Search Results for author:
Found 12 papers, 1 papers with code
From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments
We argue that SAM drastically over-segment images with high corruption levels, resulting in degraded performance when only a single segmentation mask is considered, while the combination of the masks overlapping the object of interest generates an accurate prediction.
RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning
Lastly, while conventional robustness evaluations view corruptions as out-of-distribution, we use our causal framework to show that even training on in-distribution image corruptions does not guarantee increased model robustness.
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches.
Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models
To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable.
Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity
The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e. g., a vision model for classifying street signs should work in rural, city, and highway settings under varying lighting/weather conditions).
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
We find this area of research has received disproportionately less attention relative to adversarial machine learning, yet a significant robustness gap exists that manifests in performance degradation similar in magnitude to adversarial conditions.
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation
It is based on the idea that observed frames can be synthesized from neighboring frames if accurate depth of the scene is known - or in this case, estimated.
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?
To this end, several contributions are made here: A) we develop a new metric called mean Attack Success over Transformations (mAST) to evaluate patch attack robustness and invariance; and B), we systematically assess robustness of patch attacks to 3D position and orientation for various conditions; in particular, we conduct a sensitivity analysis which provides important qualitative insights into attack effectiveness as a function of the 3D pose of a patch relative to the camera (rotation, translation) and sets forth some properties for patch attack 3D invariance; and C), we draw novel qualitative conclusions including: 1) we demonstrate that for some 3D transformations, namely rotation and loom, increasing the training distribution support yields an increase in patch success over the full range at test time.
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis
We present a technique that utilizes properties of random projections to characterize the behavior of clean and adversarial examples across a diverse set of subspaces.
Addressing Visual Search in Open and Closed Set Settings
We interpret both detection problems through a probabilistic, Bayesian lens, whereby the objectness maps produced by our method serve as priors in a maximum-a-posteriori approach to the detection step.
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth.
Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via the design of novel semi-transparent patches.
