Current state-of-the-art image-text matching methods implicitly align the visual-semantic fragments, like regions in images and words in sentences, and adopt cross-attention mechanism to discover fine-grained cross-modal semantic correspondence. However, the cross-attention mechanism may bring redundant or irrelevant region-word alignments, degenerating retrieval accuracy and limiting efficiency. Although many researchers have made progress in mining meaningful alignments and thus improving accuracy, the problem of poor efficiency remains unresolved. In this work, we propose to learn fine-grained image-text matching from the perspective of information coding. Specifically, we suggest a coding framework to explain the fragments aligning process, which provides a novel view to reexamine the cross-attention mechanism and analyze the problem of redundant alignments. Based on this framework, a Cross-modal Hard Aligning Network (CHAN) is designed, which comprehensively exploits the most relevant region-word pairs and eliminates all other alignments. Extensive experiments conducted on two public datasets, MS-COCO and Flickr30K, verify that the relevance of the most associated word-region pairs is discriminative enough as an indicator of the image-text similarity, with superior accuracy and efficiency over the state-of-the-art approaches on the bidirectional image and text retrieval tasks. Our code will be available at https://github.com/ppanzx/CHAN. 

Fine-grained Image-text Matching by Cross-modal Hard Aligning Network

Image-text matching, a bridge connecting image and language, is an important task, which generally learns a holistic cross-modal embedding to achieve a high-quality semantic alignment between the two modalities. However, previous studies only focus on capturing fragment-level relation within a sample from a particular modality, e.g., salient regions in an image or text words in a sentence, where they usually pay less attention to capturing instance-level interactions among samples and modalities, e.g., multiple images and texts. In this paper, we argue that sample relations could help learn subtle differences for hard negative instances, and thus transfer shared knowledge for infrequent samples should be promising in obtaining better holistic embeddings. Therefore, we propose a novel hierarchical relation modeling framework (HREM), which explicitly capture both fragment-and instance-level relations to learn discriminative and robust cross-modal embeddings. Extensive experiments on Flickr30K and MS-COCO show our proposed method out-performs the state-of-the-art ones by 4%-10% in terms of rSum. Our code is available at https://github.com/CrossmodalGroup/HREM. 

Learning Semantic Relationship among Instances for Image-Text Matching

Human–robot collaborative assembly (HRCA) combines the flexibility and adaptability of humans with the efficiency and reliability of robots during collaborative assembly operations, which facilitates complex product assembly in the mass personalisation paradigm. The cognitive ability of robots to recognise and predict human actions and make responses accordingly is essential but currently still limited, especially when facing highly similar human actions. To improve the cognitive ability of robots in HRCA, firstly, a two-stage skeleton-RGB integrated model focusing on human-parts interaction is proposed to recognise highly similar human actions. Specifically, it consists of a feature guidance module and a feature fusion module, which can balance the accuracy and efficiency of human action recognition. Secondly, an online prediction approach is developed to predict human actions ahead of schedule, which includes a pre-trained skeleton-RGB integrated model and a preprocessing module. Thirdly, considering the positioning accuracy of the parts to be assembled and the continuous update of human actions, a dynamic response scheme of the robot is designed. Finally, the feasibility and effectiveness of the proposed model and approach are verified by a case study of a worm-gear decelerator assembly. The experimental results demonstrate that the proposed model achieves precise human action recognition with a high accuracy of 93.75% and a lower computational cost. Specifically, only 15 frames from a skeleton stream and 5 frames (less than 16 frames in general) from an RGB video stream are adopted. Moreover, it only takes 1.026 s to achieve online human action prediction based on the proposed prediction method. The dynamic response scheme of the robot is also proven to be feasible. It is expected that the efficiency of human–robot interaction in HRCA can be improved from a closed-loop view of perception, prediction, and response. 

Skeleton-RGB integrated highly similar human action prediction in human–robot collaborative assembly

Image-text retrieval aims to take the text (image) query to retrieve the semantically relevant images (texts), which is fundamental and critical in the search system, online shopping, and social network. Existing works have shown the effectiveness of visual-semantic embedding and unimodal knowledge exploiting (e.g., textual knowledge) in connecting the image and text. However, they neglect the implicit multimodal knowledge relations between these two modalities when the image contains information that is not directly described in the text, hindering the ability to connect the image and text with the implicit semantic relations. For instance, an image shows a person next to the “tap” but the pairing text description may only include the word “wash,” missing the washing tool “tap.” The implicit semantic relation between image object “tap” and text word “wash” can help to connect the above image and text. To sufficiently utilize the implicit multimodal knowledge relations, we propose a Multimodal Knowledge enhanced Visual-Semantic Embedding (MKVSE) approach building a multimodal knowledge graph to explicitly represent the implicit multimodal knowledge relations and injecting it to visual-semantic embedding for image-text retrieval task. The contributions in this article can be summarized as follows: (1) Multimodal Knowledge Graph (MKG) is proposed to explicitly represent the implicit multimodal knowledge relations between the image and text as intra-modal semantic relations and inter-modal co-occurrence relations. Intra-modal semantic relations provide synonymy information that is implicit in the unimodal data such as the text corpus. And inter-modal co-occurrence relations characterize the co-occurrence correlations (such as temporal, causal, and logical) that are implicit in image-text pairs. These two relations help establishing reliable image-text connections in the higher-level semantic space. (2) Multimodal Graph Convolution Networks (MGCN) is proposed to reason on the MKG in two steps to sufficiently utilize the implicit multimodal knowledge relations. In the first step, MGCN focuses on the intra-modal relations to distinguish other entities in the semantic space. In the second step, MGCN focuses on the inter-modal relations to connect multimodal entities based on co-occurrence correlations. The two-step reasoning manner can sufficiently utilize the implicit semantic relations between two modal entities to enhance the embeddings of the image and text. Extensive experiments are conducted on two widely used datasets, namely, Flickr30k and MSCOCO, to demonstrate the superiority of the proposed MKVSE approach in achieving state-of-the-art performances. The codes are available at https://github.com/PKU-ICST-MIPL/MKVSE-TOMM2023.

MKVSE: Multimodal Knowledge Enhanced Visual-semantic Embedding for Image-text Retrieval

Image-sentence retrieval has attracted extensive research attention in multimedia and computer vision due to its promising application. The key issue lies in jointly learning the visual and textual representation to accurately estimate their similarity. To this end, the mainstream schema adopts an object-word based attention to calculate their relevance scores and refine their interactive representations with the attention features, which, however, neglects the context of the object representation on the inter-object relationship that matches the predicates in sentences. In this paper, we propose a Cross-modal Semantic Enhanced Interaction method, termed CMSEI for image-sentence retrieval, which correlates the intra- and inter-modal semantics be-tween objects and words. In particular, we first design the intra-modal spatial and semantic graphs based reasoning to enhance the semantic representations of objects guided by the explicit relationships of the objects’ spatial positions and their scene graph. Then the visual and textual semantic representations are refined jointly via the inter-modal interactive attention and the cross-modal alignment. To correlate the context of objects with the textual context, we further refine the visual semantic representation via the cross-level object-sentence and word-image based interactive attention. Experimental results on seven standard evaluation metrics show that the proposed CMSEI outperforms the state-of-the-art and the alternative approaches on MS-COCO and Flickr30K benchmarks. 

/pdf/cross-modal-semantic-enhanced-interaction-for-image-sentence-3drljhpv.pdf

Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval

Image-text retrieval is a fundamental cross-modal task whose main idea is to learn image-text matching. Generally, according to whether there exist interactions during the retrieval process, existing image-text retrieval methods can be classified into independent representation matching methods and cross-interaction matching methods. The independent representation matching methods generate the embeddings of images and sentences independently and thus are convenient for retrieval with hand-crafted matching measures (e.g., cosine or Euclidean distance). As to the cross-interaction matching methods, they achieve improvement by introducing the interaction-based networks for inter-relation reasoning, yet suffer the low retrieval efficiency. This article aims to develop a method that takes the advantages of cross-modal inter-relation reasoning of cross-interaction methods while being as efficient as the independent methods. To this end, we propose a graph-based Cross-modal Graph Matching Network (CGMN), which explores both intra- and inter-relations without introducing network interaction. In CGMN, graphs are used for both visual and textual representation to achieve intra-relation reasoning across regions and words, respectively. Furthermore, we propose a novel graph node matching loss to learn fine-grained cross-modal correspondence and to achieve inter-relation reasoning. Experiments on benchmark datasets MS-COCO, Flickr8K, and Flickr30K show that CGMN outperforms state-of-the-art methods in image retrieval. Moreover, CGMM is much more efficient than state-of-the-art methods using interactive matching. The code is available at https://github.com/cyh-sj/CGMN.

Cross-modal Graph Matching Network for Image-text Retrieval

Local features contain crucial clues for face anti-spoofing. Convolutional neural networks (CNNs) are powerful in extracting local features, but the intrinsic inductive bias of CNNs limits the ability to capture long-range dependencies. This paper aims to develop a simple yet effective framework that is versatile in extracting both local information and long-range dependencies for face anti-spoofing. To this end, we propose a novel architecture, namely Conv-MLP, which incorporates local patch convolution with global multi-layer perceptrons (MLP). Conv-MLP breaks the inductive bias limitation of traditional full CNNs and can be expected to better exploit long-range dependencies. Furthermore, we design a new loss specifically for the face anti-spoofing task, namely moat loss. The moat loss benefits discriminative representations learning and can improve the generalization capability on unseen presentation attacks. In this work, multi-modal data are directly fused at the signal level to extract complementary features. Extensive experiments on single and multi-modal datasets demonstrate that Conv-MLP outperforms existing state-of-the-art methods while being more computationally efficient. The code is available at https://github.com/WeihangWANG/Conv-MLP.

Conv-MLP: A Convolution and MLP Mixed Model for Multimodal Face Anti-Spoofing

Action recognition has been a heated topic in computer vision for its wide application in vision systems. Previous approaches achieve improvement by fusing the modalities of the skeleton sequence and RGB video. However, such methods pose a dilemma between the accuracy and efficiency for the high complexity of the RGB video network. To solve the problem, we propose a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of the skeleton sequence and RGB frame is close to that of the skeleton sequence and RGB video. In this way, complementary information is retained while the complexity is reduced by a large margin. To better explore the correspondence of the two modalities, a two-stage fusion framework is introduced in the network. In the early fusion stage, we introduce a skeleton attention module that projects the skeleton sequence on the single RGB frame to help the RGB frame focus on the limb movement regions. In the late fusion stage, we propose a cross-attention module to fuse the skeleton feature and the RGB feature by exploiting the correlation. Experiments on two benchmarks, NTU RGB+D and SYSU, show that the proposed model achieves competitive performance compared with the state-of-the-art methods while reducing the complexity of the network.

/pdf/skeleton-sequence-and-rgb-frame-based-multi-modality-feature-i5n0uzda.pdf

Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition

3D time-of-flight (ToF) cameras have recently attracted widespread attention because of their potential in wide applications. However, the lack of versatile 3D image signal processor (ISP) is still an obstacle that hinders their popularity. The main challenges lie in the management of high-throughput raw data and the complexity of 3D image processing in hardware. To address these challenges, this paper presents a top-down 3D ISP with high real-time performance and high energy efficiency for indirect ToF (IToF) imaging. First, we propose configurable cache strategies to improve the efficiency of non-streaming memory accesses and support different IToF sensors. Then, to resolve the phase ambiguity problem commonly existing in IToF sensors, we develop a hardware-optimized cascaded multi-frequency fusion method, which not only supports up to six modulation frequencies but also facilitates the pixel-wise detection of multipath interference. Moreover, we propose a scalable calculation engine for depth calculation and calibration, 3D image enhancement, and post-processing. The pipeline is prototyped on a Xilinx ZCU102 FPGA board. Results show that the 3D ISP has negligible precision loss and low latency, achieving <inline-formula> <tex-math notation="LaTeX">$53.35\times $ </tex-math></inline-formula> energy efficiency improvement compared with the implementation on Intel CPU and <inline-formula> <tex-math notation="LaTeX">$55.45\times $ </tex-math></inline-formula> improvement over the ARM CPU implementation. When running at 75 MHz, the 3D ISP can support <inline-formula> <tex-math notation="LaTeX">$1280\times 960$ </tex-math></inline-formula> depth, intensity, and point cloud output at a frame rate of 30 f/s.

A Configurable and Real-Time Multi-Frequency 3D Image Signal Processor for Indirect Time-of-Flight Sensors

Active simultaneous localization and mapping (SLAM) is an important technique for mobile robots to autonomously explore and map an environment. This letter considers the problem of active SLAM with a prior topo-metric graph. Unlike existing works, we consider a more challenging scenario that there exist indistinguishable subgraphs in the prior graph and the robot does not know its initial position. In this scenario, the robot cannot plan a global path for mapping at the very beginning. To address this problem, we propose a novel framework which consists of two stages, active localization and active mapping. Our main contributions include a graph matching approach for localization and an active localization strategy. The graph matching approach is designed to reliably localize the ego position in the prior graph, by which the switch from active localization to active mapping can be determined reliably. The active localization strategy uses only the maximum likelihood state as input to reduce the computational complexity of motion policy generation. Experimental results demonstrate that our active SLAM system is able to reliably identify its ego position in the prior graph and map an environment in the presence of indistinguishable subgraphs.

Active SLAM With Prior Topo-Metric Graph Starting At Uncertain Position

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