Vineeth N. Balasubramanian

Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement

Uma Ranjan, Kunal Tilaganji, Aditya Koul +9

Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety concerns in medical applications. Allowing models to abstain when uncertain im…

Real-Time Source-Free Object Detection

Sairam VCR, Varun Gopal, Poornima Jain +2

Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detec…

Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning

Deepika SN Vemuri, Sayanta Adhikari, Ankit Saha +2

Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this by representing classes thr…

Swift Sampling: Selecting Temporal Surprises via Taylor Series

Dahye Kim, Bhuvan Sachdeva, Karan Uppal +3

While most frames in long-form video are redundant, the critical information resides in temporal surprises: moments where the actual visual features deviate from their predicted ev…

A Nash Equilibrium Framework For Training-Free Multimodal Step Verification

Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3

Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations.…

Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs

Sai Srinivas Kancheti, Aditya Sanjiv Kanade, Vineeth N. Balasubramanian +1

Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. However, we show that this paradig…

Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs

Rohit Sinha, Aditya Kanade, Sai Srinivas Kancheti +2

Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and visuospatial reasoning remains…

Faithful GRPO: Improving Visual Spatial Reasoning in Multimodal Language Models via Constrained Policy Optimization

Sai Srinivas Kancheti, Aditya Kanade, Rohit Sinha +2

Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR) show improved accuracy on visual reasoning benchmarks. However, we observe tha…

$⊘$ Source Models Leak What They Shouldn't $↛$ : Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization

Arnav Devalapally, Poornima Jain, Kartik Srinivas +1

The increasing adaptation of vision models across domains, such as satellite imagery and medical scans, has raised an emerging privacy risk: models may inadvertently retain and lea…

Source-Free Domain Adaptation by Optimizing Batch-Wise Cosine Similarity

Harsharaj Pathak, Vineeth N Balasubramanian

Source-Free Domain Adaptation (SFDA) is an emerging area of research that aims to adapt a model trained on a labeled source domain to an unlabeled target domain without accessing t…

iSHIFT: Lightweight Slow-Fast GUI Agent with Adaptive Perception

Sarthak Mehrotra, Sairam V C Rebbapragada, Mani Hemanth Reddy Bonthu +1

Multimodal Large Language Models (MLLMs) show strong potential for interpreting and interacting with complex, pixel-rich Graphical User Interface (GUI) environments. However, build…

LogicCBMs: Logic-Enhanced Concept-Based Learning

Deepika SN Vemuri, Gautham Bellamkonda, Aditya Pola +1

Concept Bottleneck Models (CBMs) provide a basis for semantic abstractions within a neural network architecture. Such models have primarily been seen through the lens of interpreta…

Foundation Model Priors Enhance Object Focus in Feature Space for Source-Free Object Detection

Sairam VCR, Rishabh Lalla, Aveen Dayal +4

Current state-of-the-art approaches in Source-Free Object Detection (SFOD) typically rely on Mean-Teacher self-labeling. However, domain shift often reduces the detector's ability…

Understanding Task Transfer in Vision-Language Models

Bhuvan Sachdeva, Karan Uppal, Abhinav Java +1

Vision-Language Models (VLMs) perform well on multimodal benchmarks but lag behind humans and specialized models on visual perception tasks like depth estimation or object counting…

Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models

Pushkar Shukla, Aditya Chinchure, Emily Diana +5

The biases exhibited by text-to-image (TTI) models are often treated as independent, though in reality, they may be deeply interrelated. Addressing bias along one dimension - such…

Efficient Vocabulary-Free Fine-Grained Visual Recognition in the Age of Multimodal LLMs

Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy +1

Fine-grained Visual Recognition (FGVR) involves distinguishing between visually similar categories, which is inherently challenging due to subtle inter-class differences and the ne…

POET: Prompt Offset Tuning for Continual Human Action Adaptation

Prachi Garg, Joseph K J, Vineeth N Balasubramanian +6

As extended reality (XR) is redefining how users interact with computing devices, research in human action recognition is gaining prominence. Typically, models deployed on immersiv…

BiasConnect: Investigating Bias Interactions in Text-to-Image Models

Pushkar Shukla, Aditya Chinchure, Emily Diana +5

The biases exhibited by Text-to-Image (TTI) models are often treated as if they are independent, but in reality, they may be deeply interrelated. Addressing bias along one dimensio…

Grounding Descriptions in Images informs Zero-Shot Visual Recognition

Shaunak Halbe, Junjiao Tian, K J Joseph +4

Vision-language models (VLMs) like CLIP have been cherished for their ability to perform zero-shot visual recognition on open-vocabulary concepts. This is achieved by selecting the…

On Evaluation of Vision Datasets and Models using Human Competency Frameworks

Rahul Ramachandran, Tejal Kulkarni, Charchit Sharma +2

Evaluating models and datasets in computer vision remains a challenging task, with most leaderboards relying solely on accuracy. While accuracy is a popular metric for model evalua…

Can Better Text Semantics in Prompt Tuning Improve VLM Generalization?

Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy +1

Going beyond mere fine-tuning of vision-language models (VLMs), learnable prompt tuning has emerged as a promising, resource-efficient alternative. Despite their potential, effecti…