Deep unrolling networks have rapidly gained popularity in image reconstruction by integrating data-driven networks with iterative model-driven reconstruction algorithms. Technically, existing unrolling networks could easily break down and produce sub-optimal results due to inadequate iterations and limited receptive fields. Another challenge is that mainstream algorithms are established in isolation from the physical system and confined to digital realm. This paper proposes a novel implicit unrolling Transformer architecture, dubbed TranIU-Net, that extracts local contents and non-local dependencies to assist iterative learning, and forms indicative imaging mechanism to guide system design. Concretely, TranIU-Net unrolls the proximal gradient algorithm into a trainable network with structural interpretability. Using only constant memory cost, the implicit mapping is analytically built to guarantee the convergence through the fixed-point at unlimited depth. To consider intrinsic correlation and sparsity in reconstructed images, an embedded Transformer module is developed to capture multi-scale information with hybrid receptive fields and assign self-aware granularity with learnt significance estimator, making it an efficient backbone for implicit unrolling network. Additionally, with adaptive and flexible architecture, TranIU-Net explores a new imaging mechanism by indicating structure design and measurement condition, bridging the gap between algorithm and imaging system to facilitate reconstruction quality. Extensive numerical simulations and practical experiments of electrical tomography reconstruction demonstrate that the proposed TranIU-Net outperforms state-of-the-art alternatives in different scenarios from both quantitative and qualitative perspectives.

In few-shot fine-grained recognition (FS-FGR) tasks, the main challenge is to distinguish novel categories with high intra-class variations and low inter-class differences given scarce training data. Existing studies explore discriminative features through a compact network to avoid overfitting, while they achieve marginal performance gain owing to the limited representation capability. Motivated by the significant progress of the vision foundation model, we introduce it to describe visual attributes and boost the performance of the compact feature extractor. A few-shot fine-grained recognition method with Variational Feature Imitation Conditioned on Visual Descriptions, VFI-CVD for short, has been proposed in this paper. It simultaneously exploits the pre-trained knowledge from a vision foundation model and the expert knowledge mined by a feature extractor. Specifically, the intra-class variations shared across object categories are encoded into a common distribution thus we can augment features by sampling latent variables. To enhance the learning of intra-class variations, a condition exchange strategy (CES) is put forward to interact the knowledge between samples through feature cross-imitation. In the inference stage, the learned knowledge is further integrated through the joint prediction of visual descriptions and cross-imitated features. Comprehensive experimental results on four fine-grained benchmark datasets show that the proposed VFI-CVD achieves state-of-the-art performance, e.g., 90.37% under the 5-way 1-shot setting on CUB-200-2011. It surpasses existing methods by a large margin, especially in the challenging 30-way recognition tasks and cross-domain evaluation. The source code is publicly available:https://github.com/Lx-zjwf/VFI-CVD.

Weakly supervised object localization aims to localize objects of interest by using only image-level labels. Existing methods generally segment activation map by threshold to obtain mask and generate bounding box. However, the activation map is locally inconsistent, i.e., similar neighboring pixels of the same object are not equally activated, which leads to the blurred boundary issue: the localization result is sensitive to the threshold, and the mask obtained directly from the activation map loses the fine contours of the object, making it difficult to obtain a tight bounding box. In this paper, we introduce the Local Consistency Aware Re-prediction (LCAR) framework, which aims to recover the complete fine object mask from locally inconsistent activation map and hence obtain a tight bounding box. To this end, we propose the self-guided re-prediction module (SGRM), which employs a novel superpixel aggregation network to replace the post-processing of threshold segmentation. In order to derive more reliable pseudo label from the activation map to supervise the SGRM, we further design an affinity refinement module (ARM) that utilizes the original image feature to better align the activation map with the image appearance, and design a self-distillation CAM (SD-CAM) to alleviate the locator dependence on saliency. Experiments demonstrate that our LCAR outperforms the state-of-the-art on both the CUB-200-2011 and ILSVRC datasets, achieving 95.89% and 70.72% of GT-Know localization accuracy, respectively.