Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i.e. task-individual). In this paper, a collaborative framework called MatchDet (i.e. task-collaborative) is proposed for image matching and object detection to obtain mutual improvements. To achieve the collaborative learning of the two tasks, we propose three novel modules, including a Weighted Spatial Attention Module (WSAM) for Detector, and Weighted Attention Module (WAM) and Box Filter for Matcher. Specifically, the WSAM highlights the foreground regions of target image to benefit the subsequent detector, the WAM enhances the connection between the foreground regions of pair images to ensure high-quality matches, and Box Filter mitigates the impact of false matches. We evaluate the approaches on a new benchmark with two datasets called Warp-COCO and miniScanNet. Experimental results show our approaches are effective and achieve competitive improvements.

Anomaly detection (AD) has gained widespread attention due to its ability to identify defects in industrial scenarios using only normal samples. Although traditional AD methods achieved acceptable performance, they mainly focus on the current set of examples solely, leading to catastrophic forgetting of previously learned tasks when trained on a new one. Due to the limitation of flexibility and the requirements of realistic industrial scenarios, it is urgent to enhance the ability of continual adaptation of AD models. Therefore, this paper proposes a unified framework by incorporating continual learning (CL) to achieve our newly designed task of continual anomaly detection (CAD). Note that, we observe that data augmentation strategy can make AD methods well adapted to supervised CL (SCL) via constructing anomaly samples. Based on this, we hence propose a novel method named Distribution of Normal Embeddings (DNE), which utilizes the feature distribution of normal training samples from past tasks. It not only effectively alleviates catastrophic forgetting in CAD but also can be integrated with SCL methods to further improve their performance. Extensive experiments and visualization results on the popular benchmark dataset MVTec AD, have demonstrated advanced performance and the excellent continual adaption ability of our proposed method compared to other AD methods. To the best of our knowledge, we are the first to introduce and tackle the task of CAD. We believe that the proposed task and benchmark will be beneficial to the field of AD. Our code is available in thesupplementary material.

Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Although region-based methods are less likely to encounter issues with model generalizability than label-dependency methods, they often generate hundreds of meaningless or noisy proposals with non-discriminative information, and the contextual dependency among the localized regions is often ignored or over-simplified. This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning. Specifically, we propose category-aware weak supervision to concentrate on non-existent categories so as to provide deterministic information for local feature learning, restricting the local branch to focus on more high-quality regions of interest. Moreover, we develop a cross-granularity attention module to explore the complementary information between global and local features, which can build the high-order feature correlation containing not only global-to-local, but also local-to-local relations. Both advantages guarantee a boost in the performance of the whole network. Extensive experiments on two large-scale datasets (MS-COCO and VOC 2007) demonstrate that our framework achieves superior performance over state-of-the-art methods.

With the advances in deep learning, deep-hashing methods have achieved promising results for image retrieval. However, the problem of the distribution gap between training data and test data remains unsolved. Existing methods rely too much on manually labeled information to construct similarity matrices as supervision signals and focus less on pre-trained networks that can extract semantic information. This limits the generalization performance of the network and produces less discriminative hash codes. In this paper, we propose a novel hashing method, deep self-learning hashing (DSLH), that uses a self-learning strategy with labels constructed using the pre-trained features to enhance the embedded representation of the hash codes. Furthermore, we develop an improved loss function that preserves the similarity of the hash codes while reducing the quantization loss and ensuring the balance of the hash codes. Our analysis and experimental results demonstrate that, compared with recent image-retrieval methods, our method can achieve greater retrieval performance on two benchmark datasets: CIFAR-10 and NUS-WIDE.

Given the benefits of their low storage requirements and high retrieval efficiency, hashing methods have attracted considerable attention for large scale cross-modal retrieval and significant progress has been made recently. However, the existing methods generally use the label-guided similarity matrix to measure the similarities of sample pairs, which limits their semantic representation capability. Moreover, the sample imbalance of different classes would bias the learning process toward majority classes and affect the retrieval performance. To boost the semantic representation, to alleviate the impact of data imbalance, and to obtain a high-ranking correlation of hash code pairs, we propose a novel hashing method that uses a semantic correctional similarity matrix to enhance the embedded representation of sample pairs. Furthermore, we propose a novel cross-modal multi-similarity loss based on the general pair weighting framework to collect and weight informative pairs efficiently and accurately, thus improving the retrieval performance. Our analysis and experimental results demonstrate that, compared with recent cross-modal retrieval methods, our methods achieve greater retrieval performance on two datasets MIRFlickr-25K and NUS-WIDE.

Hashing-based cross-modal search which aims to map multiple modality features into binary codes has attracted increasingly attention due to its storage and search efficiency especially in large-scale database retrieval. Recent unsupervised deep cross-modal hashing methods have shown promising results. However, existing approaches typically suffer from two limitations: (1) They usually learn cross-modal similarity information separately or in a redundant fusion manner, which may fail to capture semantic correlations among instances from different modalities sufficiently and effectively. (2) They seldom consider the sampling and weighting schemes for unsupervised cross-modal hashing, resulting in the lack of satisfactory discriminative ability in hash codes.

Hashing methods have been widely applied to approximate nearest neighbor search for large-scale image retrieval, due to its computation efficiency and retrieval quality. Deep hashing can improve the retrieval quality by representation learning and hash coding. Existing deep hashing methods only take image spatial features into account and result in the lack of accurate semantic similarities of images pairs. In this paper, a novel deep hashing network, Deep Captioning Hashing Network (DCHN), is proposed to enhance semantic similarities of hash codes. In DCHN, the binary hash codes are generated in a Bayesian learning framework by fusing deep spatial representation and deep content captioning representation obtained by image captioning. Our analysis and simulation results have demonstrated that DCHN can achieve better retrieval performance in complex scene images compared with other supervised hashing methods and unsupervised methods on two complex scene image datasets MS COCO and NUS-WIDE.