21 accepted papers
Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxies as deterministic points in the embedding space. This rigid assumption causes severe gradient conflicts in multi-label scenarios, where gradient c
Asymmetric image retrieval (AIR), which typically employs a compact model for the query side and a large model for the database server, has garnered significant attention in resource-constrained environments. While deep hashing methods have shown great potential in large-scale image retrieval, curre
Text-video retrieval tasks have seen significant improvements due to the recent development of large-scale vision-language pre-trained models. Traditional methods primarily focus on video representations or cross-modal alignment, while recent works shift toward enriching text expressiveness to bette
Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image. Recent preference alignment methods typically rely on supervision distilled from stronger models such as GPT. However, this offline paradigm introdu
Person Re-Identification (ReID) aims to match individuals across different camera views, but occlusions in real-world scenarios, such as vehicles or crowds, hinder feature extraction and matching. Current occluded ReID methodologies typically leverage visual augmentation techniques in an attempt to
Multimodal hashing projects multimodal data into compact binary codes, enabling rapid and storage-efficient retrieval of large-scale multimedia content. In practical scenarios, the issue of missing modality frequently arises when dealing with multimodal data. Existing incomplete multimodal hashing t
Extensive advancements have been made in person ReID through the mining of semantic information. Nevertheless existing methods that utilize semantic-parts from a single image modality do not explicitly achieve this goal. Whiteness the impressive capabilities in multimodal understanding of Vision Lan…
Although image-dependent adversarial attacks have been studied, the more challenging image-agnostic adversarial attack for deep hashing remains an unexplored territory. In this paper, we take the first attempt on the more efficient and malicious targeted universal adversarial attack (TUAA) for deep…
Deep incremental hashing has become a subject of considerable interest due to its capability to learn hash codes in an incremental manner, eliminating the need to generate codes for classes that have already been learned. However, accommodating more classes requires longer hash codes, and regenerati…
"Model inversion (MI) attack reconstructs the private training data of a target model given its output, posing a significant threat to deep learning models and data privacy. On one hand, most of existing MI methods focus on searching for latent codes to represent the target identity, yet this iterat…
Large-scale data from real-world usually follow a long-tailed distribution (i.e., a few majority classes occupy plentiful training data, while most minority classes have few samples), making the hyperplanes heavily skewed to the minority classes. Traditionally, reweighting is adopted to make the hyp…
Video-text retrieval is an emerging stream in both computer vision and natural language processing communities, which aims to find relevant videos given text queries. In this paper, we study the notoriously challenging task, i.e., Unsupervised Domain Adaptation Video-text Retrieval (UDAVR), wherein…
The recent advance has shown that few-shot learning may be a promising way to alleviate the data reliance of remote sensing image scene classification. However, most existing works focus on extracting distinguishable features only from visual modality, while the problem of learning knowledge from mu…
The lack of supervised information is the pivotal problem in unsupervised hashing. Most methods leverage deep features extracted from pre-trained models to generate semantic similarities as supervised information. These fixed features are, however, neither designed originally for retrieval nor updat…
Hamming space retrieval can achieve constant-time image search, which is more efficient than linear scan. In Hamming space retrieval, the data points inside the Hamming ball imply retrievable while the data points outside are irretrievable. Therefore, it is crucial to explicitly characterize the Ham…
Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that the samples in tail categories fail to depict their intra-class diversity. Humans can imagine a sample in new poses, scenes and view angles…
Person re-identification (ReID) aims at retrieving images of the same person across non-overlapping camera views. The prior works focus on either fully supervised or unsupervised ReID settings, and achieve remarkable performances. In real scenarios, however, the major annotation cost comes from matc…
Accurate detection of multi-oriented text that accounts for a large proportion in real practice is of great significance. The performance has improved rapidly on common benchmarks in recent years. However, dense long text case and the quality of detection are easy to be overlooked. Direct regression…
Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image retrieval. How to optimize discrete hash bits is always the focus in deep hashing methods. A common strategy in these methods is to adopt an activation function, e.g. sigmoid() or tanh(), and minimize a quan…
Hashing has shown great potential in large-scale image retrieval due to its storage and computation efficiency, especially the recent deep supervised hashing methods. To achieve promising performance, deep supervised hashing methods require a large amount of training data from different classes. How…
Deep supervised hashing methods for multi-label image retrieval have achieved great success nowadays. However, these methods only take the similarity between the database images and the query images into account, but they ignore the uniqueness of the database images when deciding on their rankings.…