CausalCine enables real-time causal autoregressive multi-shot video generation via multi-shot training, content-aware memory routing for coherence, and distillation to few-step inference.
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InternVideo: General Video Foundation Models via Generative and Discriminative Learning
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abstract
The foundation models have recently shown excellent performance on a variety of downstream tasks in computer vision. However, most existing vision foundation models simply focus on image-level pretraining and adpation, which are limited for dynamic and complex video-level understanding tasks. To fill the gap, we present general video foundation models, InternVideo, by taking advantage of both generative and discriminative self-supervised video learning. Specifically, InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives, and selectively coordinates video representations of these two complementary frameworks in a learnable manner to boost various video applications. Without bells and whistles, InternVideo achieves state-of-the-art performance on 39 video datasets from extensive tasks including video action recognition/detection, video-language alignment, and open-world video applications. Especially, our methods can obtain 91.1% and 77.2% top-1 accuracy on the challenging Kinetics-400 and Something-Something V2 benchmarks, respectively. All of these results effectively show the generality of our InternVideo for video understanding. The code will be released at https://github.com/OpenGVLab/InternVideo .
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cs.CV 24representative citing papers
TransVLM formalizes Shot Transition Detection as identifying full temporal transition segments rather than single cut points and introduces a VLM that injects optical flow as a motion prior via simple feature fusion, plus a synthetic data engine and benchmark.
TF-SMOT composes pretrained vision-language models into a training-free pipeline that reaches state-of-the-art tracking and improved summary quality on the BenSMOT benchmark.
V-Nutri fuses final-dish features with cooking-process keyframes from egocentric videos to improve dish-level calorie and macronutrient estimation over single-image baselines.
InstrAction pretrains video foundation models using action-centric data filtering, hard negatives, an Action Perceiver module, DTW-Align, and Masked Action Modeling to reduce static bias and outperform prior models on a new InstrAct Bench for semantic, procedural, and retrieval tasks.
Fully end-to-end training with a sentence-conditioned adapter outperforms frozen-backbone baselines for localizing video segments that match sentence queries.
LRM is a large transformer that predicts a NeRF directly from a single image after training on a million-object multi-view dataset.
VideoChat integrates video models and LLMs via a learnable interface for chat-based spatiotemporal and causal video reasoning, trained on a new video-centric instruction dataset.
XComp reaches extreme video compression (one token per selective frame) via learnable progressive token compression and question-conditioned frame selection, lifting LVBench accuracy from 42.9 percent to 46.2 percent after tuning on 2.5 percent of standard data.
FreqFormer applies heterogeneous attention (dense global on low frequencies, block-sparse on mid, local on high) plus adaptive spectral routing to reduce attention cost in long-sequence video diffusion transformers.
UniversalVTG is a lightweight foundation model for video temporal grounding that achieves state-of-the-art results across five benchmarks while being over 100 times smaller than recent MLLM-based methods.
Streamo is a streaming video LLM trained end-to-end on the new Streamo-Instruct-465K dataset that unifies multiple real-time video tasks with claimed strong temporal reasoning and generalization.
CalMRL mitigates anchor shift in multimodal representation learning by calibrating incomplete alignments through representation-level imputation of missing modalities using priors and a bi-step optimization with closed-form shared latent posteriors.
A plug-and-play Anonymizing Adapter Module removes private information from video latent features using self-supervised privacy objectives and consistency losses while retaining utility on action recognition, temporal detection, and anomaly tasks.
VideoChat-Flash applies hierarchical video token compression to achieve ~50x reduction in context length for long videos while maintaining near-original performance on long-context benchmarks.
V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.
MVBench is a benchmark of 20 temporal video understanding tasks built by transforming static tasks into dynamic ones, with VideoChat2 outperforming prior MLLMs by over 15%.
LanguageBind aligns video, infrared, depth, and audio to a frozen language encoder via contrastive learning on the new VIDAL-10M dataset, extending video-language pretraining to N modalities.
InternVid supplies 7M videos and LLM captions to train ViCLIP, which reaches leading zero-shot action recognition and competitive retrieval performance.
The PhyScore challenge creates the first benchmark requiring metrics to jointly score video quality, physical realism, condition alignment, and temporal consistency while localizing physical anomalies in 1554 videos from seven generative models across text-to-2D, image-to-4D, and video-to-4D tracks.
A new adapter module combining boundary-aware state space modeling with spatial processing boosts localization and robustness in temporal action detection.
TempR1 applies temporal-aware multi-task RL using GRPO and three types of localization rewards to achieve SOTA temporal understanding in MLLMs with synergistic gains from joint optimization.
InternVL scales a vision model to 6B parameters and aligns it with LLMs using web data to achieve state-of-the-art results on 32 visual-linguistic benchmarks.
Proposes a multimodal pipeline for video retrieval that incorporates information from multiple frames to enable higher-level abstraction beyond single-image object detection.
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Privacy Beyond Pixels: Latent Anonymization for Privacy-Preserving Video Understanding
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Revisiting Feature Prediction for Learning Visual Representations from Video
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MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
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InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
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LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)
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Efficient Spatial-Temporal Focal Adapter with SSM for Temporal Action Detection
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