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Video shot-boundary detection: issues, challenges and solutions
The integration of high data transmission rates and the recent digital multimedia technology, paves the way to access a huge amount of video over the...
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Shot boundary detection using multimodal Siamese network
Shot Boundary Detection (SBD) is one of the most interesting pre-processing tasks involving all intelligent video analysis applications. An efficient...
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A gradient based dual detection model for shot boundary detection
An efficient video shot boundary detection is highly desirable for subsequent semantic video content analysis and retrieval applications. The major...
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Shot boundary detection in video using dual-stage optimized VGGNet based feature fusion and classification
Shot boundary detection (SBD) in video sequences is a key process in the analysis, retrieval, and summarization tasks of video content. The major...
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Visual significance model based temporal signature for video shot boundary detection
Video shot boundary detection (VSBD) is the fundamental step for video processing algorithms. The goal of any VSBD algorithm is to detect the...
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Shot Boundary Detection with Augmented Annotations
In recent years, deep learning approaches have been considered to provide state-of-the-art results in shot boundary detection. These approaches...
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A novel method for video shot boundary detection using CNN-LSTM approach
Due to the rapid growth of digital videos and the massive increase in video content, there is an urgent need to develop efficient automatic video...
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Enforced clustering for zero-to-one-shot texture anomaly detection
Recent studies on anomaly detection (AD) for industrial products typically address the problem in an unsupervised manner, requiring only normal data...
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MPF-Net: multi-projection filtering network for few-shot object detection
Deep learning-based object detection has made tremendous progress in the field of intelligent vision systems. However, one of its major complaints is...
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A fast and robust shot detection method in HEVC/H.265 compressed video
Temporal segmentation of video into shots is the first step in most video analysis. This is because within a shot, consecutive frames are similar to...
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FS-3DSSN: an efficient few-shot learning for single-stage 3D object detection on point clouds
The current 3D object detection methods have achieved promising results for conventional tasks to detect frequently occurring objects like cars,...
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Transformer-based few-shot object detection in traffic scenarios
In few-shot object detection (FSOD), many approaches retrain the detector in the inference stage, which is unrealistic in real applications....
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Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts
Recently, the powerful generalization ability exhibited by foundation models has brought forth new solutions for zero-shot anomaly segmentation...
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Few-Shot Learning with Novelty Detection
Machine learning has achieved considerable success in data-intensive applications, yet encounters challenges when confronted with small datasets....
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ITFD: an instance-level triplet few-shot detection network under weighted pair-resampling
Few-shot object detection has been widely applied in industrial applications, endangered detection, tumor lesion detection, etc. Although many...
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Adapt to Scarcity: Few-Shot Deepfake Detection via Low-Rank Adaptation
The boundary between AI-generated images and real photographs is becoming increasingly narrow, thanks to the realism provided by contemporary...
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Learning General and Specific Embedding with Transformer for Few-Shot Object Detection
Few-shot object detection (FSOD) studies how to detect novel objects with few annotated examples effectively. Recently, it has been demonstrated that...
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V2T: video to text framework using a novel automatic shot boundary detection algorithm
The generation of natural language descriptions for a video has been reported by many researchers till now. But, it is still the most interesting...
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MBA-NER: Multi-Granularity Entity Boundary-Aware Contrastive Enhanced for Two-Stage Few-Shot Named Entity Recognition
Few-Shot Named Entity Recognition (FS-NER) can identify entity boundaries and types with a limited set of labeled training instances. Pre-trained and...
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Robust Feature Space Organization with Distillation for Few-Shot Object Detection
Few-Shot Object Detection has received strong interest recently, especially as collecting annotated training data for new and varied problems becomes...