Search Results for author:
Found 7 papers, 1 papers with code
CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition
To enhance the sensitivity to the walking pattern while maintaining the robustness of recognition, we present a Complementary Learning with neural Architecture Search (CLASH) framework, consisting of walking pattern sensitive gait descriptor named dense spatial-temporal field (DSTF) and neural architecture search based complementary learning (NCL).
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry
Large language models (LLMs) have revolutionized Natural Language Processing (NLP) by minimizing the need for complex feature engineering.
Context Disentangling and Prototype Inheriting for Robust Visual Grounding
In this paper, we propose a novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both scenes.
Counterfactual-based Saliency Map: Towards Visual Contrastive Explanations for Neural Networks
Considering the insufficient study on such complex causal questions, we make the first attempt to explain different causal questions by contrastive explanations in a unified framework, ie., Counterfactual Contrastive Explanation (CCE), which visually and intuitively explains the aforementioned questions via a novel positive-negative saliency-based explanation scheme.
sub-region localized hashing for fine-grained image retrieval
Specifically, to localize diverse local regions, a sub-region localization module is developed to learn discriminative local features by locating the peaks of non-overlap sub-regions in the feature map.
Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals
In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval.
Deep Ordinal Hashing with Spatial Attention
However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images.
