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Found 5 papers, 4 papers with code
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
Our method surpasses the performance of existing state-of-the-art online learning methods in terms of both prediction accuracy and computational efficiency.
Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor
To bring this idea into WSSS, we simultaneously train two models: a classifier generating CAMs that decompose an image into segments and a reconstructor that measures the inferability between the segments.
Ranked #13 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Exploring Pixel-level Self-supervision for Weakly Supervised Semantic Segmentation
In our framework, with the help of the proposed Regional Contrastive Module (RCM) and Multi-scale Attentive Module (MAM), MainNet is trained by self-supervision from the SupportNet.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation
To enable KD across the unpaired modalities, we first propose a bidirectional modality reconstruction (BMR) module to bridge both modalities and simultaneously exploit them to distill knowledge via the crafted pairs, causing no extra computation in the inference.
Ranked #7 on
Event-based Object Segmentation
on MVSEC-SEG
Unlocking the Potential of Ordinary Classifier: Class-Specific Adversarial Erasing Framework for Weakly Supervised Semantic Segmentation
In this paper, we review the potential of the pre-trained classifier which is trained on the raw images.
Ranked #30 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
