Tootfinder

An Empirical Study on How Video-LLMs Answer Video Questions
Chenhui Gou, Ziyu Ma, Zicheng Duan, Haoyu He, Feng Chen, Akide Liu, Bohan Zhuang, Jianfei Cai, Hamid Rezatofighi
https://arxiv.org/abs/2508.15360

An Empirical Study on How Video-LLMs Answer Video Questions
Taking advantage of large-scale data and pretrained language models, Video Large Language Models (Video-LLMs) have shown strong capabilities in answering video questions. However, most existing efforts focus on improving performance, with limited attention to understanding their internal mechanisms. This paper aims to bridge this gap through a systematic empirical study. To interpret existing VideoLLMs, we adopt attention knockouts as our primary analytical tool and design three variants: Video…

StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding
Yanlai Yang, Zhuokai Zhao, Satya Narayan Shukla, Aashu Singh, Shlok Kumar Mishra, Lizhu Zhang, Mengye Ren
https://arxiv.org/abs/2508.15717

StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding
Multimodal large language models (MLLMs) have made significant progress in visual-language reasoning, but their ability to efficiently handle long videos remains limited. Despite recent advances in long-context MLLMs, storing and attending to the key-value (KV) cache for long visual contexts incurs substantial memory and computational overhead. Existing visual compression methods require either encoding the entire visual context before compression or having access to the questions in advance, w…

Waver: Wave Your Way to Lifelike Video Generation
Yifu Zhang, Hao Yang, Yuqi Zhang, Yifei Hu, Fengda Zhu, Chuang Lin, Xiaofeng Mei, Yi Jiang, Zehuan Yuan, Bingyue Peng
https://arxiv.org/abs/2508.15761 …

Waver: Wave Your Way to Lifelike Video Generation
We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training c…

Im #Windpark #Elster werden ältere #Windräder zurückgebaut und durch leistungsstärkere ersetzt.
Durch das #Repowering

Windpark-Umbau in Elster - weniger neue Windräder mit mehr Power | MDR.DE
Im Windpark Elster werden alte Windräder abgebaut und durch stärkere ersetzt. So wird mit weniger Fläche deutlich mehr Strom erzeugt. Der Umbau, der 2023 begann, soll im August abgeschlossen sein.

Kickstarted a credit-card-sized dice replacement.
For IRL D&D it was fine, but dials spin forever. Made dice tray pointless, while less satisfying. Even though I choose yellow text on black, hard for me to see in mood-lit basement on table
For Shadowrun, 6D6 is embarrassingly insufficient, given I regularly roll 14D6. The VTT roller is far better (since it does the math on pools and successes).
But they’re sturdy and hefty! Which means my mini metal dice are lighter an…

Repeating Words for Video-Language Retrieval with Coarse-to-Fine Objectives
Haoyu Zhao, Jiaxi Gu, Shicong Wang, Xing Zhang, Hang Xu, Zuxuan Wu, Yu-Gang Jiang
https://arxiv.org/abs/2508.14812

Repeating Words for Video-Language Retrieval with Coarse-to-Fine Objectives
The explosive growth of video streaming presents challenges in achieving high accuracy and low training costs for video-language retrieval. However, existing methods rely on large-scale pre-training to improve video retrieval performance, resulting in significant computational demands. Additionally, the fine-grained information in videos and texts remains underexplored. To alleviate these problems, we propose a novel framework to learn fine-grained features for better alignment and introduce an…

DashCam Video: A complementary low-cost data stream for on-demand forest-infrastructure system monitoring
Durga Joshi (Department of Natural Resources and the Environment, Eversource Energy Center, University of Connecticut, Storrs, CT, USA), Chandi Witharana (Department of Natural Resources and the Environment, Eversource Energy Center, University of Connecticut, Storrs, CT, USA), Robert Fahey (Department of Natural Resources and the Environment, Eversource Energy Center, University o…

DashCam Video: A complementary low-cost data stream for on-demand forest-infrastructure system monitoring
Our study introduces a novel, low-cost, and reproducible framework for real-time, object-level structural assessment and geolocation of roadside vegetation and infrastructure with commonly available but underutilized dashboard camera (dashcam) video data. We developed an end-to-end pipeline that combines monocular depth estimation, depth error correction, and geometric triangulation to generate accurate spatial and structural data from street-level video streams from vehicle-mounted dashcams. D…

SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos
Pengzhi Zhong, Xinzhe Wang, Dan Zeng, Qihua Zhou, Feixiang He, Shuiwang Li
https://arxiv.org/abs/2508.14607

SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos
Brain-inspired Spiking Neural Networks (SNNs) exhibit significant potential for low-power computation, yet their application in visual tasks remains largely confined to image classification, object detection, and event-based tracking. In contrast, real-world vision systems still widely use conventional RGB video streams, where the potential of directly-trained SNNs for complex temporal tasks such as multi-object tracking (MOT) remains underexplored. To address this challenge, we propose SMTrack…

Taming Diffusion Transformer for Real-Time Mobile Video Generation
Yushu Wu, Yanyu Li, Anil Kag, Ivan Skorokhodov, Willi Menapace, Ke Ma, Arpit Sahni, Ju Hu, Aliaksandr Siarohin, Dhritiman Sagar, Yanzhi Wang, Sergey Tulyakov
https://arxiv.org/abs/2507.13343

Taming Diffusion Transformer for Real-Time Mobile Video Generation
Diffusion Transformers (DiT) have shown strong performance in video generation tasks, but their high computational cost makes them impractical for resource-constrained devices like smartphones, and real-time generation is even more challenging. In this work, we propose a series of novel optimizations to significantly accelerate video generation and enable real-time performance on mobile platforms. First, we employ a highly compressed variational autoencoder (VAE) to reduce the dimensionality of…

Open-ended Hierarchical Streaming Video Understanding with Vision Language Models
Hyolim Kang, Yunsu Park, Youngbeom Yoo, Yeeun Choi, Seon Joo Kim
https://arxiv.org/abs/2509.12145

Open-ended Hierarchical Streaming Video Understanding with Vision Language Models
We introduce Hierarchical Streaming Video Understanding, a task that combines online temporal action localization with free-form description generation. Given the scarcity of datasets with hierarchical and fine-grained temporal annotations, we demonstrate that LLMs can effectively group atomic actions into higher-level events, enriching existing datasets. We then propose OpenHOUSE (Open-ended Hierarchical Online Understanding System for Events), which extends streaming action perception beyond …