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Found 11 papers, 4 papers with code
ChinaTelecom System Description to VoxCeleb Speaker Recognition Challenge 2023
This technical report describes ChinaTelecom system for Track 1 (closed) of the VoxCeleb2023 Speaker Recognition Challenge (VoxSRC 2023).
You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos
To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual features (I-frame, motion vector and residual features) for effective and efficient grounding.
Hypotheses Tree Building for One-Shot Temporal Sentence Localization
Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query.
Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval
In addition, due to the domain gap between different datasets, directly applying these pre-trained models to an unseen domain leads to a significant performance drop.
Hierarchical Local-Global Transformer for Temporal Sentence Grounding
To address this issue, in this paper, we propose a novel Hierarchical Local-Global Transformer (HLGT) to leverage this hierarchy information and model the interactions between different levels of granularity and different modalities for learning more fine-grained multi-modal representations.
Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding
Temporal sentence grounding aims to localize a target segment in an untrimmed video semantically according to a given sentence query.
V3H: View Variation and View Heredity for Incomplete Multi-view Clustering
Inspired by the variation and the heredity in genetics, V3H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively.
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods.
Double Self-weighted Multi-view Clustering via Adaptive View Fusion
For the first self-weighted operation, it assigns different weights to different features by introducing an adaptive weight matrix, which can reinforce the role of the important features in the joint representation and make each graph robust.
Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat
However, different views often have distinct incompleteness, i. e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views).
Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study
Detecting inaccurate smart meters and targeting them for replacement can save significant resources.
