10 accepted papers
We introduce FLAG-4D, a novel framework for generating novel views of dynamic scenes by reconstructing how 3D Gaussian primitives evolve through space and time. Existing methods typically rely on a single Multilayer Perceptron(MLP) to model temporal deformations, and they often struggle to capture c
Generative diffusion models often exhibit societal biases in sensitive personal attributes such as age, gender, and race. In this work, we describe GENIE – a method to reduce such biases in a variety of classifier-free diffusion models used for image editing. Our method implicitly incorporates debia…
Securing micro-expressions against leakage is crucial for privacy, as these subtle facial movements convey genuine emotions and are inherently personal. This study aims to protect micro-expression data from potential adversarial attacks, ensuring the preservation of individuals’ privacy and preventi…
Detecting microexpressions presents formidable challenges, primarily due to their fleeting nature and the limited diversity in existing datasets. Our studies find that these datasets exhibit a pronounced bias towards specific ethnicities and suffer from significant imbalances in terms of both class…
Critical BERT-based text classification tasks, such as legal text classification, require huge amounts of accurately labeled data. Legal text classification faces two trivial problems: labeling legal data is a sensitive process and can only be carried out by skilled professionals, and legal text is…
"The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense efforts considering its critical nature. Recent work \textit{Nasty Teacher} proposed to develop teachers which can not be dis…
In this work, we show the generative capability of an image classifier network by synthesizing high-resolution, photo-realistic, and diverse images at scale. The overall methodology, called Synthesize-It-Classifier (STIC), does not require an explicit generator network to estimate the density of the…
In this work, we present a novel meta-learning algorithm that regresses model parameters for novel tasks for which no ground truth is available (zero-shot tasks). In order to adapt to novel zero-shot tasks, our meta-learner learns from the model parameters of known tasks (with ground truth) and the…
Paucity of large curated hand labeled training data forms a major bottleneck in the deployment of machine learning models in computer vision and other fields. Recent work (Data Programming) has shown how distant supervision signals in the form of labeling functions can be used to obtain labels for g…
Long-short term memory networks(LSTM) models have shown considerable performance on variety of problems dealing with sequential data. In this paper, we propose a variant of Long-Term Recurrent Convolutional Network(LRCN) to detect road intersection. We call this network as IntersectNet. We pose road…