Multi-modality object Re-IDentification (ReID) targets to retrieve special objects by integrating complementary information from diverse visual sources. However, existing models that are trained on modality-complete datasets typically exhibit significantly degraded discrimination during inference with modality-incomplete inputs. This disparity highlights the necessity of developing a robust multi-modality ReID model that remains effective in real-world applications. For that, this paper delivers a flexible framework tailored for more realistic multi-modality retrieval scenario, dubbed as Miss-ReID, which is the first work to friendly support both the modality-missing training and inference conditions. The core of Miss-ReID lies in compensating for missing visual cues via vision-text knowledge transfer driven by Vision-Language foundation Models (VLMs), effectively mitigating performance degradation. In brief, we capture diverse visual features from accessible modalities first, and then build memory banks to store heterogeneous prototypes for each identity, preserving multi-modality characteristics. Afterwards, we employ structure-aware query interactions to dynamically distill modality-invariant object structures from existing localized visual patches, which are further reversed into pseudo-word tokens that encapsulate the identity-relevant structural semantics. In tandem, the inverted tokens, integrated with learnable modality prompts, are embedded into crafted textual template to form the personalized linguistic descriptions tailored for diverse modalities. Ultimately, harnessing VLMs' inherent vision-text alignment capability, the resulting textual features effectively function as compensatory semantic representations for missing visual modalities, after being optimized with some memory-based alignment constraints. Extensive experiments demonstrate our model's efficacy and superiority over state-of-the-art methods in various modality-missing scenarios, and our endeavors further propel multi-modality ReID into real-world applications.

For preeminent unsupervised visible-infrared person re-identification (US-VI-ReID), existing studies typically adhere to a two-step paradigm,i.e., intra-modality clustering and inter-modality matching. Nevertheless, high intra-modality variations may result in suboptimal clusters containing intricate pedestrians, while significant inter-modality discrepancies further complicate their cross-modality associations. Most existing methods fail to adopt a differentiated approach for samples of varying difficulty, especially intricate ones. To address this, we propose enabling the model to gradually establish cross-modality associations from easy to hard, mimicking human learning patterns to avoid error accumulation caused by intricate pedestrians. To this end, we propose a Cross-modality Self-paced Association Network, termed CSANet, embracing Twain Bipartite Graph Matching (TBGM), Cross-curriculum Association Prompter (CAP) and Instance-Prototype Consistency Constraint (IPCC) modules. TBGM conceives a graph-driven metric to tailor athree-levelcurriculum (plain,moderateandintricate) for self-paced cross-modality learning. CAP transfers high-confidence associations deduced from the plain subsets to intricate ones, prompting exploring more complex cross-modality relationships. Alongside CAP, IPCC further enforces the intricate instances to mimic their prototype characteristics, facilitating their discriminative feature learning. Extensive experiments demonstrate CSANet’s superiority over state-of-the-art methods, highlighting the potential of self-paced learning for US-VI-ReID.

For visible-infrared person re-identification (VI-ReID), current models that compensate modality-specific information strive to generate missing modality images from existing ones to bridge the cross-modality discrepancies. Despite that, those generated images often suffer from low qualities due to the significant modality gap and include interfering information, e.g., inconsistent colors, thus severely degrading the subsequent VI-ReID performance. Alternatively, we propose a feature-level modality compensation network, i.e., FMCNet+, for VI-ReID in this article as an improved version of our previous work (FMCNet). The core of FMCNet+ is to compensate for the missing modality-specific information at the feature level, rather than at the image level, enabling our model to generate more person-related and discriminative modality-specific features for VI-ReID. Concretely, FMCNet+ aims to progressively generate missing modality-specific features by fully exploring the relationships among single-modality features, modality-shared features, and modality-specific features, instead of directly generating them through a generative adversarial way as in the previous FMCNet. To this end, three modules, i.e., single-modality feature decomposition (SFD), modality characteristic dictionary learning (MCDL), and missing modality-specific feature compensation (MMFC), are incorporated in FMCNet+. Experimental results demonstrate the superiority of our proposed FMCNet+ over existing ones, especially for those that compensate for modality-specific information at the image level. Our intriguing findings highlight the necessity of feature-level modality compensation in VI-ReID. Our code and pre-trained models will be released on https://github.com/jssyzsfzy/FMCNet_series.

Toward desirable saliency prediction, the types and numbers of inputs for a salient object detection (SOD) algorithm may dynamically change in many real-life applications. However, existing SOD algorithms are mainly designed or trained for one particular type of inputs, failing to be generalized to other types of inputs. Consequentially, more types of SOD algorithms need to be prepared in advance for handling different types of inputs, raising huge hardware and research costs. Differently, in this paper, we propose a new type of SOD task, termed Arbitrary Modality SOD (AM SOD). The most prominent characteristics of AM SOD are that the modality types and modality numbers will be arbitrary or dynamically changed. The former means that the inputs to the AM SOD algorithm may be arbitrary modalities such as RGB, depths, or even any combination of them. While, the latter indicates that the inputs may have arbitrary modality numbers as the input type is changed, e.g. single-modality RGB image, dual-modality RGB-Depth (RGB-D) images or triple-modality RGB-Depth-Thermal (RGB-D-T) images. Accordingly, a preliminary solution to the above challenges, i.e. a modality switch network (MSN), is proposed in this paper. In particular, a modality switch feature extractor (MSFE) is first designed to extract discriminative features from each modality effectively by introducing some modality indicators, which will generate some weights for modality switching. Subsequently, a dynamic fusion module (DFM) is proposed to adaptively fuse features from a variable number of modalities based on a novel Transformer structure. Finally, a new dataset, named AM-XD, is constructed to facilitate research on AM SOD. Extensive experiments demonstrate that our AM SOD method can effectively cope with changes in the type and number of input modalities for robust salient object detection. Our code and AM-XD dataset will be released on https://github.com/nexiakele/AMSODFirst.