Search Results for author:
Found 10 papers, 7 papers with code
MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection
A source sample is considered suitable if it differs from the target sample only in domain, without differences in unimportant characteristics such as orientation and color, which can hinder the model's focus on aligning the domain difference.
Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following
However, as it is still in the early days of importing modular ideas to EIF, a search for modules effective in the EIF task is still far from a conclusion.
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
Long-tailed datasets, where head classes comprise much more training samples than tail classes, cause recognition models to get biased towards the head classes.
Ranked #10 on
Long-tail Learning
on Places-LT
Class-Difficulty Based Methods for Long-Tailed Visual Recognition
Further, we use the difficulty measures of each class to design a novel weighted loss technique called `class-wise difficulty based weighted (CDB-W) loss' and a novel data sampling technique called `class-wise difficulty based sampling (CDB-S)'.
Human-error-potential Estimation based on Wearable Biometric Sensors
This study tackles on a new problem of estimating human-error potential on a shop floor on the basis of wearable sensors.
Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attention
We compared the proposed sensor-fusion method with strong baselines on the MMAC Captions dataset and found that using sensor data as supplementary information to the egocentric-video data was beneficial, and that our proposed method outperformed the strong baselines, demonstrating the effectiveness of the proposed method.
QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information
We propose a simple, intuitive yet powerful method for human-object interaction (HOI) detection.
Human-Object Interaction Concept Discovery
Human-Object Interaction Detection
Influence Estimation for Generative Adversarial Networks
To this end, (1) we propose an influence estimation method that uses the Jacobian of the gradient of the generator's loss with respect to the discriminator's parameters (and vice versa) to trace how the absence of an instance in the discriminator's training affects the generator's parameters, and (2) we propose a novel evaluation scheme, in which we assess harmfulness of each training instance on the basis of how GAN evaluation metric (e. g., inception score) is expect to change due to the removal of the instance.
Class-Wise Difficulty-Balanced Loss for Solving Class-Imbalance
We claim that the 'difficulty' of a class as perceived by the model is more important to determine the weighting.
Ranked #1 on
Long-tail Learning
on EGTEA
Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors
ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes.
