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:2607. 09876v1 Announce Type: cross Abstract: Automatically retrieving videos from large camera-trap datasets remains challenging.
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: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...
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: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.
arXiv:2509.02807v2 Announce Type: replace Abstract: Multi-modal large language models (MLLMs) have shown impressive generalization across tasks using images and text modalities. While their extension...
TKCAM is a new framework for generating controllable camera motion conditioned on text and sparse keyframes, using a 12‑dimensional kinematic representation and a two‑stage masked transformer with residual vector quantization. It allows users to provide free‑form text prompts and RGB observations at selected timestamps, enabling temporally localized visual guidance for coherent trajectory synthesis. The authors also introduce the RealEstate10K‑Cap dataset and a Universal CLaTr Evaluator, showing that TKCAM outperforms recent baselines on FID, text‑motion matching, and retrieval metrics.
VidOmni-Bench is a new benchmark for fine‑grained video understanding that asks models to verify whether each event in dense video captions is supported by the video. It contains 500 videos covering five complexity types and durations from 4 seconds to 90 minutes, and uses human‑verified sentence‑level labels to create hard negatives. Experiments show that Video‑LLMs often hallucinate events, struggle to detect incorrect descriptions, and exhibit varying weaknesses depending on video complexity and duration.
arXiv:2501.04001v4 Announce Type: replace Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...
Generating high-quality and controllable camera motion is essential for AI-assisted cinematography, video synthesis, and 3D scene understanding. We introduce TKCAM, a Text- and Keyframe-conditioned CA...
The paper introduces three new vision‑centric evaluation benchmarks—temporal frame retrieval, video future prediction, and causal memory distortion—to assess visual question answering in large video models. Unlike traditional benchmarks that rely on text-based multiple choice questions, these tasks require models to reason directly from visual inputs. The authors find that current state‑of‑the‑art models struggle with visual queries, highlighting a gap in visual understanding that future research should address.