Search Results for author:
Found 8 papers, 5 papers with code
RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition
Conventional multi-modal multi-label emotion recognition (MMER) from videos typically assumes full availability of visual, textual, and acoustic modalities.
Behavior Modeling Space Reconstruction for E-Commerce Search
Delivering superior search services is crucial for enhancing customer experience and driving revenue growth.
AskChart: Universal Chart Understanding through Textual Enhancement
To effectively train AskChart, we design a three-stage training strategy to align visual and textual modalities for learning robust visual-textual representations and optimizing the learning of the MoE layer.
Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval
SSAN is based on two newly proposed modules in video retrieval: (1) An efficient Self-supervised Keyframe Extraction (SKE) module to reduce redundant frame features, (2) A robust Similarity Pattern Detection (SPD) module for temporal alignment.
Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval
For traditional model upgrades, the old model will not be replaced by the new one until the embeddings of all the images in the database are re-computed by the new model, which takes days or weeks for a large amount of data.
TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision
Previous methods typically start from frame-to-frame similarity matrix generated by cosine similarity between frame-level features of the input video pair, and then detect and refine the boundaries of copied segments on similarity matrix under temporal constraints.
A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection
In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset.
