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Found 14 papers, 8 papers with code
Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model
In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e. g., language descriptions and synchronous audios.
PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout
Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design.
Scanning Only Once: An End-to-end Framework for Fast Temporal Grounding in Long Videos
Video temporal grounding aims to pinpoint a video segment that matches the query description.
SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization
To address the above limitations, we propose the Structure Information Modeling Transformer (SIM-Trans) to incorporate object structure information into transformer for enhancing discriminative representation learning to contain both the appearance information and structure information.
Ranked #8 on
Fine-Grained Image Classification
on CUB-200-2011
Video Similarity and Alignment Learning on Partial Video Copy Detection
To address the above issues, we propose the Video Similarity and Alignment Learning (VSAL) approach, which jointly models spatial similarity, temporal similarity and partial alignment.
HANet: Hierarchical Alignment Networks for Video-Text Retrieval
Based on these, we naturally construct hierarchical representations in the individual-local-global manner, where the individual level focuses on the alignment between frame and word, local level focuses on the alignment between video clip and textual context, and global level focuses on the alignment between the whole video and text.
Self-supervised Video Retrieval Transformer Network
In addition, most retrieval systems are based on frame-level features for video similarity searching, making it expensive both storage wise and search wise.
A New Benchmark and Approach for Fine-grained Cross-media Retrieval
To the best of our knowledge, it is the first benchmark with 4 media types for fine-grained cross-media retrieval.
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization
Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: (1) n-pathway end-to-end discriminative localization network is designed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation.
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers.
Fine-grained Visual-textual Representation Learning
As is known to all, when we describe the object of an image via textual descriptions, we mainly focus on the pivotal characteristics, and rarely pay attention to common characteristics as well as the background areas.
Fine-Grained Image Classification via Combining Vision and Language
Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy.
Fine-graind Image Classification via Combining Vision and Language
Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy.
Object-Part Attention Model for Fine-grained Image Classification
Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories.
