Search Results for author:
Found 9 papers, 9 papers with code
VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting
Zero-Shot Object Counting (ZSOC) aims to count referred instances of arbitrary classes in a query image without human-annotated exemplars.
Ranked #2 on
Zero-Shot Counting
on FSC147
Correlation-guided Query-Dependency Calibration in Video Representation Learning for Temporal Grounding
Dummy tokens conditioned by text query take portions of the attention weights, preventing irrelevant video clips from being represented by the text query.
Ranked #1 on
Highlight Detection
on TvSum
Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification
While TDM influences high-level feature maps by task-adaptive calibration of channel-wise importance, we further introduce Instance Attention Module (IAM) operating in intermediate layers of feature extractors to instance-wisely highlight object-relevant channels, by extending QAM.
Leveraging Hidden Positives for Unsupervised Semantic Segmentation
Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores.
Ranked #2 on
Unsupervised Semantic Segmentation
on Potsdam-3
Query-Dependent Video Representation for Moment Retrieval and Highlight Detection
As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation.
Ranked #2 on
Highlight Detection
on TvSum
Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition
A dramatic increase in real-world video volume with extremely diverse and emerging topics naturally forms a long-tailed video distribution in terms of their categories, and it spotlights the need for Video Long-Tailed Recognition (VLTR).
Difficulty-Aware Simulator for Open Set Recognition
Furthermore, moderate- and easy-difficulty samples are also yielded by our modified GAN and Copycat, respectively.
Tailoring Self-Supervision for Supervised Learning
Our exhaustive experiments validate the merits of LoRot as a pretext task tailored for supervised learning in terms of robustness and generalization capability.
Ranked #9 on
Data Augmentation
on ImageNet
Task Discrepancy Maximization for Fine-grained Few-Shot Classification
Specifically, TDM learns task-specific channel weights based on two novel components: Support Attention Module (SAM) and Query Attention Module (QAM).
Ranked #9 on
Few-Shot Image Classification
on CUB 200 5-way 5-shot
(using extra training data)
