While visual reasoning for simple analogies has received significant attention, compositional visual relations (CVR) remain relatively unexplored due to their greater complexity. To solve CVR tasks, we propose Predictive Reasoning with Augmented Anomaly Contrastive Learning (PR-A$^{2}$CL), i.e., to identify an outlier image given three other images that follow the same compositional rules. To address the challenge of modelling abundant compositional rules, an Augmented Anomaly Contrastive Learning is designed to distil discriminative and generalizable features by maximizing similarity among normal instances while minimizing similarity between normal and anomalous outliers. More importantly, a predict-and-verify paradigm is introduced for rule-based reasoning, in which a series of Predictive Anomaly Reasoning Blocks (PARBs) iteratively leverage features from three out of the four images to predict those of the remaining one. Throughout the subsequent verification stage, the PARBs progressively pinpoint the specific discrepancies attributable to the underlying rules. Experimental results on SVRT, CVR and MC$^{2}$R datasets show that PR-A$^{2}$CL significantly outperforms state-of-the-art reasoning models.

Abstract visual reasoning (AVR) is a critical ability of humans, and it has been widely studied, but arithmetic visual reasoning, a unique task in AVR to reason over number sense, is less studied in the literature. To facilitate this research, we construct a Machine Number Reasoning (MNR) dataset to assess the model's ability in arithmetic visual reasoning over number sense and spatial layouts. To solve the MNR tasks, we propose a Dual-branch Arithmetic Regression Reasoning (DARR) framework, which includes an Intra-Image Arithmetic Regression Reasoning (IIARR) module and a Cross-Image Arithmetic Regression Reasoning (CIARR) module. The IIARR includes a set of Intra-Image Regression Blocks to identify the correct number orders and the underlying arithmetic rules within individual images, and an Order Gate to determine the correct number order. The CIARR establishes the arithmetic relations across different images through a `3-to-1' regressor and a set of `2-to-1' regressors, with a Selection Gate to select the most suitable `2-to-1' regressor and a gated fusion to combine the two kinds of regressors. Experiments on the MNR dataset show that the DARR outperforms state-of-the-art models for arithmetic visual reasoning.

Executing neural networks on resource-constrained embedded devices faces challenges. Efforts have been made at the application and system levels to reduce the execution cost. Among them, the early-exit networks reduce computational cost through intermediate exits, while Dynamic Voltage and Frequency Scaling (DVFS) offers system energy reduction. Existing works strive to unify early-exit and DVFS for combined benefits on both timing and energy flexibility, yet limitations exist: 1) varying time constraints that make different exit points become more, or less, important in terms of inference accuracy, are not taken care of, and 2) the optimal decisions of unifying DVFS and early-exit as a multi-objective optimization problem are not achieved due to the large configuration space. To address these challenges, we propose Dr2r, a reinforcement learning-based framework that jointly optimizes early-exit points and DVFS settings for continuous inference. In particular, Dr2r includes a cross-training mechanism that fine-tunes the early-exit network to accommodate dynamic time constraints and system conditions. Experimental results demonstrate that Dr2r achieves up to 22.03% energy reduction and 3.23% accuracy gain compared to contemporary techniques.

Most existing models for abstract visual reasoning perform poorly in compositional visual reasoning (CVR), due to complex nature of compositional rules and difficulties in distinguishing tiny rule differences between outliers and normal images. To tackle the challenges, we propose a Dual-Branch Compositional Reasoning (DBCR) model, exploiting both intra-cluster relations among the cluster of normal images and extra-cluster relations between normal images and outliers. Specifically, we design one branch of Intra-Cluster Regression Reasoning Blocks (ICR2Bs) to encapsulate common relations among normal images through hierarchical regressing reasoning, and the other branch of Contrastive Attention Reasoning Blocks (CARBs) to exploit extra-cluster differences between normal images and outliers through self-attention. Simultaneously minimizing the regression errors in ICR2Bs and maximizing the extra-cluster differences in CARBs help identify the correct cluster of normal images. Experimental results on two CVR datasets show that the proposed DBCR consistently outperforms state-of-the-art models. The code is available at https://github.com/He1mont/DBCR.

Abstract Visual Reasoning (AVR) entails discerning latent patterns in visual data and inferring underlying rules. Existing solutions often lack scalability and adaptability, as deep architectures tend to overfit training data, and static neural networks fail to dynamically capture diverse rules. To tackle the challenges, we propose a Dynamic and Scalable Reasoning Framework (DSRF) that greatly enhances the reasoning ability by widening the network instead of deepening it, and dynamically adjusting the reasoning network to better fit novel samples instead of a static network. Specifically, we design a Multi-View Reasoning Pyramid (MVRP) to capture complex rules through layered reasoning to focus features at each view on distinct combinations of attributes, widening the reasoning network to cover more attribute combinations analogous to complex reasoning rules. Additionally, we propose a Dynamic Domain-Contrast Prediction (DDCP) block to handle varying task-specific relationships dynamically by introducing a Gram matrix to model feature distributions, and a gate matrix to capture subtle domain differences between context and target features. Extensive experiments on six AVR tasks demonstrate DSRF’s superior performance, achieving state-of-the-art results under various settings. Code is available here: https://github.com/UNNCRoxLi/DSRF.