Matiur Rahman Minar

Matiur Rahman Minar, Seunghun Oh, GangHyeon Jeong et al.

Autoregressive video diffusion models enable streaming generation but often degrade over long rollouts: static scene layouts drift, while mechanisms that improve spatial stability tend to suppress motion, causing natural flows such as water, fire, or smoke to stagnate. We study this stability-motion trade-off in fixed-camera long-horizon nature video generation, where the two failure modes can be more clearly separated than in moving-camera settings. We propose Steady-Forcing, a memory and training framework combining a persistent visual anchor (V-Sink), an exponential moving-average motion memory (EMA-Sink), block-relative temporal encoding, periodic cache purification, and distillation from a Wan2.1-14B teacher with motion-rewarded priors under task-focused configurations. Together, these components are designed to preserve background identity while sustaining visually plausible fluid dynamics over multi-minute autoregressive rollouts. Evaluations across seven baselines show that Steady-Forcing improves long horizon background consistency and imaging quality, while a blind user study indicates stronger perceived stability and motion continuity. The benchmark evaluation further suggest that generic VBench aggregate scores under-penalize fixed-camera artifacts as well as rewarding drift-induced optical flow as Dynamic Degree while not directly penalizing texture hardening or flow stagnation - motivating future task-specific benchmarks for static-camera nature-flow evaluation. Project page: https://minar09.github.io/steadyforcing/

Jibon Naher, Matiur Rahman Minar

Social Networking Site (SNS) is a great innovation of modern times. Facebook, Twitter etc. have become an everyday part of peoples' life. Among all SNSs, Facebook is the most popular social network all over the world. Bangladesh is no exception. People of Bangladesh use Facebook for social communication, online shopping, business, knowledge and experience sharing etc. As well as the various uses of SNSs, people sometimes find themselves involved in real life violence, provoked by some social media posts or activities. In this paper, we discussed some case studies in which real life violence is originated based on Facebook activities in Bangladesh. Facebook was used in these incidents intentionally or unintentionally mostly as a tool to trigger hatred and violence. We analyzed and discussed the real-world consequences of these virtual activities in social media. Lastly, we recommended possible future measurements to prevent such violence.

Matiur Rahman Minar, Jibon Naher

Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques. In recent years, the world has seen many major breakthroughs in this field. Since deep learning is evolving at a huge speed, its kind of hard to keep track of the regular advances especially for new researchers. In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years.