3 papers
cs.CL2026
Evaluating Open-Weight LLMs for Turkish Domain Documents Under Retrieval and Hardware Constraints
Imtiaz Ul Hassan, Öykü Akbulut, Onur Kaya +4
Most Turkish-capable large language models (LLMs) are evaluated using general-purpose benchmarks rather than long, structurally complex domain documents. This paper evaluates five…
cs.CV2026
Fine-Grained Action Recognition with Cross-Attentive Latent Sparse Experts
Imtiaz Ul Hassan, Tasweer Ahmad, Nik Bessis +1
Fine-grained human action recognition (FHAR) must distinguish visually similar actions that differ mainly in body configuration, timing, or local appearance. RGB representations re…
cs.CV2026
TAG-Head: Time-Aligned Graph Head for Plug-and-Play Fine-grained Action Recognition
Imtiaz Ul Hassan, Nik Bessis, Ardhendu Behera
Fine-grained human action recognition (FHAR) is challenging because visually similar actions differ by subtle spatio-temporal cues. Many recent systems enhance discriminability wit…