PV-TAM uses prompt-side semantics and a bias filter to improve attention-based and IoU localization metrics for vision-language models over answer-side baselines.
In: Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
roles
method 1polarities
use method 1representative citing papers
TGQ-Former uses metadata-guided hybrid queries and dual-gated modulation to improve visual token selection in multimodal e-commerce retrieval, raising average Hit Rate@100 by 6.04% over baselines.
The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.
citing papers explorer
-
Listening makes Vision Clear for VLMs
PV-TAM uses prompt-side semantics and a bias filter to improve attention-based and IoU localization metrics for vision-language models over answer-side baselines.
-
Text-Guided Visual Representation Learning for Robust Multimodal E-Commerce Recommendation
TGQ-Former uses metadata-guided hybrid queries and dual-gated modulation to improve visual token selection in multimodal e-commerce retrieval, raising average Hit Rate@100 by 6.04% over baselines.