Visible-infrared person re-identification (VI-ReID) has raised more attention in night-time surveillance applications due to the struggle to capture valid appearance information under poor illumination conditions via visible cameras. Existing works usually separate the modality-specific and modality-irrelevant information in visible and infrared features, or project features of two modalities into a unified embedding feature space directly, which aims to eliminate huge modality discrepancies. However, these methods neglect the intra-modality and inter-modality correlations. We argue that the correlations can implicitly guide the network to discover the modality-irrelevant information, thus more beneficial for eliminating huge modality discrepancies and preserving individual differences. To this end, we propose a novel framework, termed as correlation-guided semantic consistency network (CSC-Net), to explore and exploit the intra-modality and inter-modality correlations. Specifically, CSC-Net consists of a cross-modality semantic alignment (CSA) module, a cross-granularity discrepancy awareness (CDA) module, and a probability consistency constraint (PCC) module. CSA mines the inter-modality correlation by calculating the semantic similarity between modalities to explore modality-irrelevant features, and then transfers the learned features to the backbone network to face the input of only single modality images. To preserve the individual differences, CDA sufficiently utilizes the intra-modality correlation via exploring the multi-granularity discriminative information. Finally, PCC constrains the network at the probability level, cooperating with the CSA which constrains at the feature level, to further alleviate the modality discrepancy. Extensive experiments on two public VI-ReID datasets SYSU-MM01 and RegDB have verified the effectiveness of our approach.

Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained parameters by updating far fewer parameters, enabling edge devices and downstream applications to reuse the increasingly large foundation models deployed on the cloud. With the aim of helping researchers get the full picture and future directions of visual tuning, this survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models. Specifically, it provides a detailed background of visual tuning and categorizes recent visual tuning techniques into five groups: fine-tuning, prompt tuning, adapter tuning, parameter tuning, and remapping tuning. Meanwhile, it offers some exciting research directions for prospective pre-training and various interactions in visual tuning.

Fine-grained image retrieval mainly focuses on learning salient features from the seen subcategories as discriminative embedding while neglecting the problems behind zero-shot settings. We argue that retrieving fine-grained objects from unseen subcategories may rely on more diverse clues, which are easily restrained by the salient features learnt from seen subcategories. To address this issue, we propose a novel Content-aware Rectified Activation model, which enables this model to suppress the activation on salient regions while preserving their discrimination, and spread activation to adjacent non-salient regions, thus mining more diverse discriminative features for retrieving unseen subcategories. Specifically, we construct a content-aware rectified prototype (CARP) by perceiving semantics of salient regions. CARP acts as a channel-wise non-destructive activation upper bound and can be selectively used to suppress salient regions for obtaining the rectified features. Moreover, two regularizations are proposed: 1) a semantic coherency constraint that imposes a restriction on semantic coherency of CARP and salient regions, aiming at propagating the discriminative ability of salient regions to CARP, 2) a feature-navigated constraint to further guide the model to adaptively balance the discrimination power of rectified features and the suppression power of salient features. Experimental results on fine-grained and product retrieval benchmarks demonstrate that our method consistently outperforms the state-of-the-art methods.

Existing fine-grained image recognition works have attempted to dig into low-level details for emphasizing subtle discrepancies among sub-categories. However, a potential limitation of these methods is that they integrate the low-level details and high-level semantics directly, and neglect their content complementarity and spatial corresponding correlation. To handle this limitation, we propose an end-to-end Semantic-guided Information Alignment Network (SIA-Net) to dynamically pick out the low-level details under the guidance of accurate semantics to make selected details spatially corresponding to high-level semantics and complementary in content. Technically, SIA-Net consists of an Accurate Semantic Calibration (ASC) module for providing accurate semantics and a Discriminative Feature Alignment (DFA) module for aggregating low-level details and high-level semantics using accurate semantics generated by ASC. ASC learns the pixel-level feature shifting caused by convolutional operations, which is utilized for replacing the incorrectly highlighted semantics by shifting discriminative semantics or background features. After obtaining the accurate semantic features, DFA digs into the complementary details and simultaneously makes the selected details spatially corresponding via applying the guidance of accurate semantics to obtain the reassembly features. Finally, the reassembly features, which serve as discriminative cues, are used for more accurate discriminative region localization. Extensive experiments verify that our proposed method yields the best performance under the same settings with the most competitive approaches on CUB-birds, Stanford-Cars, and FGVC Aircraft datasets.