We present our submissions to the WSLP 2026 Isolated Sign Language Recognition shared task. The task requires predicting the correct English gloss from a short Indian Sign Language video.

Our approach first establishes a strong primary prediction using vision features and identifies uncertain cases using model disagreement and prediction confidence. Rather than allowing all available experts to influence every prediction, we selectively invoke auxiliary models for difficult cases and incorporate their evidence through constrained late-fusion strategies. This enables complementary appearance, pose, and vision-language information to be used when the primary prediction is uncertain while preserving reliable predictions for confident cases. We introduce a disagreement-gated Qwen2.5-VL model as an auxiliary decision-making module and further incorporate complementary appearance and pose experts through several constrained fusion strategies.

The final system combines multiple fusion variants using reciprocal rank fusion and achieves 66.16% Top-1 accuracy, 72.43% Top-5 accuracy, and 73.19% Top-10 accuracy on the blind test set

In this paper, we designed a Speech-to-Text Translation (ST) system to translate English into Hindi, Bengali, and Tamil, and vice versa. We explored both cascaded and End-to-End (E2E) approaches as part of the IWSLT 2025 Indic shared task.

This study investigates the biometric capabilities of ChatGPT-4o, evaluating its performance on age estimation, gender classification, and identity verification across two challenging datasets: the ITWCC (images of children aged 6–17) and a pediatric surgery dataset. By leveraging tailored prompts that bypass safety filters, ChatGPT-4o outperformed conventional CNN-based models such as DeepFace, achieving higher accuracy and offering interpretable, rationale-rich outputs. Specifically, it delivered a mean absolute error of 1.8 years in age estimation, 96–100% gender classification accuracy, and over 85% identity continuity recognition, even across surgical transformations. The findings demonstrate the potential of multimodal LLMs to complement or exceed traditional approaches in face analysis tasks, though the study notes the importance of expanding demographic diversity, refining prompt strategies, and ensuring fairness and robustness in real-world settings.