Cross-Scale Context Extracted Hashing for Fine-Grained Image Binary Encoding

Deep hashing has been widely applied to large-scale image retrieval tasks owing to efficient computation and low storage cost by encoding high-dimensional image data into binary codes. Since binary codes do not contain as much information as float features, the essence of binary encoding is preserving the main context to guarantee retrieval quality. However, the existing hashing methods have great limitations on suppressing redundant background information and accurately encoding from Euclidean space to Hamming space by a simple sign function. In order to solve these problems, a Cross-Scale Context Extracted Hashing Network (CSCE-Net) is proposed in this paper. Firstly, we design a two-branch framework to capture fine-grained local information while maintaining high-level global semantic information. Besides, Attention guided Information Extraction module (AIE) is introduced between two branches, which suppresses areas of low context information cooperated with global sliding windows. Unlike previous methods, our CSCE-Net learns a content-related Dynamic Sign Function (DSF) to replace the original simple sign function. Therefore, the proposed CSCE-Net is context-sensitive and able to perform well on accurate image binary encoding. We further demonstrate that our CSCE-Net is superior to the existing hashing methods, which improves retrieval performance on standard benchmarks.

DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features

Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their local features. Previous learning-based studies mainly focus on either global or local image representation learning to tackle the retrieval task. In this paper, we abandon the two-stage paradigm and seek to design an effective single-stage solution by integrating local and global information inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention at first. Components orthogonal to the global image representation are then extracted from the local information. At last, the orthogonal components are concatenated with the global representation as a complementary, and then aggregation is performed to generate the final representation. The whole framework is end-to-end differentiable and can be trained with image-level labels. Extensive experimental results validate the effectiveness of our solution and show that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.

Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning

Reconstructing perceived images based on brain signals measured with functional magnetic resonance imaging (fMRI) is a significant and meaningful task in brain-driven computer vision. However, the inconsistent distribution and representation between fMRI signals and visual images cause the heterogeneity gap, which makes it challenging to learn a reliable mapping between them. Moreover, considering that fMRI signals are extremely high-dimensional and contain a lot of visually-irrelevant information, effectively reducing the noise and encoding powerful visual representations for image reconstruction is also an open problem. We show that it is possible to overcome these challenges by learning a visually-relevant latent representation from fMRI signals guided by the corresponding visual features, and recovering the perceived images via adversarial learning. The resulting framework is called Dual-Variational Autoencoder/ Generative Adversarial Network (D-VAE/GAN). By using a novel 3-stage training strategy, it encodes both cognitive and visual features via a dual structure variational autoencoder (D-VAE) to adapt cognitive features to visual feature space, and then learns to reconstruct perceived images with generative adversarial network (GAN). Extensive experiments on three fMRI recording datasets show that D-VAE/GAN achieves more accurate visual reconstruction compared with the state-of-the-art methods.