Vineeth N Balasubramanian

Most of our papers have the code links in them. Please check them out if they are useful to you.

Datasets (Selected)

Mind's Eye is available here. The dataset and the benchmarked results are described in: “Rohit Sinha, Aditya S Kanade, Sai Srinivas K, Vineeth N Balasubramanian, Tanuja Ganu, Mind’s Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs, Proceedings of the Association of Computational Linguistics (ACL’26), Jul 2026”

CANDLE (Causal ANalysis in DisentangLed rEpresentations) is available here. The dataset and the corresponding work are described in: “A Gowtham Reddy, Benin Godfrey, V. Balasubramanian, On Causally Disentangled Representations, Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI’22), Feb 2022”

DaiSEE: Dataset for Affective States in E-Environments is available here. The dataset and the corresponding work are described in: “A Gupta, A DCunha, K Awasthi, V Balasubramanian, DAiSEE: Towards User Engagement Recognition in the Wild, arXiv preprint: arXiv:1609.01885” and “A Kamath, A Biswas, V. Balasubramanian, A Crowdsourced Approach to Student Engagement Recognition in e-Learning Environments, IEEE Winter Conference on Applications of Computer Vision (WACV’16)”

S2M2: Manifold Mixup for Few-shot Learning: A few-shot classification algorithm: Github code

ACE (Average Causal Effect): Method to compute causal attributions of input variables in a neural network classification model: Github code

A Little Fog for a Large Turn: Method to generate foggy images for evaluation of autonomous navigation models: Project Website

MASON: A Model AgnoStic ObjectNess Framework: a simple method to detect objects in an image (at a pixel level) with only an image-level classification model: Github code