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Found 15 papers, 8 papers with code
Amplifying Key Cues for Human-Object-Interaction Detection
In this paper we introduce two methods to amplify key cues in the image, and also a method to combine these and other cues when considering the interaction between a human and an object.
Progressive trajectory matching for medical dataset distillation
To solve these barriers, we propose to design a novel progressive trajectory matching strategy to improve the training stability for medical image dataset distillation.
On the Model-Agnostic Multi-Source-Free Unsupervised Domain Adaptation
Specifically, we first conduct source model selection based on the proposed selection principles.
EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
Then, we incorporate the human prior into the prompts, which is vital for alleviating the domain gap between natural and medical images and enhancing the applicability and usefulness of SAM in medical scenarios.
Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory
This observation motivates us to design an HOI detector that can be trained even with long-tailed labeled data and can leverage existing knowledge from pre-trained models.
Incorporating Pre-training Data Matters in Unsupervised Domain Adaptation
To answer the first question, we empirically observed an interesting Spontaneous Pulling (SP) Effect in fine-tuning where the discrepancies between any two of the three domains (ImageNet, Source, Target) decrease but at the cost of the impaired semantic structure of the pre-train domain.
Confidence-aware Pseudo-label Learning for Weakly Supervised Visual Grounding
Visual grounding aims at localizing the target object in image which is most related to the given free-form natural language query.
Masked Retraining Teacher-Student Framework for Domain Adaptive Object Detection
This helps the student model capture target domain characteristics and become a more data-efficient learner to gain knowledge from the limited number of pseudo boxes.
Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation
Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i)the source knowledge calibration module that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii)the target semantics calibration module that calibrates the unreliable semantics.
Delving into the Continuous Domain Adaptation
Existing domain adaptation methods assume that domain discrepancies are caused by a few discrete attributes and variations, e. g., art, real, painting, quickdraw, etc.
Seeing your sleep stage: cross-modal distillation from EEG to infrared video
$S^3VE$ is a large-scale dataset including synchronized infrared video and EEG signal for sleep stage classification, including 105 subjects and 154, 573 video clips that is more than 1100 hours long.
Weakly Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal Learning
Moreover, they train their model to distinguish positive visual-language pairs from negative ones randomly collected from other videos, ignoring the highly confusing video segments within the same video.
Ranked #7 on
Temporal Sentence Grounding
on Charades-STA
Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval
In this paper, we study the task of visual-text retrieval in the highly practical setting in which labelled visual data with paired text descriptions are available in one domain (the "source"), but only unlabelled visual data (without text descriptions) are available in the domain of interest (the "target").
Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program.
Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation
In this paper, we propose the Re-weighted Adversarial Adaptation Network (RAAN) to reduce the feature distribution divergence and adapt the classifier when domain discrepancies are disparate.
Open-Ended Question Answering
Unsupervised Domain Adaptation
