Search Results for author:
Found 16 papers, 6 papers with code
Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors
In this paper, we present Unbiased Teacher v2, which shows the generalization of SS-OD method to anchor-free detectors and also introduces Listen2Student mechanism for the unsupervised regression loss.
Cross-Domain Adaptive Teacher for Object Detection
To mitigate this problem, we propose a teacher-student framework named Adaptive Teacher (AT) which leverages domain adversarial learning and weak-strong data augmentation to address the domain gap.
Adaptive Unbiased Teacher for Cross-Domain Object Detection
This enables the student model to capture domain-invariant features.
CrossMatch: Improving Semi-Supervised Object Detection via Multi-Scale Consistency
Inspired by the fact that teacher/student pseudo-labeling approaches result in a weak and sparse gradient signal due to the difficulty of confidence-thresholding, CrossMatch leverages \textit{multi-scale feature extraction} in object detection.
Enhancing Multi-Robot Perception via Learned Data Association
In this paper, we address the multi-robot collaborative perception problem, specifically in the context of multi-view infilling for distributed semantic segmentation.
Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking
In this paper, we address bandwidth-limited and obstruction-prone collaborative perception, specifically in the context of multi-agent semantic segmentation.
Unbiased Teacher for Semi-Supervised Object Detection
To address this, we introduce Unbiased Teacher, a simple yet effective approach that jointly trains a student and a gradually progressing teacher in a mutually-beneficial manner.
Posterior Re-calibration for Imbalanced Datasets
Neural Networks can perform poorly when the training label distribution is heavily imbalanced, as well as when the testing data differs from the training distribution.
Ranked #12 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
When2com: Multi-Agent Perception via Communication Graph Grouping
While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as coverage and robustness.
Who2com: Collaborative Perception via Learnable Handshake Communication
In this paper, we propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve accuracy on a perception task.
UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation
Specifically, we analyze a number of uncertainty measures, each of which captures a different aspect of uncertainty, and we propose a novel way to fuse degraded inputs by scaling modality-specific output softmax probabilities.
A Closer Look at Few-shot Classification
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines
Continual learning has received a great deal of attention recently with several approaches being proposed.
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains.
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras.
Ranked #19 on
Unsupervised Domain Adaptation
on Duke to Market
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated.
