✨LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling

📝 Summary:
LongVT is an agentic framework that improves long video reasoning. It uses LMMs as tools for global-to-local video cropping and frame resampling to ground answers. This novel approach consistently outperforms existing baselines.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20785
• PDF: https://arxiv.org/pdf/2511.20785
• Project Page: https://evolvinglmms-lab.github.io/LongVT/
• Github: https://github.com/EvolvingLMMs-Lab/LongVT

🔹 Models citing this paper:
• https://huggingface.co/longvideotool/LongVT-RFT
• https://huggingface.co/longvideotool/LongVT-SFT
• https://huggingface.co/longvideotool/LongVT-RL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/longvideotool/LongVT-Source
• https://huggingface.co/datasets/longvideotool/LongVT-Parquet

✨ Spaces citing this paper:
• https://huggingface.co/spaces/longvideotool/LongVT-Demo

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#VideoAI #LMMs #AgenticAI #ComputerVision #AIResearch

✨InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision

📝 Summary:
InternVideo-Next proposes a two-stage Encoder-Predictor-Decoder framework for general video representation learning without text supervision. It uses a conditional diffusion decoder to bridge pixel fidelity with semantics in Stage 1, then a latent world model in Stage 2 to learn world knowledge a...

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01342
• PDF: https://arxiv.org/pdf/2512.01342

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#VideoFoundationModels #VideoAI #DeepLearning #UnsupervisedLearning #DiffusionModels

✨A Benchmark and Agentic Framework for Omni-Modal Reasoning and Tool Use in Long Videos

📝 Summary:
This paper introduces LongShOTBench, a diagnostic benchmark for long-form multimodal video understanding with open-ended questions and agentic tool use. It also presents LongShOTAgent, an agentic system for video analysis. Results show state-of-the-art models struggle significantly, highlighting ...

🔹 Publication Date: Published on Dec 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16978
• PDF: https://arxiv.org/pdf/2512.16978
• Project Page: https://mbzuai-oryx.github.io/LongShOT/
• Github: https://github.com/mbzuai-oryx/longshot

✨ Datasets citing this paper:
• https://huggingface.co/datasets/MBZUAI/longshot-bench

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#VideoAI #MultimodalAI #AgenticAI #AIbenchmark #AIResearch

✨DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer

📝 Summary:
DreamID-V is a novel video face swapping framework that uses diffusion transformers and curriculum learning. It achieves superior identity preservation and visual realism by bridging the image-to-video gap, outperforming existing methods and enhancing temporal consistency.

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01425
• PDF: https://arxiv.org/pdf/2601.01425
• Project Page: https://guoxu1233.github.io/DreamID-V/
• Github: https://guoxu1233.github.io/DreamID-V/

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#FaceSwapping #DiffusionModels #ComputerVision #GenerativeAI #VideoAI

✨Adaptive 1D Video Diffusion Autoencoder

📝 Summary:
One-DVA is a transformer video autoencoder with adaptive encoding and diffusion decoding. It enables variable-length latents and improved compression and detail recovery, addressing fixed-rate compression and deterministic reconstruction.

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04220
• PDF: https://arxiv.org/pdf/2602.04220

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#VideoAI #DiffusionModels #Autoencoders #DeepLearning #ComputerVision

✨Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions

📝 Summary:
Researchers created ASID-1M, a dataset of structured, quality-verified audiovisual instructions, and ASID-Captioner, a model trained on it. This improves fine-grained caption quality, reduces hallucinations, and achieves SOTA results.

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13013
• PDF: https://arxiv.org/pdf/2602.13013
• Github: https://github.com/ASID-Caption/ASID-Caption

🔹 Models citing this paper:
• https://huggingface.co/AudioVisual-Caption/ASID-Captioner-3B
• https://huggingface.co/AudioVisual-Caption/ASID-Captioner-7B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/AudioVisual-Caption/ASID-1M

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#MLLM #VideoAI #DeepLearning #ComputerVision #NLP

✨V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

📝 Summary:
V-JEPA 2 uses self-supervised learning on web videos and minimal robot data. It excels at video understanding, anticipation, Q&A, and zero-shot robotic planning. This approach yields a powerful world model for physical world planning.

🔹 Publication Date: Published on Jun 11, 2025

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/v-jepa-2-self-supervised-video-models-enable-understanding-prediction-and-planning
• PDF: https://arxiv.org/pdf/2506.09985
• Github: https://github.com/facebookresearch/vjepa2

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ckadirt/vjxla

✨ Spaces citing this paper:
• https://huggingface.co/spaces/vselvarajijay/vjepa2-latent-prediction
• https://huggingface.co/spaces/aavi21458/vjepa2-latent-prediction

==================================

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#AI #SelfSupervisedLearning #VideoAI #Robotics #WorldModels

✨Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning

📝 Summary:
SlotCurri addresses video object over-fragmentation using a reconstruction-guided slot curriculum. It progressively allocates slots, employs a structure-aware loss for sharp boundaries, and uses cyclic inference for temporal consistency. This method significantly improves object decomposition.

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22758
• PDF: https://arxiv.org/pdf/2603.22758
• Github: https://github.com/wjun0830/SlotCurri

🔹 Models citing this paper:
• https://huggingface.co/WJ0830/SlotCurri

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#VideoAI #ObjectCentricLearning #ComputerVision #DeepLearning #ObjectSegmentation

✨6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models

📝 Summary:
This paper introduces a mixed-precision quantization framework for video diffusion transformers. It dynamically allocates NVFP4/INT8 based on layer volatility and uses Temporal Delta Cache to skip computations, significantly reducing memory and cost while preserving quality.

🔹 Publication Date: Published on Mar 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18742
• PDF: https://arxiv.org/pdf/2603.18742

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#Quantization #DiffusionModels #VideoAI #DeepLearning #ModelOptimization

✨Video Analysis and Generation via a Semantic Progress Function

📝 Summary:
Researchers developed a Semantic Progress Function to analyze and correct non-linear semantic evolution in generated media. This function identifies uneven pacing, enabling a linearization procedure that re-times sequences for smoother, more coherent transitions at a constant semantic rate.

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.22554
• PDF: https://arxiv.org/pdf/2604.22554
• Project Page: https://sagipolaczek.github.io/semantic-progress-function/
• Github: https://github.com/SagiPolaczek/semantic-progress-function

==================================

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#VideoAI #GenerativeAI #ComputerVision #SemanticAnalysis #AIResearch