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Found 7 papers, 2 papers with code
EgoViT: Pyramid Video Transformer for Egocentric Action Recognition
Different from previous video transformers, which use the same static embedding as the class token for diverse inputs, we propose a dynamic class token generator that produces a class token for each input video by analyzing the hand-object interaction and the related motion information.
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation
To this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling framework to transfer the pretraining knowledge to the target real domain.
MKANet: A Lightweight Network with Sobel Boundary Loss for Efficient Land-cover Classification of Satellite Remote Sensing Imagery
Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by down sampling or cropping them into small patches less than 512*512 pixels before sending them to a deep neural network.
Label prompt for multi-label text classification
In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model.
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions.
Ranked #18 on
Click-Through Rate Prediction
on Criteo
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features.
Ranked #17 on
Click-Through Rate Prediction
on Criteo
Video In Sentences Out
We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it.
