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Found 9 papers, 4 papers with code
ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer
However, most previous works treat the live as a whole item and explore the Click-through-Rate (CTR) prediction framework on item-level, neglecting that the dynamic changes that occur even within the same live room.
Generation-Guided Multi-Level Unified Network for Video Grounding
Video grounding aims to locate the timestamps best matching the query description within an untrimmed video.
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset
Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG.
Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity.
Ranked #9 on
Video Retrieval
on MSVD
(using extra training data)
MlTr: Multi-label Classification with Transformer
The task of multi-label image classification is to recognize all the object labels presented in an image.
Ranked #12 on
Multi-Label Classification
on MS-COCO
CAT: Cross Attention in Vision Transformer
In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps capture global information.
ES-Net: Erasing Salient Parts to Learn More in Re-Identification
In this paper, we propose a novel network, Erasing-Salient Net (ES-Net), to learn comprehensive features by erasing the salient areas in an image.
Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification
In this paper, to complement these low recall neighbor pseudo labels, we propose a joint learning framework to learn better feature embeddings via high precision neighbor pseudo labels and high recall group pseudo labels.
Progressive Transfer Learning
Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch.
