Fedseg: Class-heterogeneous federated learning for semantic segmentation
Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a
global model across multiple clients with data privacy-preserving. Although many FL …
global model across multiple clients with data privacy-preserving. Although many FL …
Winner: Weakly-supervised hierarchical decomposition and alignment for spatio-temporal video grounding
Spatio-temporal video grounding aims to localize the aligned visual tube corresponding to a
language query. Existing techniques achieve such alignment by exploiting dense boundary …
language query. Existing techniques achieve such alignment by exploiting dense boundary …
Revisiting the domain shift and sample uncertainty in multi-source active domain transfer
Abstract Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a
new target domain by actively selecting a limited number of target data to annotate. This …
new target domain by actively selecting a limited number of target data to annotate. This …
Are binary annotations sufficient? video moment retrieval via hierarchical uncertainty-based active learning
Recent research on video moment retrieval has mostly focused on enhancing the
performance of accuracy, efficiency, and robustness, all of which largely rely on the …
performance of accuracy, efficiency, and robustness, all of which largely rely on the …
Panoptic scene graph generation with semantics-prototype learning
Panoptic Scene Graph Generation (PSG) parses objects and predicts their relationships
(predicate) to connect human language and visual scenes. However, different language …
(predicate) to connect human language and visual scenes. However, different language …
Gradient-regulated meta-prompt learning for generalizable vision-language models
Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-
training models to adapt to downstream tasks in a parameter-and data-efficient way, by …
training models to adapt to downstream tasks in a parameter-and data-efficient way, by …
Video-audio domain generalization via confounder disentanglement
Existing video-audio understanding models are trained and evaluated in an intra-domain
setting, facing performance degeneration in real-world applications where multiple domains …
setting, facing performance degeneration in real-world applications where multiple domains …
Multi-modal action chain abductive reasoning
M Li, T Wang, J Xu, K Han, S Zhang… - Proceedings of the …, 2023 - aclanthology.org
Abductive Reasoning, has long been considered to be at the core ability of humans, which
enables us to infer the most plausible explanation of incomplete known phenomena in daily …
enables us to infer the most plausible explanation of incomplete known phenomena in daily …
Learning in imperfect environment: Multi-label classification with long-tailed distribution and partial labels
Conventional multi-label classification (MLC) methods assume that all samples are fully
labeled and identically distributed. Unfortunately, this assumption is unrealistic in large …
labeled and identically distributed. Unfortunately, this assumption is unrealistic in large …
Unsupervised domain adaptation for video object grounding with cascaded debiasing learning
This paper addresses the Unsupervised Domain Adaptation (UDA) for the dense frame
prediction task-Video Object Grounding (VOG). This investigation springs from the …
prediction task-Video Object Grounding (VOG). This investigation springs from the …