Time Blindness: Why Video-Language Models Can't See What Humans Can?
Fuente:
arXiv
Saved in:
| Main Authors: | Upadhyay, Ujjwal, Ranjan, Mukul, Shen, Zhiqiang, Elhoseiny, Mohamed |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
From Sora What We Can See: A Survey of Text-to-Video Generation
by: Sun, Rui, et al.
Published: (2024)
by: Sun, Rui, et al.
Published: (2024)
On the Cultural Anachronism and Temporal Reasoning in Vision Language Models
by: Ranjan, Mukul, et al.
Published: (2026)
by: Ranjan, Mukul, et al.
Published: (2026)
Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
by: Wang, Wenxuan, et al.
Published: (2025)
by: Wang, Wenxuan, et al.
Published: (2025)
Vision-Language Models Can't See the Obvious
by: Dahou, Yasser, et al.
Published: (2025)
by: Dahou, Yasser, et al.
Published: (2025)
AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?
by: Bao, Han, et al.
Published: (2024)
by: Bao, Han, et al.
Published: (2024)
Your Vision-Language Model Can't Even Count to 20: Exposing the Failures of VLMs in Compositional Counting
by: Guo, Xuyang, et al.
Published: (2025)
by: Guo, Xuyang, et al.
Published: (2025)
Why Can't I Open My Drawer? Mitigating Object-Driven Shortcuts in Zero-Shot Compositional Action Recognition
by: Ahn, Geo, et al.
Published: (2026)
by: Ahn, Geo, et al.
Published: (2026)
BLINK: Multimodal Large Language Models Can See but Not Perceive
by: Fu, Xingyu, et al.
Published: (2024)
by: Fu, Xingyu, et al.
Published: (2024)
When Words Can't Capture It All: Towards Video-Based User Complaint Text Generation with Multimodal Video Complaint Dataset
by: Das, Sarmistha, et al.
Published: (2025)
by: Das, Sarmistha, et al.
Published: (2025)
Language Models Can See Better: Visual Contrastive Decoding For LLM Multimodal Reasoning
by: Pang, Yuqi, et al.
Published: (2025)
by: Pang, Yuqi, et al.
Published: (2025)
3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition
by: Slim, Habib, et al.
Published: (2023)
by: Slim, Habib, et al.
Published: (2023)
Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
by: Lee, Heekyung, et al.
Published: (2025)
by: Lee, Heekyung, et al.
Published: (2025)
How Well Can Vision Language Models See Image Details?
by: Gou, Chenhui, et al.
Published: (2024)
by: Gou, Chenhui, et al.
Published: (2024)
Can NeRFs See without Cameras?
by: Amballa, Chaitanya, et al.
Published: (2025)
by: Amballa, Chaitanya, et al.
Published: (2025)
Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos
by: Shen, Yixuan, et al.
Published: (2026)
by: Shen, Yixuan, et al.
Published: (2026)
Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision
by: Kim, Jinhee, et al.
Published: (2024)
by: Kim, Jinhee, et al.
Published: (2024)
GameVerse: Can Vision-Language Models Learn from Video-based Reflection?
by: Zhang, Kuan, et al.
Published: (2026)
by: Zhang, Kuan, et al.
Published: (2026)
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents
by: Chen, Jun, et al.
Published: (2024)
by: Chen, Jun, et al.
Published: (2024)
VideoVLA: Video Generators Can Be Generalizable Robot Manipulators
by: Shen, Yichao, et al.
Published: (2025)
by: Shen, Yichao, et al.
Published: (2025)
Can Hallucination Correction Improve Video-Language Alignment?
by: Zhao, Lingjun, et al.
Published: (2025)
by: Zhao, Lingjun, et al.
Published: (2025)
Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions
by: Guo, Zhongbin, et al.
Published: (2026)
by: Guo, Zhongbin, et al.
Published: (2026)
MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks
by: Chowdhury, Sanjoy, et al.
Published: (2025)
by: Chowdhury, Sanjoy, et al.
Published: (2025)
Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoning
by: Zhang, Gengyuan, et al.
Published: (2023)
by: Zhang, Gengyuan, et al.
Published: (2023)
Can Vision-Language Models Replace Human Annotators: A Case Study with CelebA Dataset
by: Lu, Haoming, et al.
Published: (2024)
by: Lu, Haoming, et al.
Published: (2024)
TRINS: Towards Multimodal Language Models that Can Read
by: Zhang, Ruiyi, et al.
Published: (2024)
by: Zhang, Ruiyi, et al.
Published: (2024)
LatentRouter: Can We Choose the Right Multimodal Model Before Seeing Its Answer?
by: Cheng, Xueqi, et al.
Published: (2026)
by: Cheng, Xueqi, et al.
Published: (2026)
Can Visual Encoder Learn to See Arrows?
by: Terashita, Naoyuki, et al.
Published: (2025)
by: Terashita, Naoyuki, et al.
Published: (2025)
StoryGPT-V: Large Language Models as Consistent Story Visualizers
by: Shen, Xiaoqian, et al.
Published: (2023)
by: Shen, Xiaoqian, et al.
Published: (2023)
VideoGameBench: Can Vision-Language Models complete popular video games?
by: Zhang, Alex L., et al.
Published: (2025)
by: Zhang, Alex L., et al.
Published: (2025)
RISE-Video: Can Video Generators Decode Implicit World Rules?
by: Liu, Mingxin, et al.
Published: (2026)
by: Liu, Mingxin, et al.
Published: (2026)
Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now
by: Sarkar, Ayush, et al.
Published: (2023)
by: Sarkar, Ayush, et al.
Published: (2023)
Can We Talk Models Into Seeing the World Differently?
by: Gavrikov, Paul, et al.
Published: (2024)
by: Gavrikov, Paul, et al.
Published: (2024)
MVP-Bench: Can Large Vision--Language Models Conduct Multi-level Visual Perception Like Humans?
by: Li, Guanzhen, et al.
Published: (2024)
by: Li, Guanzhen, et al.
Published: (2024)
LensWalk: Agentic Video Understanding by Planning How You See in Videos
by: Li, Keliang, et al.
Published: (2026)
by: Li, Keliang, et al.
Published: (2026)
Language Models Can Explain Visual Features via Steering
by: Ferrando, Javier, et al.
Published: (2026)
by: Ferrando, Javier, et al.
Published: (2026)
AI-Generated Images: What Humans and Machines See When They Look at the Same Image
by: Poletti, Silvia, et al.
Published: (2026)
by: Poletti, Silvia, et al.
Published: (2026)
CaTS-Bench: Can Language Models Describe Time Series?
by: Zhou, Luca, et al.
Published: (2025)
by: Zhou, Luca, et al.
Published: (2025)
Multimodal Language Models See Better When They Look Shallower
by: Chen, Haoran, et al.
Published: (2025)
by: Chen, Haoran, et al.
Published: (2025)
VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?
by: Tang, Yolo Y., et al.
Published: (2024)
by: Tang, Yolo Y., et al.
Published: (2024)
TimeBlind: A Spatio-Temporal Compositionality Benchmark for Video LLMs
by: Li, Baiqi, et al.
Published: (2026)
by: Li, Baiqi, et al.
Published: (2026)
Similar Items
-
From Sora What We Can See: A Survey of Text-to-Video Generation
by: Sun, Rui, et al.
Published: (2024) -
On the Cultural Anachronism and Temporal Reasoning in Vision Language Models
by: Ranjan, Mukul, et al.
Published: (2026) -
Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
by: Wang, Wenxuan, et al.
Published: (2025) -
Vision-Language Models Can't See the Obvious
by: Dahou, Yasser, et al.
Published: (2025) -
AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?
by: Bao, Han, et al.
Published: (2024)