17 citations · 60 across the 34 of their papers we have counts for
9 papers · 1 filter
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding
Morgan Heisler, Amin Banitalebi-Dehkordi, Yong Zhang
Data augmentation is an essential technique in improving the generalization of deep neural networks. The majority of existing image-domain augmentations either rely on geometric an…
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data
Amin Banitalebi-Dehkordi, Pratik Gujjar, Yong Zhang
Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is dr…
Spatial Cross-Attention Improves Self-Supervised Visual Representation Learning
Mehdi Seyfi, Amin Banitalebi-Dehkordi, Yong Zhang
Unsupervised representation learning methods like SwAV are proved to be effective in learning visual semantics of a target dataset. The main idea behind these methods is that diffe…
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples
Amin Banitalebi-Dehkordi, Yong Zhang
Convolutional Neural Networks (CNNs) for visual tasks are believed to learn both the low-level textures and high-level object attributes, throughout the network depth. This paper f…
EBJR: Energy-Based Joint Reasoning for Adaptive Inference
Mohammad Akbari, Amin Banitalebi-Dehkordi, Yong Zhang
State-of-the-art deep learning models have achieved significant performance levels on various benchmarks. However, the excellent performance comes at a cost of inefficient computat…
SimROD: A Simple Adaptation Method for Robust Object Detection
Rindra Ramamonjison, Amin Banitalebi-Dehkordi, Xinyu Kang +2
This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label…
Revisiting Knowledge Distillation for Object Detection
Amin Banitalebi-Dehkordi
The existing solutions for object detection distillation rely on the availability of both a teacher model and ground-truth labels. We propose a new perspective to relax this constr…
A Learning-Based Visual Saliency Prediction Model for Stereoscopic 3D Video (LBVS-3D)
Amin Banitalebi-Dehkordi, Mahsa T. Pourazad, Panos Nasiopoulos
Over the past decade, many computational saliency prediction models have been proposed for 2D images and videos. Considering that the human visual system has evolved in a natural 3…
A Learning-Based Visual Saliency Fusion Model for High Dynamic Range Video (LBVS-HDR)
Amin Banitalebi-Dehkordi, Yuanyuan Dong, Mahsa T. Pourazad +1
Saliency prediction for Standard Dynamic Range (SDR) videos has been well explored in the last decade. However, limited studies are available on High Dynamic Range (HDR) Visual Att…