This work introduces VideoChat3, a fully open, efficient, and generalist video-centric MLLM, which surpasses prior open-source models with equal or larger parameter counts with only 4B parameters and higher efficiency.
Abstract
Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse video types, making them effective only in specific domains. High computational demands further restrict their efficiency and scalability. Moreover, most models are only partially open, with key components such as training code, strategy, or datasets unavailable, which hinders reproducibility and slows community-driven development. To address these issues, we introduce VideoChat3, a fully open, efficient, and generalist video-centric MLLM. VideoChat3 advances video understanding through two complementary designs. For efficiency, we introduce Inflated 3D Vision Transformer (I3D-ViT) and Adaptive Frame Resolution for Streaming Video Perception, which enables efficient spatiotemporal representation and reduces the cost of processing video inputs during training and inference. For effectiveness, we develop a scalable video data synthesis pipeline that curates three diverse, high-quality training datasets: VideoChat3-Academic2M, VideoChat3-LV116K, and VideoChat3-OL617K, covering general, long-form, and streaming video scenarios, improving the model's generalization across domains. By integrating these designs, VideoChat3 achieves a rare balance of broad generalization and computational efficiency. Experiments across general, long-form, and streaming benchmarks demonstrate that VideoChat3 surpasses prior open-source models with equal or larger parameter counts with only 4B parameters and higher efficiency.
Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained large language models and condition generation on a textual prompt. Their strong performance on captioning, question answering, retrieval and temporal grounding comes at a computation and memory cost that grows with frame count and context length, limiting deployment in real-time, mobile and resource-constrained settings. This survey covers inference-efficiency mechanisms for visual and audiovisual VideoLLMs that report concrete reductions in parameter count, FLOPs per input, latency, memory, or visual and audio token count. We analyze bottlenecks across frame sampling, modality encoding, connector-level token reduction, and LLM prefilling and decoding. We organize methods by the pipeline stage at which they act, covering VideoLLMs developed since late 2022 together with earlier frame-sampling and vision-encoder mechanisms that remain components of current pipelines. We assemble literature-reported accuracy--cost comparisons under shared host models and input protocols wherever available, distinguish them from heterogeneous cross-paper evidence, and identify gaps in audiovisual efficiency and standardized evaluation. We maintain a repository at https://github.com/momentslab/awesome-efficient-videollm.
Killian Steunou, Yannis Tevissen, M. E. El Yacoubi· 0 citations
Understanding long-range videos remains a key challenge in computer vision due to high temporal redundancy and computational burden. Despite strong performance of recent models, they are constrained in terms of scalability and generalization when applied to longer video sequences. In this work, we present Keyframe-based Spatio-Temporal Adaptive Representation (K-STAR), a redundancy-aware video summarization framework designed to generate compact and semantically rich representations that are effective in downstream tasks. The proposed method jointly models appearance and motion cues while filtering redundant frames. Importantly, it preserves critical temporal transitions while significantly reducing the number of processed frames. Additionally, each key frame is encoded using object, scene, and background-aware prompts, enabling richer semantic representation. Evaluated on the UCF-101 dataset, K-STAR achieves Top-1 accuracy of 93.06% and Top-5 accuracy of $\mathbf{9 8. 7 3 \%}$, with $\mathbf{5 6} \times$ frame reduction and $\mathbf{1 1. 5} \times$ faster inference, demonstrating competitive performance with substantially improved efficiency.
Rahul Kumar, S. Channappayya· International Conference on...· 0 citations
It is proved that the computationally cheaper split space-time attention is equivalent to full space-time attention and is promising to extend VideoSEMA to longer videos with a dilated/sparse temporal attention.
N. Tran, Fanghui Xue, Shuai Zhang et al.· arXiv.org· 0 citations
STITCH is presented, a training-free method that divides a video into semantically meaningful temporal chunks that are computed once per video and reused across tasks, suggesting that reusable temporal abstraction is a promising direction for general video understanding.
Etienne Casanova, S. Brodjian, Pietro Perona· 0 citations
Open-world video understanding often requires a model to locate sparse visual evidence and acquire external knowledge that is absent from the video and its parametric memory. While Thinking-with-Videos enables active temporal perception and Deep Research supports multi-step information seeking, the two capabilities are typically developed in isolation. We introduce VideoRover, a unified Video Deep Research framework that iteratively coordinates video cropping, multimodal search, and webpage browsing. Given a video-question pair, VideoRover uses each tool result to select the next action, so localized video clips guide external retrieval and retrieved evidence triggers further video inspection and verification. To develop this capability, we construct an automated data curation pipeline, producing 26K verified SFT trajectories and 3K challenging RL instances. We also introduce VideoRover-Bench, a benchmark stratified by video duration and research difficulty. Experiments on VideoDR and VideoRover-Bench show that our VideoRover-8B-RL achieves performance comparable to proprietary models in the direct-answer setting without tool use while outperforming larger open-source models equipped with the same tool suite. Ablation studies and training dynamics further validate the complementary roles of active video grounding, external retrieval, and long-horizon reinforcement learning.
Wenqi Liu, Shijie Ma, Yunxiao Wang et al.· 0 citations
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