Hallucinate, Ground, Repeat: A Framework for Generalized Visual Relationship Detection

Understanding relationships between objects is central to visual intelligence, with applications in embodied AI, assistive systems, and scene understanding. Yet, most visual relationship detection (VRD) models rely on a fixed predicate set, limiting their generalization to novel interactions. A key challenge is the inability to visually ground semantically plausible, but unannotated, relationships hypothesized from external knowledge. This work introduces an iterative visual grounding framework that leverages large language models (LLMs) as structured relational priors. Inspired by expectation-maximization (EM), our method alternates between generating candidate scene graphs from detected objects using an LLM (expectation) and training a visual model to align these hypotheses with perceptual evidence (maximization). This process bootstraps relational understanding beyond annotated data and enables generalization to unseen predicates. Additionally, we introduce a new benchmark for open-world VRD on Visual Genome with 21 held-out predicates and evaluate under three settings: seen, unseen, and mixed. Our model outperforms LLM-only, few-shot, and debiased baselines, achieving mean recall (mR@50) of 15.9, 13.1, and 11.7 on predicate classification on these three sets. These results highlight the promise of grounded LLM priors for scalable open-world visual understanding.

A Probabilistic Jump-Diffusion Framework for Open-World Egocentric Activity Recognition

Open-world egocentric activity recognition poses a fundamental challenge due to its unconstrained nature, requiring models to infer unseen activities from an expansive, partially observed search space. We introduce ProbRes, a Probabilistic Residual search framework based on jump-diffusion that efficiently navigates this space by balancing prior-guided exploration with likelihood-driven exploitation. Our approach integrates structured commonsense priors to construct a semantically coherent search space, adaptively refines predictions using Vision-Language Models (VLMs) and employs a stochastic search mechanism to locate high-likelihood activity labels while minimizing exhaustive enumeration efficiently. We systematically evaluate ProbRes across multiple openness levels (L0--L3), demonstrating its adaptability to increasing search space complexity. In addition to achieving state-of-the-art performance on benchmark datasets (GTEA Gaze, GTEA Gaze+, EPIC-Kitchens, and Charades-Ego), we establish a clear taxonomy for open-world recognition, delineating the challenges and methodological advancements necessary for egocentric activity understanding.

ProbRes: Probabilistic Jump Diffusion for Open-World Egocentric Activity Recognition

Open-world egocentric activity recognition poses a fundamental challenge due to its unconstrained nature, requiring models to infer unseen activities from an expansive, partially observed search space. We introduce ProbRes, a Probabilistic Residual search framework based on jump-diffusion that efficiently navigates this space by balancing prior-guided exploration with likelihood-driven exploitation. Our approach integrates structured commonsense priors to construct a semantically coherent search space, adaptively refines predictions using Vision-Language Models (VLMs) and employs a stochastic search mechanism to locate high-likelihood activity labels while minimizing exhaustive enumeration efficiently. We systematically evaluate ProbRes across multiple openness levels (L0 - L3), demonstrating its adaptability to increasing search space complexity. In addition to achieving state-of-the-art performance on benchmark datasets (GTEA Gaze, GTEA Gaze+, EPIC-Kitchens, and Charades-Ego), we establish a clear taxonomy for open-world recognition, delineating the challenges and methodological advancements necessary for egocentric activity understanding. Our results highlight the importance of structured search strategies, paving the way for scalable and efficient open-world activity recognition.

Capturing Temporal Components for Time Series Classification

Analyzing sequential data is crucial in many domains, particularly due to the abundance of data collected from the Internet of Things paradigm. Time series classification, the task of categorizing sequential data, has gained prominence, with machine learning approaches demonstrating remarkable performance on public benchmark datasets. However, progress has primarily been in designing architectures for learning representations from raw data at fixed (or ideal) time scales, which can fail to generalize to longer sequences. This work introduces a \textit{compositional representation learning} approach trained on statistically coherent components extracted from sequential data. Based on a multi-scale change space, an unsupervised approach is proposed to segment the sequential data into chunks with similar statistical properties. A sequence-based encoder model is trained in a multi-task setting to learn compositional representations from these temporal components for time series classification. We demonstrate its effectiveness through extensive experiments on publicly available time series classification benchmarks. Evaluating the coherence of segmented components shows its competitive performance on the unsupervised segmentation task.

Self-supervised Multi-actor Social Activity Understanding in Streaming Videos

This work addresses the problem of Social Activity Recognition (SAR), a critical component in real-world tasks like surveillance and assistive robotics. Unlike traditional event understanding approaches, SAR necessitates modeling individual actors' appearance and motions and contextualizing them within their social interactions. Traditional action localization methods fall short due to their single-actor, single-action assumption. Previous SAR research has relied heavily on densely annotated data, but privacy concerns limit their applicability in real-world settings. In this work, we propose a self-supervised approach based on multi-actor predictive learning for SAR in streaming videos. Using a visual-semantic graph structure, we model social interactions, enabling relational reasoning for robust performance with minimal labeled data. The proposed framework achieves competitive performance on standard group activity recognition benchmarks. Evaluation on three publicly available action localization benchmarks demonstrates its generalizability to arbitrary action localization.

ALGO: Object-Grounded Visual Commonsense Reasoning for Open-World Egocentric Action Recognition

Learning to infer labels in an open world, i.e., in an environment where the target "labels" are unknown, is an important characteristic for achieving autonomy. Foundation models pre-trained on enormous amounts of data have shown remarkable generalization skills through prompting, particularly in zero-shot inference. However, their performance is restricted to the correctness of the target label's search space. In an open world, this target search space can be unknown or exceptionally large, which severely restricts the performance of such models. To tackle this challenging problem, we propose a neuro-symbolic framework called ALGO - Action Learning with Grounded Object recognition that uses symbolic knowledge stored in large-scale knowledge bases to infer activities in egocentric videos with limited supervision using two steps. First, we propose a neuro-symbolic prompting approach that uses object-centric vision-language models as a noisy oracle to ground objects in the video through evidence-based reasoning. Second, driven by prior commonsense knowledge, we discover plausible activities through an energy-based symbolic pattern theory framework and learn to ground knowledge-based action (verb) concepts in the video. Extensive experiments on four publicly available datasets (EPIC-Kitchens, GTEA Gaze, GTEA Gaze Plus) demonstrate its performance on open-world activity inference.

ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognition

Developing reliable computational frameworks for early parasite detection, particularly at the ova (or egg) stage is crucial for advancing healthcare and effectively managing potential public health crises. While deep learning has significantly assisted human workers in various tasks, its application and diagnostics has been constrained by the need for extensive datasets. The ability to learn from an extremely scarce training dataset, i.e., when fewer than 5 examples per class are present, is essential for scaling deep learning models in biomedical applications where large-scale data collection and annotation can be expensive or not possible (in case of novel or unknown infectious agents). In this study, we introduce ProtoKD, one of the first approaches to tackle the problem of multi-class parasitic ova recognition using extremely scarce data. Combining the principles of prototypical networks and self-distillation, we can learn robust representations from only one sample per class. Furthermore, we establish a new benchmark to drive research in this critical direction and validate that the proposed ProtoKD framework achieves state-of-the-art performance. Additionally, we evaluate the framework's generalizability to other downstream tasks by assessing its performance on a large-scale taxonomic profiling task based on metagenomes sequenced from real-world clinical data.

Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis

This paper focuses on the statistical analysis of shapes of data objects called shape graphs, a set of nodes connected by articulated curves with arbitrary shapes. A critical need here is a constrained registration of points (nodes to nodes, edges to edges) across objects. This, in turn, requires optimization over the permutation group, made challenging by differences in nodes (in terms of numbers, locations) and edges (in terms of shapes, placements, and sizes) across objects. This paper tackles this registration problem using a novel neural-network architecture and involves an unsupervised loss function developed using the elastic shape metric for curves. This architecture results in (1) state-of-the-art matching performance and (2) an order of magnitude reduction in the computational cost relative to baseline approaches. We demonstrate the effectiveness of the proposed approach using both simulated data and real-world 2D and 3D shape graphs. Code and data will be made publicly available after review to foster research.

Discovering Novel Actions in an Open World with Object-Grounded Visual Commonsense Reasoning

Learning to infer labels in an open world, i.e., in an environment where the target ``labels'' are unknown, is an important characteristic for achieving autonomy. Foundation models pre-trained on enormous amounts of data have shown remarkable generalization skills through prompting, particularly in zero-shot inference. However, their performance is restricted to the correctness of the target label's search space. In an open world where these labels are unknown, the search space can be exceptionally large. It can require reasoning over several combinations of elementary concepts to arrive at an inference, which severely restricts the performance of such models. To tackle this challenging problem, we propose a neuro-symbolic framework called ALGO - novel Action Learning with Grounded Object recognition that can use symbolic knowledge stored in large-scale knowledge bases to infer activities (verb-noun combinations) in egocentric videos with limited supervision using two steps. First, we propose a novel neuro-symbolic prompting approach that uses object-centric vision-language foundation models as a noisy oracle to ground objects in the video through evidence-based reasoning. Second, driven by prior commonsense knowledge, we discover plausible activities through an energy-based symbolic pattern theory framework and learn to ground knowledge-based action (verb) concepts in the video. Extensive experiments on two publicly available datasets (GTEA Gaze and GTEA Gaze Plus) demonstrate its performance on open-world activity inference and its generalization to unseen actions in an unknown search space. We show that ALGO can be extended to zero-shot settings and demonstrate its competitive performance to multimodal foundation models.

Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning

Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources at a reduced cost. Given the sheer magnitude of the collected data and the significant challenges offered by the presence of highly similar genomic structure across species, there is a need for robust, scalable analysis platforms to extract actionable knowledge such as the presence of potentially zoonotic pathogens. The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis. In this work, we propose MG2Vec, a deep learning-based solution that uses the transformer network as its backbone, to learn robust features from raw metagenome sequences for downstream biomedical tasks such as targeted and generalized pathogen detection. Extensive experiments on four increasingly challenging, yet realistic diagnostic settings, show that the proposed approach can help detect pathogens from uncurated, real-world clinical samples with minimal human supervision in the form of labels. Further, we demonstrate that the learned representations can generalize to completely unrelated pathogens across diseases and species for large-scale metagenome analysis. We provide a comprehensive evaluation of a novel representation learning framework for metagenome-based disease diagnostics with deep learning and provide a way forward for extracting and using robust vector representations from low-cost next generation sequencing to develop generalizable diagnostic tools.