Video Large Language Models (video LLMs) have demonstrated remarkable capabilities in video understanding tasks, such as video question answering and temporal localization. However, understanding long videos still remains a significant challenge. Existing video LLMs adopt uni-granularity tokens for long videos, failing to simultaneously understand both high-level semantics and low-level visual details in videos. To tackle this problem, we propose ReCrossVLLM, a reflective cross-granularity grounding framework for video LLM with preference optimization to collaboratively achieve long video understanding, which not only retains the capabilities of high-level video semantics understanding, but also strengthens the fine-grained understanding abilities. Specifically, we propose the coarse-to-fine grounding and fine-to-coarse reflection strategies for long video understanding. In the coarse-to-fine grounding strategy, the video LLM with a coarse-grained module first locates the key video segments from the long video by tackling massive frames of the long video with fewer per-frame tokens. And then video LLM adapted with the fine-grained module further analyzes the key video segments with more per-frame tokens so that it can understand fine-grained information. In case the video LLM locates the wrong key video segments, during the inference stage, our designed fine-to-coarse reflection strategy instructs the fine-grained module to reflect the effectiveness of the locating result and decide whether to return to the coarse-to-fine grounding strategy with reflection feedback. Additionally, during the training stage, the coarse-to-fine grounding strategy is optimized with our proposed cross-granularity preference optimization strategy to further improve grounding efficiency. Extensive experiments for long video question answering and temporal video grounding tasks demonstrate that our proposed ReCrossVLLM framework can significantly improve the Video Large Language Model for long video understanding.

Video Temporal Grounding (VTG) localizes moments in untrimmed videos using natural language queries. Most VTG datasets focus on short videos, and existing approaches excel in short-term cross-modal matching but struggle with long VTG, where long-range temporal reasoning is required for complex events. Existing approaches typically output timestamp predictions without intermediate steps, limiting effective reasoning, whereas humans solve this step by step. To address this, we propose a long VTG framework, StepVTG, with multimodal visual and speech inputs, leveraging Large Language Models (LLMs) for step-by-step reasoning. Specifically, we transform task descriptions, speech, and visual inputs into text prompts. To enhance temporal reasoning, we introduce the Boundary-Perceptive Prompting strategy, which includes: i) a multiscale denoising Chain-of-Thought (CoT) combining global and local semantics with noise filtering, ii) validity principles to ensure LLMs generate reasonable, parsable predictions, and iii) one-shot In-Context Learning (ICL) to improve reasoning via imitation. For evaluation, we establish MM-LVTG, a new long VTG benchmark with multimodal inputs, and demonstrate through extensive experiments that StepVTG achieves state-of-the-art performance. It offers explainable reasoning steps for predictions and reveals potential in facilitating video understanding with off-the-shelf LLMs.

Multimodal Large Language Models (Multimodal LLMs) have shown their strength in Video Question Answering (VideoQA). However, due to the black-box nature of end-to-end training strategies, existing approaches based on Multimodal LLMs suffer from the lack of interpretability for VideoQA: they can neither present reasoning paths nor indicate where the answers are derived from the video. To address this issue, we propose **MSR-ViR** (**M**odularized **S**elf-**R**eflected **Vi**deo **R**easoner), which for the first time integrates modular networks to Multimodal LLMs, capable of providing VideoQA with explicit reasoning paths for more interpretability. Specifically, a **MoST-Grounding** (Modularized Spatial-Temporal Grounding) network is proposed to decompose complex questions via tree-structured policies, localizing relevant temporal and spatial segments within videos through step-by-step reasoning. The proposed MoST-Grounding network provides explicit visually grounded information for Multimodal LLMs with clear reasoning paths, thus enhancing interpretability for the predicted answers. To further improve the reasoning quality, we design an **Alternate Self-reflection Training Strategy** to jointly optimize policy generation and Multimodal LLMs. Experiments on real-world datasets demonstrate the superiority of our proposed MSR-ViR framework in video understanding, reasoning transparency, and providing explicit localization evidence for answers.

Large language models (LLMs) have shown remarkable text understanding capabilities, which have been ex-tended as Video LLMs to handle video data for compre-hending visual details. However, existing Video LLMs can only provide a coarse description of the entire video, failing to capture the precise start and end time bound-ary of specific events. In this paper, we solve this issue via proposing VTimeLLM, a novel Video LLM designed for fine-grained video moment understanding and reasoning with respect to time boundary. Specifically, our VTimeLLM adopts a boundary-aware three-stage training strategy, which respectively utilizes image-text pairs for feature alignment, multiple-event videos to increase temporal-boundary awareness, and high-quality video-instruction tuning to further improve temporal understanding ability as well as align with human intents. Extensive experiments demonstrate that in fine-grained time-related comprehension tasks for videos such as Temporal Video Grounding and Dense Video Captioning, VTimeLLM significantly outperforms existing Video LLMs. Besides, benefits from the fine-grained temporal understanding of the videos further enable VTimeLLM to beat existing Video LLMs in video di-alogue benchmark, showing its superior cross-modal understanding and reasoning abilities.11Our project page is at https://github.com/huangb23/VTimeLLM

We present H2V4Sports, a real-time horizontal-to-vertical video converter specifically designed for sports live broadcasts. With the increasing demand of smartphone users who prefer to watch sports events on their vertical screens anywhere, anytime, our platform provides a seamless viewing experience. We achieve this by fine-tuning and pruning an object detector and tracker, which enables us to provide real-time, accurate key-object tracking results despite the complexity of sports scenes. Additionally, we propose a video virtual director platform that captures the most informative vertical zones from horizontal video live frames using various director logic for a smooth frame-to-frame transition. We have successfully demonstrated our platform in two popular sports: basketball and diving, and the results indicate that our technology delivers high-quality vertical scenes that are beneficial for smartphone users and other vertical scenarios.