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Found 16 papers, 9 papers with code
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.
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.
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components.
Frustratingly Simple Domain Generalization via Image Stylization
Convolutional Neural Networks (CNNs) show impressive performance in the standard classification setting where training and testing data are drawn i. i. d.
Ranked #23 on
Domain Generalization
on PACS
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.
Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification
We then show that when combined with these regularizers, the proposed method facilitates the propagation of information from generated prototypes to image data to further improve results.
Learning to Generate Grounded Visual Captions without Localization Supervision
When automatically generating a sentence description for an image or video, it often remains unclear how well the generated caption is grounded, that is whether the model uses the correct image regions to output particular words, or if the model is hallucinating based on priors in the dataset and/or the language model.
The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation
As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making.
Ranked #90 on
Vision and Language Navigation
on VLN Challenge
The Regretful Navigation Agent for Vision-and-Language Navigation
As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making.
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments.
Ranked #90 on
Vision and Language Navigation
on VLN Challenge
Natural Language Visual Grounding
Vision and Language Navigation
+2
AdaFrame: Adaptive Frame Selection for Fast Video Recognition
We present AdaFrame, a framework that adaptively selects relevant frames on a per-input basis for fast video recognition.
Attend and Interact: Higher-Order Object Interactions for Video Understanding
Human actions often involve complex interactions across several inter-related objects in the scene.
Grounded Objects and Interactions for Video Captioning
We address the problem of video captioning by grounding language generation on object interactions in the video.
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
We demonstrate that using both RNNs (using LSTMs) and Temporal-ConvNets on spatiotemporal feature matrices are able to exploit spatiotemporal dynamics to improve the overall performance.
Ranked #50 on
Action Recognition
on UCF101
