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Found 11 papers, 1 papers with code
SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos
We propose a novel self-supervised embedding to learn how actions sound from narrated in-the-wild egocentric videos.
Detours for Navigating Instructional Videos
We introduce the video detours problem for navigating instructional videos.
Learning Object State Changes in Videos: An Open-World Perspective
Object State Changes (OSCs) are pivotal for video understanding.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
HierVL: Learning Hierarchical Video-Language Embeddings
Video-language embeddings are a promising avenue for injecting semantics into visual representations, but existing methods capture only short-term associations between seconds-long video clips and their accompanying text.
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What You Say Is What You Show: Visual Narration Detection in Instructional Videos
Narrated ''how-to'' videos have emerged as a promising data source for a wide range of learning problems, from learning visual representations to training robot policies.
RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging
Our extensive experiments on popular medical imaging classification tasks (cardiopulmonary disease and lesion classification) using real-world datasets, show the performance benefit of RoS-KD, its ability to distill knowledge from many popular large networks (ResNet-50, DenseNet-121, MobileNet-V2) in a comparatively small network, and its robustness to adversarial attacks (PGD, FSGM).
3D-NVS: A 3D Supervision Approach for Next View Selection
We present a classification based approach for the next best view selection and show how we can plausibly obtain a supervisory signal for this task.
Lower Bounds for Policy Iteration on Multi-action MDPs
An important theoretical question is how many iterations a specified PI variant will take to terminate as a function of the number of states $n$ and the number of actions $k$ in the input MDP.
Bandit algorithms: Letting go of logarithmic regret for statistical robustness
We study regret minimization in a stochastic multi-armed bandit setting and establish a fundamental trade-off between the regret suffered under an algorithm, and its statistical robustness.
Analysis of Lower Bounds for Simple Policy Iteration
We generalize the previous result and prove a novel exponential lower bound on the number of iterations taken by policy iteration for $N-$state, $k-$action MDPs.
