TempCore: Are Video QA Benchmarks Temporally Grounded?

arXiv:2509.01167 · cs.CV, cs.CL, cs.LG · Submitted 2025-09-01 · Read on arXiv

cs.CV, cs.CL, cs.LG

Submitted: 2025-09-01

Updated: 2026-09-20

Comments: EMNLP 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 122× in this paper · explained in 2 episodes
• Accuracy — 45× in this paper · explained in 1 episode
• Temporal Reasoning — 11× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 9× in this paper
• Grounding — 8× in this paper · explained in 3 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Frame Selection Sensitivity — 6× in this paper
• Language Independence Score — 6× in this paper
• Visual Grounding — 5× in this paper · explained in 2 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Vision-Language Models (VLMs) — 3× in this paper · explained in 6 episodes
• AGN — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Association for Computational Linguistics — 2× in this paper
• Benchmarking — 2× in this paper · explained in 3 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Temporal Purity Index — 2× in this paper
• AUC — 1× in this paper · explained in 2 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• In Empirical Methods in Natural Language Processing — 1× in this paper
• In International Conference on Learning Representations — 1× in this paper
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• Qwen2-Audio Technical Report
• When +1% Is Not Enough: A Paired Bootstrap Protocol for Evaluating Small Improvements
• GPT-4 Technical Report
• Gemma 2: Improving Open Language Models at a Practical Size
• Qwen3-VL Technical Report
• The Llama 3 Herd of Models
• Qwen2.5-VL Technical Report
• Frame Sampling Strategies Matter: A Benchmark for small vision language models
• Reducing Text Bias in Synthetically Generated MCQAs for VLMs in Autonomous Driving
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• Time Blindness: Why Video-Language Models Can't See What Humans Can?
• SmolVLM: Redefining small and efficient multimodal models
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
• Qwen2 Technical Report
• VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
• LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models

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