Manvik Pasula

Sahil Shah, S P Sharan, Harsh Goel et al.

While vision-language models (VLMs) excel at tasks involving single images or short videos, they still struggle with Long Video Question Answering (LVQA) due to its demand for complex multi-step temporal reasoning. Vanilla approaches, which simply sample frames uniformly and feed them to a VLM along with the question, incur significant token overhead. This forces aggressive downsampling of long videos, causing models to miss fine-grained visual structure, subtle event transitions, and key temporal cues. Recent works attempt to overcome these limitations through heuristic approaches; however, they lack explicit mechanisms for encoding temporal relationships and fail to provide any formal guarantees that the sampled context actually encodes the compositional or causal logic required by the question. To address these foundational gaps, we introduce NeuS-QA, a training-free, plug-and-play neuro-symbolic pipeline for LVQA. NeuS-QA first translates a natural language question into a logic specification that models the temporal relationship between frame-level events. Next, we construct a video automaton to model the video's frame-by-frame event progression, and finally employ model checking to compare the automaton against the specification to identify all video segments that satisfy the question's logical requirements. Only these logic-verified segments are submitted to the VLM, thus improving interpretability, reducing hallucinations, and enabling compositional reasoning without modifying or fine-tuning the model. Experiments on the LongVideoBench and CinePile LVQA benchmarks show that NeuS-QA significantly improves performance by over 10%, particularly on questions involving event ordering, causality, and multi-step reasoning. We open-source our code at https://utaustin-swarmlab.github.io/NeuS-QA/.

Manvik Pasula, Pramit Saha

This paper introduces a simple yet effective strategy for exercise classification and muscle group activation prediction (MGAP). These tasks have significant implications for personal fitness, facilitating more affordable, accessible, safer, and simpler exercise routines. This is particularly relevant for novices and individuals with disabilities. Previous research in the field is mostly dominated by the reliance on mounted sensors and a limited scope of exercises, reducing practicality for everyday use. Furthermore, existing MGAP methodologies suffer from a similar dependency on sensors and a restricted range of muscle groups, often excluding strength training exercises, which are pivotal for a comprehensive fitness regimen. Addressing these limitations, our research employs a video-based deep learning framework that encompasses a broad spectrum of exercises and muscle groups, including those vital for strength training. Utilizing the "Workout/Exercises Video" dataset, our approach integrates the X3D and SlowFast video activity recognition models in an effective way to enhance exercise classification and MGAP performance. Our findings demonstrate that this hybrid method, obtained via weighted ensemble, outperforms existing baseline models in accuracy. Pretrained models play a crucial role in enhancing overall performance, with optimal channel reduction values for the SlowFast model identified near 10. Through an ablation study that explores fine-tuning, we further elucidate the interrelation between the two tasks. Our composite model, a weighted-average ensemble of X3D and SlowFast, sets a new benchmark in both exercise classification and MGAP across all evaluated categories, offering a robust solution to the limitations of previous approaches.