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Found 21 papers, 9 papers with code
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline
While the vast majority of prior work has studied this as a frame-level Image-DAS problem, a few Video-DAS works have sought to additionally leverage the temporal signal present in adjacent frames.
AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Images
Synthetic data (SIM) drawn from simulators have emerged as a popular alternative for training models where acquiring annotated real-world images is difficult.
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
Battle of the Backbones (BoB) makes this choice easier by benchmarking a diverse suite of pretrained models, including vision-language models, those trained via self-supervised learning, and the Stable Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more.
FACTS: First Amplify Correlations and Then Slice to Discover Bias
Computer vision datasets frequently contain spurious correlations between task-relevant labels and (easy to learn) latent task-irrelevant attributes (e. g. context).
ICON$^2$: Reliably Benchmarking Predictive Inequity in Object Detection
As computer vision systems are being increasingly deployed at scale in high-stakes applications like autonomous driving, concerns about social bias in these systems are rising.
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
We propose an automated algorithm to stress-test a trained visual model by generating language-guided counterfactual test images (LANCE).
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domain.
Mitigating Bias in Visual Transformers via Targeted Alignment
We visualize the feature space of the transformer self-attention modules and discover that a significant portion of the bias is encoded in the query matrix.
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
Inspired by the design of recent SSL approaches based on learning from partial image inputs generated via masking or cropping -- either by learning to predict the missing pixels, or learning representational invariances to such augmentations -- we propose PACMAC, a simple two-stage adaptation algorithm for self-supervised ViTs.
Can domain adaptation make object recognition work for everyone?
Despite the rapid progress in deep visual recognition, modern computer vision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies.
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models
Deep learning models have been shown to learn spurious correlations from data that sometimes lead to systematic failures for certain subpopulations.
AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation
Most modern approaches for domain adaptive semantic segmentation rely on continued access to source data during adaptation, which may be infeasible due to computational or privacy constraints.
SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
Many existing approaches for unsupervised domain adaptation (UDA) focus on adapting under only data distribution shift and offer limited success under additional cross-domain label distribution shift.
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Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
Generalizing deep neural networks to new target domains is critical to their real-world utility.
Open Set Medical Diagnosis
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i. e. that models will only encounter conditions on which they have been trained.
Prototypical Clustering Networks for Dermatological Disease Diagnosis
We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis.
Do Explanations make VQA Models more Predictable to a Human?
A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA).
Fabrik: An Online Collaborative Neural Network Editor
We present Fabrik, an online neural network editor that provides tools to visualize, edit, and share neural networks from within a browser.
Evaluating Visual Conversational Agents via Cooperative Human-AI Games
This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.
The Promise of Premise: Harnessing Question Premises in Visual Question Answering
In this paper, we make a simple observation that questions about images often contain premises - objects and relationships implied by the question - and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions.
It Takes Two to Tango: Towards Theory of AI's Mind
Surprisingly, we find that having access to the model's internal states - its confidence in its top-k predictions, explicit or implicit attention maps which highlight regions in the image (and words in the question) the model is looking at (and listening to) while answering a question about an image - do not help people better predict its behavior.
