Search Results for author:
Found 11 papers, 6 papers with code
In-Context Data Distillation with TabPFN
Foundation models have revolutionized tasks in computer vision and natural language processing.
Data-Efficient Multimodal Fusion on a Single GPU
The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs.
A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis
Ball mixing is a ball bearing production quality problem that is difficult to identify using traditional frequency domain analysis methods since it requires high frequency resolutions of the measurement signals and results in a long analyzing time.
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
Instead, texts often capture sub-regions of entire videos and are most semantically similar to certain frames within videos.
Ranked #17 on
Video Retrieval
on LSMDC
(using extra training data)
Weakly Supervised Action Selection Learning in Video
A common approach is to train a frame-level classifier where frames with the highest class probability are selected to make a video-level prediction.
Ranked #4 on
Weakly Supervised Action Localization
on FineAction
Context-Aware Scene Graph Generation With Seq2Seq Transformers
In this task, the model needs to detect objects and predict visual relationships between them.
Learning Effective Visual Relationship Detector on 1 GPU
We present our winning solution to the Open Images 2019 Visual Relationship challenge.
Guided Similarity Separation for Image Retrieval
Despite recent progress in computer vision, image retrieval remains a challenging open problem.
Cross-Class Relevance Learning for Temporal Concept Localization
We present a novel Cross-Class Relevance Learning approach for the task of temporal concept localization.
Semi-Supervised Exploration in Image Retrieval
We present our solution to Landmark Image Retrieval Challenge 2019.
DropoutNet: Addressing Cold Start in Recommender Systems
Latent models have become the default choice for recommender systems due to their performance and scalability.
