Search Results for author:
Found 11 papers, 2 papers with code
Bias Mitigating Few-Shot Class-Incremental Learning
Some recent methods somewhat alleviate the accuracy imbalance between base and incremental classes by fine-tuning the feature extractor in the incremental sessions, but they further cause the accuracy imbalance between past and current incremental classes.
Generating Human-Centric Visual Cues for Human-Object Interaction Detection via Large Vision-Language Models
To capitalize on these rich Human-Centric Visual Cues, we propose a novel approach named HCVC for HOI detection.
Federated Class-Incremental Learning with Prompting
For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid).
FedVMR: A New Federated Learning method for Video Moment Retrieval
As the first that is explored in VMR field, the new task is defined as video moment retrieval with distributed data.
Prototype-Based Layered Federated Cross-Modal Hashing
And due to the problems of statistical heterogeneity, model heterogeneity, and forcing each client to accept the same parameters, applying federated learning to cross-modal hash learning becomes very tricky.
Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval
To narrow the domain differences between sketches and images, we extract edge maps for natural images and treat them as a bridge between images and sketches, which have similar content to images and similar style to sketches.
ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator
Recently, several Vision Transformer (ViT) based methods have been proposed for Fine-Grained Visual Classification (FGVC). These methods significantly surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC tasks. However, there are some limitations when applying ViT directly to FGVC. First, ViT needs to split images into patches and calculate the attention of every pair, which may result in heavy redundant calculation and unsatisfying performance when handling fine-grained images with complex background and small objects. Second, a standard ViT only utilizes the class token in the final layer for classification, which is not enough to extract comprehensive fine-grained information.
Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data
With the vigorous development of multimedia equipment and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic.
Weakly-Supervised Online Hashing
However, existing hashing methods for social image retrieval are based on batch mode which violates the nature of social images, i. e., social images are usually generated periodically or collected in a stream fashion.
Deep Recurrent Quantization for Generating Sequential Binary Codes
To the end, when the model is trained, a sequence of binary codes can be generated and the code length can be easily controlled by adjusting the number of recurrent iterations.
Optimized Cartesian $K$-Means
In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace.
