18 citations · 21 across the 14 of their papers we have counts for
17 papers
Are Online Skill and Memory Modules Always Worth Their Tokens? A Budget-Constrained Study of Web Agents
Sina Hajimiri, Masih Aminbeidokhti, Jose Dolz +4
Online web agents often augment a base actor with memory, workflow, or skill modules. These modules can improve performance, but they also consume test-time tokens, a cost rarely r…
How (Mis)calibrated is your Federated CLIP and what to do about it?
Mainak Singha, Masih Aminbeidokhti, Paolo Casari +3
Vision-language models (VLMs) such as CLIP are increasingly adapted across decentralized data silos, yet the reliability of their predictions under federated learning (FL) remains…
LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups
Masih Aminbeidokhti, Subhankar Roy, Eric Granger +2
Real-world datasets typically exhibit long-tailed (LT) distributions, where a few head classes dominate and many tail classes are severely underrepresented. While recent work shows…
High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization
Masih Aminbeidokhti, Heitor Rapela Medeiros, Srikanth Muralidharan +2
Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial compu…
Robust and Efficient Finetuning of Vision Foundation Models via Implicit Ensembling
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger +1
Robust finetuning of foundation models seeks to improve out-of-distribution (OOD) generalization during in-distribution (ID) adaptation. We revisit Mixout, a stochastic regularizer…
Infrared Object Detection with Ultra Small ConvNets: Is ImageNet Pretraining Still Useful?
Srikanth Muralidharan, Heitor R. Medeiros, Masih Aminbeidokhti +2
Many real-world applications require recognition models that are robust to different operational conditions and modalities, but at the same time run on small embedded devices, with…
CTA: Cross-Task Alignment for Better Test Time Training
Samuel Barbeau, Pedram Fekri, David Osowiechi +4
Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with…
WiSE-OD: Benchmarking Robustness in Infrared Object Detection
Heitor R. Medeiros, Atif Belal, Masih Aminbeidokhti +2
Object detection (OD) in infrared (IR) imagery is critical for low-light and nighttime applications. However, the scarcity of large-scale IR datasets forces models to rely on weigh…
Distilling Specialized Orders for Visual Generation
Rishav Pramanik, Amin Sghaier, Masih Aminbeidokhti +6
Autoregressive (AR) image generators are becoming increasingly popular due to their ability to produce high-quality images and their scalability. Typical AR models are locked onto…
Source-Free Domain Adaptation for YOLO Object Detection
Simon Varailhon, Masih Aminbeidokhti, Marco Pedersoli +1
Source-free domain adaptation (SFDA) is a challenging problem in object detection, where a pre-trained source model is adapted to a new target domain without using any source domai…
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average
Louis Fournier, Adel Nabli, Masih Aminbeidokhti +3
The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at an increased cost at inference. Weight a…
Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge
Heitor Rapela Medeiros, Masih Aminbeidokhti, Fidel Guerrero Pena +3
A common practice in deep learning involves training large neural networks on massive datasets to achieve high accuracy across various domains and tasks. While this approach works…
Domain Generalization by Rejecting Extreme Augmentations
Masih Aminbeidokhti, Fidel A. Guerrero Peña, Heitor Rapela Medeiros +3
Data augmentation is one of the most effective techniques for regularizing deep learning models and improving their recognition performance in a variety of tasks and domains. Howev…
HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information
Heitor Rapela Medeiros, Fidel A. Guerrero Pena, Masih Aminbeidokhti +3
A powerful way to adapt a visual recognition model to a new domain is through image translation. However, common image translation approaches only focus on generating data from the…
Re-basin via implicit Sinkhorn differentiation
Fidel A. Guerrero Peña, Heitor Rapela Medeiros, Thomas Dubail +3
The recent emergence of new algorithms for permuting models into functionally equivalent regions of the solution space has shed some light on the complexity of error surfaces, and…
Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras
Thomas Dubail, Fidel Alejandro Guerrero Peña, Heitor Rapela Medeiros +3
In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with re…
Emotion Recognition with Spatial Attention and Temporal Softmax Pooling
Masih Aminbeidokhti, Marco Pedersoli, Patrick Cardinal +1
Video-based emotion recognition is a challenging task because it requires to distinguish the small deformations of the human face that represent emotions, while being invariant to…