep Classification Models for Domain Generalization
Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision.
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
This type of manipulated images and video have been coined Deepfakes.
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time.
Multi-way Encoding for Robustness
However, it has been shown that deep models are vulnerable to adversarial examples.
Multi-way Encoding for Robustness to Adversarial Attacks
Deep models are state-of-the-art for many computer vision tasks including image classification and object detection.
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions.
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout.
Hashing with Mutual Information
Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as image and video retrieval.
Moments in Time Dataset: one million videos for event understanding
We present the Moments in Time Dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds.
Excitation Backprop for RNNs
Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions.
Hashing as Tie-Aware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval.
MIHash: Online Hashing with Mutual Information
Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data.
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web
In contrast, collecting action images from the Web is much easier and training on images requires much less computation.
Ranked #14 on
Action Recognition
on ActivityNet
(using extra training data)
Online Supervised Hashing for Ever-Growing Datasets
To address these issues, we propose an online hashing method that is amenable to changes and expansions of the datasets.
