Can you SPLICE it together? A Human Curated Benchmark for Probing Visual Reasoning in VLMs
SPLICE benchmark reveals significant gaps in VLM visual reasoning compared to human performance.
Key Findings
Methodology
SPLICE benchmark is derived from the COIN video dataset, comprising 3,381 videos segmented into 11,423 event clips. The task involves rearranging these clips to assess visual reasoning. Tests include video, text, and video+text input modes.
Key Results
- VLMs achieve 23% to 51% accuracy in video-only mode, significantly lower than human 85%.
- Text annotations improve model accuracy but do not affect human performance, indicating models rely more on language than visuals.
- VLMs perform better on videos dominated by causal and temporal reasoning.
Significance
This study highlights the deficiencies in VLMs' visual reasoning, advancing the understanding of multimodal models, especially in complex reasoning tasks. It provides clear directions for future model improvements.
Technical Contribution
SPLICE offers a human-curated benchmark, filling the gap in visual reasoning evaluation. It challenges current state-of-the-art VLMs through multidimensional reasoning tasks.
Novelty
SPLICE is the first benchmark focusing on event sequence reconstruction, emphasizing visual reasoning over language priors, distinguishing it from previous multimodal studies.
Limitations
- VLMs still rely on language cues when processing visual information, leading to poor performance in purely visual tasks.
- Some tasks in the dataset may be too complex for non-experts.
Future Work
Future research could explore more complex multimodal interactions and more efficient visual information processing to bridge the gap with human performance.
AI Executive Summary
Vision-language models (VLMs) have made significant strides in multimodal tasks, yet they still struggle with visual reasoning. The SPLICE benchmark evaluates models on event sequence reconstruction using 3,381 videos from the COIN dataset. Results show that while models improve with text annotations, they still fall short of human performance.
The study reveals that VLMs perform better on videos dominated by causal and temporal reasoning but struggle with those requiring contextual and spatial reasoning. Even in everyday tasks, models fail to match human reasoning abilities, indicating a reliance on language priors in visual understanding.
SPLICE provides a crucial reference for future multimodal model research, emphasizing the need for more complex model designs and efficient visual information processing in visual reasoning tasks. Limitations include models' over-reliance on language cues and the complexity of some tasks for non-experts. Future research directions include exploring more complex multimodal interactions and efficient visual processing.
Deep Analysis
Background
In recent years, vision-language models (VLMs) have achieved remarkable progress in multimodal tasks. However, despite their excellent performance in text reasoning tasks, VLMs still underperform in purely visual reasoning tasks. Existing benchmarks often rely on language cues, lacking rigorous evaluation of visual reasoning capabilities.
Core Problem
The core problem is assessing VLMs' reasoning capabilities in purely visual tasks. Current benchmarks often rely on language priors, failing to fully reflect models' visual reasoning abilities, which is crucial for complex reasoning tasks.
Innovation
The SPLICE benchmark provides a human-curated test environment focused on event sequence reconstruction. It emphasizes visual reasoning over language priors, filling a gap in existing benchmarks.
Methodology
- �� Extract videos from the COIN dataset, ensuring diversity.
- �� Segment videos into event clips, requiring models to reorder them.
- �� Provide video, text, and video+text input modes.
- �� Test using various state-of-the-art VLMs.
Experiments
The experimental design includes 3,381 videos from the COIN dataset, segmented into 11,423 event clips. Tests include video, text, and video+text input modes, evaluated using various state-of-the-art VLMs.
Results
Results show VLMs achieve 23% to 51% accuracy in video-only mode, significantly lower than human 85%. Text annotations improve model accuracy but do not affect human performance, indicating models rely more on language than visuals.
Applications
The SPLICE benchmark can be used to evaluate multimodal models' performance in visual reasoning tasks, especially those requiring complex reasoning.
Limitations & Outlook
VLMs still rely on language cues when processing visual information, leading to poor performance in purely visual tasks. Some tasks in the dataset may be too complex for non-experts.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking a meal. You have a series of steps like chopping vegetables, boiling rice, and plating. SPLICE is like scrambling these steps and asking you to reorder them. For humans, this is simple because we know the cooking sequence. But for machines, it's like a puzzle. Machines need to understand the meaning of each step and order them based on context and causality. It's like giving machines an IQ test to see if they can complete tasks as intelligently as humans.
ELI14 Explained like you're 14
Imagine you're playing a puzzle game, but the pieces are video clips. SPLICE is like this game, scrambling video clips and asking machines to reorder them. Humans are great at this because we know the order of things, like washing hands before eating. But machines are like beginners, needing to learn how to understand these sequences. This research is to see if machines can complete this task as smartly as we do. The results show machines need more practice to catch up with us!
Glossary
Vision-Language Model (VLM)
An AI model capable of processing both visual and language information.
Used to evaluate model performance in multimodal tasks.
SPLICE
A benchmark for testing visual reasoning capabilities, focusing on event sequence reconstruction.
Used to evaluate VLM performance in visual reasoning tasks.
COIN Dataset
A video dataset containing various tasks with detailed step annotations.
The data source for the SPLICE benchmark.
Event Sequence Reconstruction
The task of reordering shuffled event clips.
The core task of the SPLICE benchmark.
Multimodal
Tasks involving multiple forms of information, such as visual and language.
VLMs need to process multimodal information to complete tasks.
Open Questions Unanswered questions from this research
- 1 How to improve VLM performance in purely visual tasks? Current methods rely too heavily on language cues.
- 2 How to design more complex multimodal interactions to enhance model reasoning capabilities?
Applications
Immediate Applications
Educational Assistance
SPLICE can be used to evaluate visual reasoning capabilities in educational software, aiding in the development of smarter teaching tools.
Long-term Vision
Autonomous Driving
By enhancing visual reasoning capabilities, VLMs can be used in decision-making systems for autonomous vehicles, improving safety and efficiency.
Abstract
In this work, we introduce SPLICE, a human-curated benchmark derived from the COIN instructional video dataset, designed to probe event-based reasoning across multiple dimensions: temporal, causal, spatial, contextual, and general knowledge. SPLICE includes 3,381 human-filtered videos spanning 12 categories and 180 sub-categories, such as sports, engineering, and housework. These videos are segmented into a total of 11,423 event clips. We evaluate both human participants and state-of-the-art vision-language models (VLMs) on the task of rearranging these clips into coherent event sequences to assess visual reasoning capabilities. Results reveal a significant gap: VLMs struggle to match human performance. While human-annotated textual descriptions improve model accuracy, they do not affect human performance, suggesting that models rely more on language priors than on visual understanding. Even with annotations, VLMs fall short of human-level reasoning, underscoring persistent challenges in visual reasoning. A deeper analysis across sub-categories shows that VLMs perform relatively better on videos where temporal and causal reasoning are dominant, compared to those where contextual and spatial reasoning are dominant. They also perform better on everyday tasks than on specialized ones.