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Found 24 papers, 5 papers with code
Concept-based Analysis of Neural Networks via Vision-Language Models
The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures.
BRI3L: A Brightness Illusion Image Dataset for Identification and Localization of Regions of Illusory Perception
To this end, we generate a large-scale dataset of 22, 366 images (BRI3L: BRightness Illusion Image dataset for Identification and Localization of illusory perception) of the five types of brightness illusions and benchmark the dataset using data-driven neural network based approaches.
Direct Amortized Likelihood Ratio Estimation
Our estimator is simple to train and estimates the likelihood ratio using a single forward pass of the neural estimator.
TIJO: Trigger Inversion with Joint Optimization for Defending Multimodal Backdoored Models
We present a Multimodal Backdoor Defense technique TIJO (Trigger Inversion using Joint Optimization).
Automatic Measures for Evaluating Generative Design Methods for Architects
Of interest for architects is to use these methods to generate design proposals from conceptual sketches, usually hand-drawn sketches that are quickly developed and can embody a design intent.
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos
We learn a class prototype for each class and a loss function is employed to minimize the distances between a class prototype and the samples from the class.
Cap2Aug: Caption guided Image to Image data Augmentation
We generate captions from the limited training images and using these captions edit the training images using an image-to-image stable diffusion model to generate semantically meaningful augmentations.
Design of Unmanned Air Vehicles Using Transformer Surrogate Models
In this paper, we develop an AI Designer that synthesizes novel UAV designs.
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution.
Multiple Testing Framework for Out-of-Distribution Detection
While a number of tests for OOD detection have been proposed in prior work, a formal framework for studying this problem is lacking.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Detecting out-of-context objects using contextual cues
GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects.
Ranked #1 on
Anomaly Detection
on COCO-OOC
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection.
Physical System Design Using Hamiltonian Monte Carlo over Learned Manifolds
We use deep generative models to learn a manifold of the valid design space, followed by Hamiltonian Monte Carlo (HMC) with simulated annealing to explore and optimize design over the learned manifold, producing a diverse set of optimal designs.
Detecting OODs as datapoints with High Uncertainty
We demonstrate the difference in the detection ability of these techniques and propose an ensemble approach for detection of OODs as datapoints with high uncertainty (epistemic or aleatoric).
MISA: Online Defense of Trojaned Models using Misattributions
Recent studies have shown that neural networks are vulnerable to Trojan attacks, where a network is trained to respond to specially crafted trigger patterns in the inputs in specific and potentially malicious ways.
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution (OOD) inputs.
Constraining reionization with the first measurement of the cross-correlation between the CMB optical-depth fluctuations and the Compton y-map
Since the power spectrum of the electron density fluctuations is constrained by the $\delta\tau$ auto spectrum, the temperature constraints should be only weakly model-dependent on the details of the electron distributions and should be statistically representative of the temperature in ionized bubbles during reionization.
PICO
Cosmology and Nongalactic Astrophysics
Astrophysics of Galaxies
Detecting Trojaned DNNs Using Counterfactual Attributions
We target the problem of detecting Trojans or backdoors in DNNs.
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment
We propose a novel end-to-end model that uses caption-to-image retrieval as a `downstream' task to guide the process of phrase localization.
The Simons Observatory: Science goals and forecasts
With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio.
Cosmology and Nongalactic Astrophysics
Understanding Visual Ads by Aligning Symbols and Objects using Co-Attention
We show that our model outperforms other baselines on the benchmark Ad dataset and also show qualitative results to highlight the advantages of using multihop co-attention.
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation
This paper addresses the problem of weakly supervised semantic image segmentation.
Monocular Depth Estimation Using Neural Regression Forest
This paper presents a novel deep architecture, called neural regression forest (NRF), for depth estimation from a single image.
Scene Labeling Using Beam Search Under Mutex Constraints
This paper addresses the problem of assigning object class labels to image pixels.
