MMVIAD is the first multi-view continuous video dataset for industrial anomaly detection with four supported tasks, and the VISTA model improves average benchmark scores from 45.0 to 57.5 on unseen data while surpassing GPT-5.4.
Video-llama: An instruction-tuned audio-visual language model for video understanding
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3roles
background 2polarities
background 2representative citing papers
MLLMs know event timing during prefill via sparse Temporal Grounding Heads but lose it in autoregressive decoding; restricting visual context to the high-attention interval at inference time improves VTG performance on three benchmarks.
VISD proposes structured self-distillation with a multi-dimensional judge model and direction-magnitude decoupling to improve token-level credit assignment and convergence speed in VideoLLM reasoning training.
citing papers explorer
-
MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection
MMVIAD is the first multi-view continuous video dataset for industrial anomaly detection with four supported tasks, and the VISTA model improves average benchmark scores from 45.0 to 57.5 on unseen data while surpassing GPT-5.4.
-
MLLMs Know When Before Speaking: Revealing and Recovering Temporal Grounding via Attention Cues
MLLMs know event timing during prefill via sparse Temporal Grounding Heads but lose it in autoregressive decoding; restricting visual context to the high-attention interval at inference time improves VTG performance on three benchmarks.
-
VISD: Enhancing Video Reasoning via Structured Self-Distillation
VISD proposes structured self-distillation with a multi-dimensional judge model and direction-magnitude decoupling to improve token-level credit assignment and convergence speed in VideoLLM reasoning training.