Search Results for author:
Found 20 papers, 5 papers with code
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding in Open World
In particular, we first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness.
Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding
In precision agriculture, the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield.
Cross-view Action Recognition Understanding From Exocentric to Egocentric Perspective
Then, we propose a new cross-view self-attention loss learned on unpaired cross-view data to enforce the self-attention mechanism learning to transfer knowledge across views.
CROVIA: Seeing Drone Scenes from Car Perspective via Cross-View Adaptation
First, a novel geometry-based constraint to cross-view adaptation is introduced based on the geometry correlation between views.
CoMaL: Conditional Maximum Likelihood Approach to Self-supervised Domain Adaptation in Long-tail Semantic Segmentation
They are insufficient to model both global and local structures of a given image, especially in small regions of tail classes.
FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding
Although Domain Adaptation in Semantic Scene Segmentation has shown impressive improvement in recent years, the fairness concerns in the domain adaptation have yet to be well defined and addressed.
Ranked #5 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars
Also, the previous data training of segmentation models may be inaccessible due to privacy problems.
Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition
We therefore introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) to synthesize faces of a subject given his/her extracted face recognition features.
Self-supervised Domain Adaptation in Crowd Counting
Self-training crowd counting has not been attentively explored though it is one of the important challenges in computer vision.
OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation
Unsupervised domain adaptation is one of the challenging problems in computer vision.
DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
Various 3D-CNN based methods have been presented to tackle both the spatial and temporal dimensions in the task of video action recognition with competitive results.
Ranked #1 on
Action Recognition
on Jester (Gesture Recognition)
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
Semantic segmentation aims to predict pixel-level labels.
The Right to Talk: An Audio-Visual Transformer Approach
Therefore, this work introduces a new Audio-Visual Transformer approach to the problem of localization and highlighting the main speaker in both audio and visual channels of a multi-speaker conversation video in the wild.
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications.
LIAAD: Lightweight Attentive Angular Distillation for Large-scale Age-Invariant Face Recognition
This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale Lightweight AiFR that overcomes these limitations.
Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition
Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging.
Image Alignment in Unseen Domains via Domain Deep Generalization
This paper presents a novel deep learning based approach to tackle the problem of across unseen modalities.
Domain Generalization via Universal Non-volume Preserving Models
However, it has been largely overlooked in the problem of recognition in new unseen domains.
Fast Flow Reconstruction via Robust Invertible nxn Convolution
The experiments on CIFAR-10, ImageNet and Celeb-HQ datasets, have shown that our invertible $n \times n$ convolution helps to improve the performance of generative models significantly.
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models
However, it has been largely overlooked in the problem of recognition in new unseen domains.
