While significant progress has been made in single-view 3D human pose estimation, multi-view 3D human pose estimation remains challenging, particularly in terms of generalizing to new camera configurations. Existing attention-based transformers often struggle to accurately model the spatial arrangement of keypoints, especially in occluded scenarios. Additionally, they tend to overfit specific camera arrangements and visual scenes from training data, resulting in substantial performance drops in new settings. In this study, we introduce a novel Multi-View State Space Modeling framework, named MV-SSM, for robustly estimating 3D human keypoints. We explicitly model the joint spatial sequence at two distinct levels: the feature level from multi-view images and the person keypoint level. We propose a Projective State Space (PSS) block to learn a generalized representation of joint spatial arrangements using state space modeling. Moreover, we modify Mamba’s traditional scanning into an effective Grid Token-guided Bidirectional Scanning (GTBS), which is integral to the PSS block. Multiple experiments demonstrate that MV-SSM achieves strong generalization, outperforming state-of-the-art methods: $ + {\mathbf{10}}.{\mathbf{8}}$ on AP25 $\left( { + 24\% {\text{ }} \uparrow } \right)$on the challenging three-camera setting in CMU Panoptic, $ + {\mathbf{7}}.{\mathbf{0}}$ on AP25 $\left( { + 13\% {\text{ }} \uparrow } \right)$on varying camera arrangements, and $ + {\mathbf{15}}.{\mathbf{3}}$ PCP $\left( { + 38\% {\text{ }} \uparrow } \right)$ on Campus A1 in cross-dataset evaluations. Project Website: https://aviralchharia.github.io/MV-SSM.

3D Hand reconstruction from a single RGB image is challenging due to the articulated motion, self-occlusion, and interaction with objects. Existing SOTA methods employ attention-based transformers to learn the 3D hand pose and shape, yet they do not fully achieve robust and accurate performance, primarily due to inefficiently modeling spatial relations between joints. To address this problem, we propose a novel graph-guided Mamba framework, named Hamba, which bridges graph learning and state space modeling. Our core idea is to reformulate Mamba's scanning into graph-guided bidirectional scanning for 3D reconstruction using a few effective tokens. This enables us to efficiently learn the spatial relationships between joints for improving reconstruction performance. Specifically, we design a Graph-guided State Space (GSS) block that learns the graph-structured relations and spatial sequences of joints and uses 88.5\% fewer tokens than attention-based methods. Additionally, we integrate the state space features and the global features using a fusion module. By utilizing the GSS block and the fusion module, Hamba effectively leverages the graph-guided state space features and jointly considers global and local features to improve performance. Experiments on several benchmarks and in-the-wild tests demonstrate that Hamba significantly outperforms existing SOTAs, achieving the PA-MPVPE of 5.3mm and F@15mm of 0.992 on FreiHAND. At the time of this paper's acceptance, Hamba holds the top position, Rank 1, in two competition leaderboards on 3D hand reconstruction.

Aptamers are oligonucleotides or peptides with unique binding properties for specific target molecules, and they have shown great potential in diagnostics, therapeutics, and bio-sensing. However, the current in vitro SELEX-based method for discovering new target-selective aptamers is challenging, time-consuming, and often unsuccessful in finding high-affinity aptamers. Recently, in silico methods have gained immense attention. However, since labeled interaction-pair data collection is expensive and needs highly trained specialists, available data is sparse. Further, since acquiring positive-class samples is even more challenging, available datasets showcase high-class imbalance. This makes designing deep learning models incredibly challenging, as they require a sufficiently large training set and are biased towards the dominant class. Additionally, current models cannot be updated in real-time, and end-to-end re-training is necessary for each new aptamer-target interaction pair discovery. The present work is the first to address both these challenges. We present cAPTured, a novel fuzzy continual learning method for predicting aptamer-target protein interaction pairs in a continual learning environment. cAPTured continually updates its learned feature space on a non-stationary interaction-pair data stream. We performed extensive evaluation studies and experiments to establish the effectiveness of the proposed approach. cAPTured outperforms existing methods on the benchmark dataset by a significant margin.

The identification of B-cell epitopes that elicit an antigen-specific immune response is essential for a variety of immunodetection and immunotherapeutic applications, including the development of safe and high efficacy vaccines. Identifying diagnostically or therapeutically useful epitopes is a difficult, time-consuming, and resource-intensive procedure. In silico prediction of B-cell epitope has gained immense attention in recent years due to its low cost, fast results, and less labor-intensive method compared to NMR spectroscopy and 3D X-ray structural analysis of antibody-antigen complexes. However, one of the major problems that most established models confront is gathering huge volumes of data. Moreover, most models do not achieve high levels of accuracy. The current work is the first to propose the ‘Fuzzy’ approach to in silico B-cell epitope prediction. The effectiveness of the proposed approach is demonstrated on severely imbalanced and limited datasets through several experiments. The results show that using the proposed method enhances both accuracy and precision when compared to existing approaches. Further, the model is tested on the SARS-CoV-1 antigen-antibody PDB complex. The proposed approach outperforms state-of-the-art machine learning (ML) models trained on the same dataset. Results obtained indicate that applying the proposed method improves the prediction compared to the other approaches.