The rapid development of multimodal large-language models (MLLMs) has significantly expanded the scope of visual language reasoning, enabling unified systems to interpret and describe complex visual content. However, applying these models to long-video understanding remains computationally intensive. Dense frame encoding generates excessive visual tokens, leading to high memory consumption, redundant computation, and limited scalability in real-world applications. This inefficiency highlights a key limitation of the traditional process-then-reason paradigm, which analyzes visual streams exhaustively before semantic reasoning. To address this challenge, we introduce Video-QTR (Query-Driven Temporal Reasoning), a lightweight framework that redefines video comprehension as a query-guided reasoning process. Instead of encoding every frame, Video-QTR dynamically allocates perceptual resources based on the semantic intent of the query, creating an adaptive feedback loop between reasoning and perception. Extensive experiments across five benchmarks: MSVD-QA, Activity Net-QA, Movie Chat, and Video MME demonstrate that Video-QTR achieves state-of-the-art performance while reducing input frame consumption by up to 73%. These results confirm that query-driven temporal reasoning provides an efficient and scalable solution for video understanding.
Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tencent（腾讯）；Wuhan University（武汉大学）；Southeast University（东南大学）；National University of Singapore（新加坡国立大学）
Tracking by Detection and Query: An Efficient End-to-End Framework for Multi-Object Tracking
organization= School of Automation , addressline= Southeast University , city= Nanjing , postcode= 210096 , country= China；organization= Key Laboratory of Measurement；organization= School of Physical \& Mathematical Sciences , addressline= Nanyang Technological University , postcode= 639798 , country= Singapore；organization= Big Data Institute , addressline= Central South University , city= Changsha , postcode= 410083 , country= China；organization= School of Instrument Science；Engineering , addressline= Southeast University , city= Nanjing , postcode= 210096 , country= China
StreamScout: Learning When to Look Deeper for Streaming Video Understanding
Carnegie Mellon University（卡内基梅隆大学）；University of Rochester（罗切斯特大学）；Northwestern University（西北大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；TikTok（字节跳动旗下TikTok）
Technical University of Munich（慕尼黑工业大学）；Siemens AG（西门子公司）
We propose a lightweight retrieval-based pipeline to predict 6DOF camera poses from RGB images. Our pipeline uses a convolutional neural network (CNN) to encode a query image as a feature vector. A nearest neighbor lookup finds the pose-wise nearest database image. A siamese convolutional neural network regresses the relative pose from the nearest neighboring database image to the query image. The relative pose is then applied to the nearest neighboring absolute pose to obtain the query image's final absolute pose prediction. Our model is a distilled version of NN-Net that reduces its paramete…
We propose a Vision-Language Transformer (VLT) framework for referring segmentation to facilitate deep interactions among multi-modal information and enhance the holistic understanding to vision-language features. There are different ways to understand the dynamic emphasis of a language expression, especially when interacting with the image. However, the learned queries in existing transformer works are fixed after training, which cannot cope with the randomness and huge diversity of the language expressions. To address this issue, we propose a Query Generation Module, which dynamically produc…
Lightweight Language-driven Grasp Detection using Conditional Consistency Model
FPT Software AI Center（FPT软件人工智能中心）；Automation & Control Institute, TU Wien（维也纳工业大学自动化与控制研究所）；Austrian Institute of Technology (AIT) GmbH（奥地利技术研究院）；Imperial College London（伦敦帝国学院）；University of Arkansas（阿肯色大学）；National University of Singapore（新加坡国立大学）；University of Liverpool（利物浦大学）
RankGround: Efficient High-Resolution GUI Grounding via Lightweight Reranker-Guided Crop Selection
Shenzhen University of Advanced Technology（深圳理工大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Xiamen University（厦门大学）
