arXiv AI
MarineLife-16K: An Attribute-Rich Video-Text Dataset for Motion-Aware Marine Video Understanding
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arXiv AI
Jul 28
MarineEVT: Advancing Event-Centric Marine Video Understanding via Visual Tool Reasoning
arXiv:2607. 24064v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) have achieved remarkable success in visual understanding, driven by the growing availability of high-quality image-text pairs.
arXiv AI
Jul 14
Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data
arXiv:2607. 09876v1 Announce Type: cross Abstract: Automatically retrieving videos from large camera-trap datasets remains challenging.
arXiv AI
2d ago
OpenHome: From Perception to Insight in Smart-Home Camera Videos
OpenHome is a visual-only, multiple-choice benchmark designed to evaluate smart‑home camera video understanding. It includes 2,254 questions across 1,118 real‑world clips, organized into 10 tasks spanning perception, reasoning, and insight levels. The benchmark reveals that closed‑source models outperform open‑weight models, with the best closed‑source model surpassing the best open‑weight model by significant margins on all levels.
arXiv Computer Vision
Aug 24
WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition
arXiv:2608.21281v1 Announce Type: new Abstract: Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging...
arXiv Computation and Language
Aug 31
Long Story Short: Story-level Video Understanding from 20K Short Films
The paper introduces Short‑Films 20K (SF20K), a large publicly available movie dataset comprising 20,143 amateur films totaling 3,582 hours, with an average length of 12 minutes per film. Accompanying the dataset is SF20K‑Test, a manual open‑ended question‑answering benchmark featuring 95 movies and 979 question‑answer pairs. Analysis of the benchmark shows limited data leakage, highlights the necessity of long‑term reasoning, and demonstrates that instruction tuning on the large‑scale dataset significantly boosts vision‑language model performance.
arXiv Computer Vision
Aug 21
ID-VTG: Image-Disambiguated Video Temporal Grounding
arXiv:2608. 20127v1 Announce Type: new Abstract: Video Temporal Grounding (VTG) faces significant challenges when natural language queries must distinguish between multiple events involving visually similar entities, particularly when relying on fine-grained visual attributes that are difficult to describe accurately in words alone.