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SmolVLM2: Bringing Video Understanding to Every Device
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Jul 6
Learning Flexible Generalization in Video Quality Assessment by Bringing Device and Viewing Condition Distributions
Video quality assessment (VQA) plays a critical role in optimizing video delivery systems. While numerous objective metrics have been proposed to approximate human perception, the perceived quality strongly depends on viewing conditions and display characteristics.
arXiv AI
Jun 8
Watch, Remember, Reason: Human-View Video Understanding with MLLMs
arXiv:2606. 07433v1 Announce Type: cross Abstract: Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios.
arXiv AI
Jun 29
MLVC: Multi-platform Learned Video Codec for Real-World Deployment
arXiv:2606. 28027v1 Announce Type: cross Abstract: Neural video codecs have surpassed classical codecs in coding efficiency but remain impractical for deployment due to cross-platform incompatibility and high computational cost.
arXiv AI
Jun 9
Video Understanding by Design: How Datasets Shape Video Models
arXiv:2509. 09151v2 Announce Type: replace-cross Abstract: Research in video understanding has advanced rapidly, driven by increasingly diverse datasets and more powerful model architectures.
arXiv AI
Jun 12
OmniDirector: General Multi-Shot Camera Cloning without Cross-Paired Data
arXiv:2606. 13432v1 Announce Type: cross Abstract: Cloning camera motion from reference videos is an important task in video generation, as videos provide intuitive and precise control.
arXiv AI
Jul 10
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
arXiv:2607. 07907v1 Announce Type: cross Abstract: With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data.