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Found 19 papers, 11 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.
SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions.
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.
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).
Benchmarking Low-Shot Robustness to Natural Distribution Shifts
Robustness to natural distribution shifts has seen remarkable progress thanks to recent pre-training strategies combined with better fine-tuning methods.
PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalization
Synthetic data offers the promise of cheap and bountiful training data for settings where labeled real-world data is scarce.
RobustNav: Towards Benchmarking Robustness in Embodied Navigation
As an attempt towards assessing the robustness of embodied navigation agents, we propose RobustNav, a framework to quantify the performance of embodied navigation agents when exposed to a wide variety of visual - affecting RGB inputs - and dynamics - affecting transition dynamics - corruptions.
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
For domain generalization, the goal is to learn from a set of source domains to produce a single model that will best generalize to an unseen target domain.
Ranked #24 on
Domain Generalization
on DomainNet
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses
Convolutional Neural Networks have been shown to be vulnerable to adversarial examples, which are known to locate in subspaces close to where normal data lies but are not naturally occurring and of low probability.
DS-VIC: Unsupervised Discovery of Decision States for Transfer in RL
We learn to identify decision states, namely the parsimonious set of states where decisions meaningfully affect the future states an agent can reach in an environment.
Improving Generative Visual Dialog by Answering Diverse Questions
Prior work on training generative Visual Dialog models with reinforcement learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown that this 'self-talk' approach can lead to improved performance at the downstream dialog-conditioned image-guessing task.
IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability.
EvalAI: Towards Better Evaluation Systems for AI Agents
We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale.
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).
Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance
Our approach, which we call Neuron Importance-AwareWeight Transfer (NIWT), learns to map domain knowledge about novel "unseen" classes onto this dictionary of learned concepts and then optimizes for network parameters that can effectively combine these concepts - essentially learning classifiers by discovering and composing learned semantic concepts in deep networks.
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.
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.
Counting Everyday Objects in Everyday Scenes
In this work, we build dedicated models for counting designed to tackle the large variance in counts, appearances, and scales of objects found in natural scenes.
Ranked #1 on
Object Counting
on COCO count-test
