431 citations · 844 across the 46 of their papers we have counts for
21 papers · 1 filter
CLR: Channel-wise Lightweight Reprogramming for Continual Learning
Yunhao Ge, Yuecheng Li, Shuo Ni +3
Continual learning aims to emulate the human ability to continually accumulate knowledge over sequential tasks. The main challenge is to maintain performance on previously learned…
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models
Yunhao Ge, Jie Ren, Jiaping Zhao +4
We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains…
EM-Paste: EM-guided Cut-Paste with DALL-E Augmentation for Image-level Weakly Supervised Instance Segmentation
Yunhao Ge, Jiashu Xu, Brian Nlong Zhao +2
We propose EM-PASTE: an Expectation Maximization(EM) guided Cut-Paste compositional dataset augmentation approach for weakly-supervised instance segmentation using only image-level…
Improving Zero-shot Generalization and Robustness of Multi-modal Models
Yunhao Ge, Jie Ren, Andrew Gallagher +6
Multi-modal image-text models such as CLIP and LiT have demonstrated impressive performance on image classification benchmarks and their zero-shot generalization ability is particu…
Neural-Sim: Learning to Generate Training Data with NeRF
Yunhao Ge, Harkirat Behl, Jiashu Xu +6
Training computer vision models usually requires collecting and labeling vast amounts of imagery under a diverse set of scene configurations and properties. This process is incredi…
Contributions of Shape, Texture, and Color in Visual Recognition
Yunhao Ge, Yao Xiao, Zhi Xu +2
We investigate the contributions of three important features of the human visual system (HVS)~ -- ~shape, texture, and color ~ -- ~to object classification. We build a humanoid vis…
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection
Yunhao Ge, Jiashu Xu, Brian Nlong Zhao +3
We propose a new paradigm to automatically generate training data with accurate labels at scale using the text-toimage synthesis frameworks (e.g., DALL-E, Stable Diffusion, etc.).…
What can we learn from misclassified ImageNet images?
Shixian Wen, Amanda Sofie Rios, Kiran Lekkala +1
Understanding the patterns of misclassified ImageNet images is particularly important, as it could guide us to design deep neural networks (DNN) that generalize better. However, th…
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization
Yunhao Ge, Zhi Xu, Yao Xiao +3
We focus on controllable disentangled representation learning (C-Dis-RL), where users can control the partition of the disentangled latent space to factorize dataset attributes (co…
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
Yunhao Ge, Yao Xiao, Zhi Xu +5
Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability. While recent d…
Beneficial Perturbation Network for designing general adaptive artificial intelligence systems
Shixian Wen, Amanda Rios, Yunhao Ge +1
The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural network…
Zero-shot Synthesis with Group-Supervised Learning
Yunhao Ge, Sami Abu-El-Haija, Gan Xin +1
Visual cognition of primates is superior to that of artificial neural networks in its ability to 'envision' a visual object, even a newly-introduced one, in different attributes in…
Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition
Yunhao Ge, Jiaping Zhao, Laurent Itti
Object pose increases intraclass object variance which makes object recognition from 2D images harder. To render a classifier robust to pose variations, most deep neural networks t…
Learning to Recognize Objects by Retaining other Factors of Variation
Jiaping Zhao, Chin-kai Chang, Laurent Itti
Natural images are generated under many factors, including shape, pose, illumination etc. Most existing ConvNets formulate object recognition from natural images as a single task c…
Improved Deep Learning of Object Category using Pose Information
Jiaping Zhao, Laurent Itti
Despite significant recent progress, the best available computer vision algorithms still lag far behind human capabilities, even for recognizing individual discrete objects under v…
shapeDTW: shape Dynamic Time Warping
Jiaping Zhao, Laurent Itti
Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data ali…
What can we learn about CNNs from a large scale controlled object dataset?
Ali Borji, Saeed Izadi, Laurent Itti
Tolerance to image variations (e.g. translation, scale, pose, illumination) is an important desired property of any object recognition system, be it human or machine. Moving toward…
Computational models: Bottom-up and top-down aspects
Laurent Itti, Ali Borji
Computational models of visual attention have become popular over the past decade, we believe primarily for two reasons: First, models make testable predictions that can be explore…
Computational models of attention
Laurent Itti, Ali Borji
This chapter reviews recent computational models of visual attention. We begin with models for the bottom-up or stimulus-driven guidance of attention to salient visual items, which…
CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research
Ali Borji, Laurent Itti
Saliency modeling has been an active research area in computer vision for about two decades. Existing state of the art models perform very well in predicting where people look in n…
6th International Symposium on Attention in Cognitive Systems 2013
Lucas Paletta, Laurent Itti, Björn Schuller +1
This volume contains the papers accepted at the 6th International Symposium on Attention in Cognitive Systems (ISACS 2013), held in Beijing, August 5, 2013. The aim of this symposi…