The paper proposes an explicit pixel‑level regional representation for video quality assessment called a Quality‑Relevance Heatmap (QRH), which fuses localized degradation evidence with perceptual priors guided by a vision‑language model, all without pixel‑level annotations. Two deterministic fragment‑sampling strategies are introduced: distribution‑preserving sampling approximates the original QRH‑score distribution without retraining, while quality‑aware sampling focuses on high‑QRH regions and is used with fine‑tuning. Experiments on the DOVER model across multiple benchmarks show that distribution‑preserving sampling closely matches predictions from random sampling, and quality‑aware sampling improves most evaluated metrics, demonstrating the utility of explicit regional representations for downstream VQA tasks.
arXiv:2607. 01086v1 Announce Type: cross Abstract: The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs).
CamWorldQA introduces the first benchmark for assessing the perceptual quality of camera‑controlled world video generation, featuring 720 videos generated by six methods from 20 source videos across six camera trajectories, each scored by human raters. The paper also presents CWQA, a no‑reference quality assessment network that combines spatial, temporal motion, and optical flow features to predict quality scores. Experiments show CWQA outperforms existing VQA methods on the CamWorldQA dataset.
VGIQA is a new image quality assessment model that provides both local and global quality evaluations. It uses a two-stage training process—initial cold-start followed by reinforcement learning—with local quality reward functions. The model outperforms existing large multimodal language models on the newly introduced VGSR-2K dataset and improves performance on degradation perception tasks.
High-Fidelity Video Quality Assessment (HFVQA) is a new framework that uses fixed-size spatio‑temporal patches across multiple scales, including the original resolution, to preserve low‑level quality cues and semantic context. It incorporates a lightweight auxiliary network that learns VQA‑specific saliency directly from quality supervision, enabling the model to focus on the most important spatio‑temporal regions. By combining high‑fidelity cues with task‑specific saliency, HFVQA achieves state‑of‑the‑art performance on standard no‑reference VQA benchmarks while processing only about 12% of the candidate patches, making it computationally efficient.
arXiv:2611.07301v1 Announce Type: new Abstract: Reinforcement learning has become an effective approach for improving video generation, but its success depends heavily on reliable reward models. Exis...
VGIQA is a new image quality assessment model that provides both local and global quality evaluations. It uses a two‑stage training process—starting with a cold‑start phase and then reinforcement learning guided by local quality reward functions. The model demonstrates superior performance on the newly introduced VGSR‑2K dataset, outperforming existing open‑source multi‑modal large language models such as Qwen2.5‑VL‑72B‑Instruct and Qwen3‑VL‑32B‑Instruct, and it also improves degradation perception tasks.
arXiv:2607. 14194v1 Announce Type: cross Abstract: Text-to-video (T2V) generators can synthesize realistic and temporally coherent videos, but controllably removing a target concept from a generator remains difficult.
arXiv:2609.37925v1 Announce Type: cross Abstract: Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Trai...
arXiv:2609.26425v1 Announce Type: new Abstract: KV cache memory has become a major deployment bottleneck for video generation and world models, which motivates low-bit quantization study for efficien...
The paper surveys training‑free methods to accelerate inference in Video Large Language Models (VLMs), aiming to reduce computational, latency, and memory costs while retaining task‑relevant spatiotemporal evidence. It categorizes techniques by their intervention points in the inference pipeline—input, visual encoding, projector, prefill, and decoding—and discusses post‑training quantization as a cross‑stage approach. The authors analyze how the position of each intervention influences evidence preservation and computational savings, noting that local efficiency gains may not lead to overall acceleration, and they provide quantitative comparisons from prior work and new experiments to evaluate accuracy‑latency trade‑offs.
arXiv:2507.08375v2 Announce Type: replace Abstract: Video restoration and enhancement are critical not only for improving visual quality, but also as essential pre-processing steps to boost the perfo...
arXiv:2609.38377v1 Announce Type: new Abstract: Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language mode...