55 citations · 131 across the 18 of their papers we have counts for
18 papers
RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields
Chuhan Chen, Tianshu Huang, Akarsh Prabhakara +5
Radars are an ideal complement to cameras: both are inexpensive, solid-state sensors, with cameras offering fine angular resolution, while radars provide metric depth and robustnes…
Visual Self-Refinement for Autoregressive Models
Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi +5
Autoregressive models excel in sequential modeling and have proven to be effective for vision-language data. However, the spatial nature of visual signals conflicts with the sequen…
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA
Divyansh Jhunjhunwala, Arian Raje, Madan Ravi Ganesh +6
LoRA has emerged as one of the most promising fine-tuning techniques, especially for federated learning (FL), since it significantly reduces communication and computation costs at…
Text-driven Prompt Generation for Vision-Language Models in Federated Learning
Chen Qiu, Xingyu Li, Chaithanya Kumar Mummadi +4
Prompt learning for vision-language models, e.g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learnin…
Zero-Shot Visual Classification with Guided Cropping
Piyapat Saranrittichai, Mauricio Munoz, Volker Fischer +1
Pretrained vision-language models, such as CLIP, show promising zero-shot performance across a wide variety of datasets. For closed-set classification tasks, however, there is an i…
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models
Jan Hendrik Metzen, Piyapat Saranrittichai, Chaithanya Kumar Mummadi
Classifiers built upon vision-language models such as CLIP have shown remarkable zero-shot performance across a broad range of image classification tasks. Prior work has studied di…
PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
Bang An, Sicheng Zhu, Michael-Andrei Panaitescu-Liess +2
Vision-language models like CLIP are widely used in zero-shot image classification due to their ability to understand various visual concepts and natural language descriptions. How…
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches
Aniruddha Saha, Shuhua Yu, Arash Norouzzadeh +2
Certifiably robust defenses against adversarial patches for image classifiers ensure correct prediction against any changes to a constrained neighborhood of pixels. PatchCleanser a…
Multi-Attribute Open Set Recognition
Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Claudia Blaiotta +2
Open Set Recognition (OSR) extends image classification to an open-world setting, by simultaneously classifying known classes and identifying unknown ones. While conventional OSR a…
Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain
Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Claudia Blaiotta +2
Shortcut learning occurs when a deep neural network overly relies on spurious correlations in the training dataset in order to solve downstream tasks. Prior works have shown how th…
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness
Giulio Lovisotto, Nicole Finnie, Mauricio Munoz +2
Neural architectures based on attention such as vision transformers are revolutionizing image recognition. Their main benefit is that attention allows reasoning about all parts of…
DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities
Elias Eulig, Piyapat Saranrittichai, Chaithanya Kumar Mummadi +4
Common deep neural networks (DNNs) for image classification have been shown to rely on shortcut opportunities (SO) in the form of predictive and easy-to-represent visual factors. T…
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach +3
Deep neural networks often exhibit poor performance on data that is unlikely under the train-time data distribution, for instance data affected by corruptions. Previous works demon…
Does enhanced shape bias improve neural network robustness to common corruptions?
Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher +3
Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicate…
SELF: Learning to Filter Noisy Labels with Self-Ensembling
Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo +3
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and eff…
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision
Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi +4
Deep neural network (DNN) based salient object detection in images based on high-quality labels is expensive. Alternative unsupervised approaches rely on careful selection of multi…
Group Pruning using a Bounded-Lp norm for Group Gating and Regularization
Chaithanya Kumar Mummadi, Tim Genewein, Dan Zhang +2
Deep neural networks achieve state-of-the-art results on several tasks while increasing in complexity. It has been shown that neural networks can be pruned during training by impos…
Defending Against Universal Perturbations With Shared Adversarial Training
Chaithanya Kumar Mummadi, Thomas Brox, Jan Hendrik Metzen
Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial trainin…