5 citations · 11 across the 28 of their papers we have counts for
34 papers
Class Geometry as Supervision for Sample-Efficient Open-World Detection
Akash Rao, Zhou Chen, Revanth Reddy Palem +3
Open-world object detection requires models to recognize known categories, reject unfamiliar objects, and incorporate new classes over time. This is especially challenging in scarc…
FUSE: Active Functional Affordance Grounding through Adaptive Semantic-Geometric Evidence Acquisition
Zhou Chen, Sathyanarayanan N. Aakur
Embodied agents must often identify and interact with objects based on their function rather than their identity, requiring them to actively acquire observations that reveal discri…
CVT-Bench: Probing Spatial-State Integrity through Counterfactual Viewpoint Transformations
Shanmukha Vellamcheti, Uday Kiran Kothapalli, Disharee Bhowmick +1
Multimodal large language models (MLLMs) perform strongly on isolated spatial tasks, but whether their predictions remain persistent and mutually coherent across viewpoints and com…
CRAFT-E: A Neuro-Symbolic Framework for Embodied Affordance Grounding
Zhou Chen, Joe Lin, Carson Bulgin +1
Assistive robots operating in unstructured environments must understand not only what objects are, but what they can be used for. This requires grounding language-based action quer…
Generalized Event Partonomy Inference with Structured Hierarchical Predictive Learning
Zhou Chen, Joe Lin, Sathyanarayanan N. Aakur\\
Humans naturally perceive continuous experience as a hierarchy of temporally nested events, fine-grained actions embedded within coarser routines. Replicating this structure in com…
STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Disharee Bhowmick, Ranjith Ramanathan, Sathyanarayanan N. Aakur
Time series data often contain latent temporal structure, transitions between locally stationary regimes, repeated motifs, and bursts of variability, that are rarely leveraged in s…
FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection
Shubham Trehan, Udhav Ramachandran, Akash Rao +2
Object detection in biomedical settings is fundamentally constrained by the scarcity of labeled data and the frequent emergence of novel or rare categories. We present FSP-DETR, a…
CRAFT: A Neuro-Symbolic Framework for Visual Functional Affordance Grounding
Zhou Chen, Joe Lin, Sathyanarayanan N. Aakur
We introduce CRAFT, a neuro-symbolic framework for interpretable affordance grounding, which identifies the objects in a scene that enable a given action (e.g., "cut"). CRAFT integ…
EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization
Zhou Chen, Sanjoy Kundu, Harsimran S. Baweja +1
Active event perception, the ability to dynamically detect, track, and summarize events in real time, is essential for embodied intelligence in tasks such as human-AI collaboration…
Hallucinate, Ground, Repeat: A Framework for Generalized Visual Relationship Detection
Shanmukha Vellamcheti, Sanjoy Kundu, Sathyanarayanan N. Aakur
Understanding relationships between objects is central to visual intelligence, with applications in embodied AI, assistive systems, and scene understanding. Yet, most visual relati…
A Probabilistic Jump-Diffusion Framework for Open-World Egocentric Activity Recognition
Sanjoy Kundu, Shanmukha Vellamcheti, Sathyanarayanan N. Aakur
Open-world egocentric activity recognition poses a fundamental challenge due to its unconstrained nature, requiring models to infer unseen activities from an expansive, partially o…
ProbRes: Probabilistic Jump Diffusion for Open-World Egocentric Activity Recognition
Sanjoy Kundu, Shanmukha Vellamcheti, Sathyanarayanan N. Aakur
Open-world egocentric activity recognition poses a fundamental challenge due to its unconstrained nature, requiring models to infer unseen activities from an expansive, partially o…
Self-supervised Multi-actor Social Activity Understanding in Streaming Videos
Shubham Trehan, Sathyanarayanan N. Aakur
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 un…
Capturing Temporal Components for Time Series Classification
Venkata Ragavendra Vavilthota, Ranjith Ramanathan, Sathyanarayanan N. Aakur
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…
ALGO: Object-Grounded Visual Commonsense Reasoning for Open-World Egocentric Action Recognition
Sanjoy Kundu, Shubham Trehan, Sathyanarayanan N. Aakur
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 p…
TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification
Sathyanarayanan Aakur, Vishalini R. Laguduva, Priyadharsini Ramamurthy +1
A species' genetic code or genome encodes valuable evolutionary, biological, and phylogenetic information that aids in species recognition, taxonomic classification, and understand…
ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognition
Shubham Trehan, Udhav Ramachandran, Ruth Scimeca +1
Developing reliable computational frameworks for early parasite detection, particularly at the ova (or egg) stage is crucial for advancing healthcare and effectively managing poten…
Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis
Shenyuan Liang, Mauricio Pamplona Segundo, Sathyanarayanan N. Aakur +2
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…
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning
Sanjoy Kundu, Shubham Trehan, Sathyanarayanan N. Aakur
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…
Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning
Sai Narayanan, Sathyanarayanan N. Aakur, Priyadharsini Ramamurthy +3
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 a…
Iterative Scene Graph Generation with Generative Transformers
Sanjoy Kundu, Sathyanarayanan N. Aakur
Scene graphs provide a rich, structured representation of a scene by encoding the entities (objects) and their spatial relationships in a graphical format. This representation has…
A Rich Recipe Representation as Plan to Support Expressive Multi Modal Queries on Recipe Content and Preparation Process
Vishal Pallagani, Priyadharsini Ramamurthy, Vedant Khandelwal +4
Food is not only a basic human necessity but also a key factor driving a society's health and economic well-being. As a result, the cooking domain is a popular use-case to demonstr…
Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis
Sathyanarayanan N. Aakur, Vineela Indla, Vennela Indla +4
Advances in next-generation metagenome sequencing have the potential to revolutionize the point-of-care diagnosis of novel pathogen infections, which could help prevent potential w…
Towards Active Vision for Action Localization with Reactive Control and Predictive Learning
Shubham Trehan, Sathyanarayanan N. Aakur
Visual event perception tasks such as action localization have primarily focused on supervised learning settings under a static observer, i.e., the camera is static and cannot be c…