CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning (1910.04744v2)

Abstract: Computer vision has undergone a dramatic revolution in performance, driven in large part through deep features trained on large-scale supervised datasets. However, much of these improvements have focused on static image analysis; video understanding has seen rather modest improvements. Even though new datasets and spatiotemporal models have been proposed, simple frame-by-frame classification methods often still remain competitive. We posit that current video datasets are plagued with implicit biases over scene and object structure that can dwarf variations in temporal structure. In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved. Our dataset, named CATER, is rendered synthetically using a library of standard 3D objects, and tests the ability to recognize compositions of object movements that require long-term reasoning. In addition to being a challenging dataset, CATER also provides a plethora of diagnostic tools to analyze modern spatiotemporal video architectures by being completely observable and controllable. Using CATER, we provide insights into some of the most recent state of the art deep video architectures.

• The paper introduces a synthetic dataset that challenges video models to perform long-term spatiotemporal reasoning.
• It provides diagnostic tools that expose current architectures’ struggles with complex action compositions and severe occlusions.
• Experimental benchmarks reveal that state-of-the-art 3D networks underperform on tasks requiring adversarial target tracking and precise spatiotemporal analysis.

Overview of "CATER: A Diagnostic Dataset for Compositional Actions and Temporal Reasoning"

The paper entitled "CATER: A diagnostic dataset for Compositional Actions and Temporal Reasoning" addresses a notable gap in the video understanding domain by introducing a dataset specifically designed to test models' abilities to interpret complex spatiotemporal compositions. Contemporary video action recognition tasks frequently rely on datasets where implicit scene and object biases may overshadow temporal dynamics, thus not fully capitalizing on the intrinsic understanding capabilities of models. This paper proposes the CATER dataset, which seeks to bypass these limitations through a synthetic yet highly controllable environment that mandates thorough spatiotemporal reasoning.

Key Contributions of the CATER Dataset

1. Synthetic Video Dataset: The CATER dataset is synthetically generated, enabling precise control over scene dynamics to better emphasize temporal reasoning. It employs a library of standard 3D objects to generate videos where recognizing compositions of object movements necessitates long-term reasoning.
2. Diagnostic Tools: CATER is not merely a data collection for benchmarking but a toolkit for diagnosing and analyzing modern spatiotemporal architectures. Its comprehensive observability and control afford a new avenue for scrutinizing model behaviors under variably controlled scenarios.
3. Focus on Spatiotemporal Understanding: The dataset is specifically crafted to interrogate a model's understanding of long-term spatial relations and temporal reasoning. The authors contended that current video architectures underperform relative to image-centric models due to the oversimplification of video tasks predominantly judged on static frames.
4. Benchmarking with State-of-the-Art Models: The paper subjects recent spatiotemporal models, such as 3D convolutional networks and non-local networks, to benchmark performance on CATER, revealing their struggles with tasks involving complex temporal compositions and occlusions.

Dataset Structure and Tasks

CATER comprises three distinct tasks intended to incrementally elevate the complexity of video understanding:

• Atomic Action Recognition: This task involves recognizing simple, individual actions like "slide(cube)" or "rotate(cylinder)" in each video segment.
• Compositional Action Recognition: This task increases complexity by requiring recognition of spatiotemporal compositions, leveraging Allen's temporal logic to establish relationships between atomic actions.
• Adversarial Target Tracking: This task mimics real-world challenges, like the cup-and-ball game, requiring the system to track an object hidden through containment and occlusion by other objects throughout the video sequence.

Evaluation and Results

In their experimental evaluation, various models, including R3D and TSN, struggled on the CATER tasks, particularly on tasks involving long-term occlusions. Despite employing strategies such as segment-based temporal pooling and LSTM-based aggregation, the results indicated significant challenges in "snitch localization," revealing that current models are inadequate in understanding adversarial spatiotemporal contexts fully.

Implications and Future Directions

The CATER dataset foregrounds an essential predicament in video understanding: models necessitate improved temporal reasoning capabilities to mimic human-like comprehension when actions and intended outcomes are disconnected over time. Future research might pursue building models that leverage end-to-end spatial and temporal dynamics comprehension, potentially incorporating causal reasoning frameworks.

Moreover, while the dataset is synthetic and thus detached from real-world complexities, it stands as a critical step for developing intermediate-level representations that could bridge the gap between low-level perception (e.g., object detection) and high-level understanding tasks (e.g., intention prediction). As video datasets continue to proliferate, converging insights from CATER with large-scale real-world datasets might establish balanced benchmarks that assess both scene recognition and dynamic reasoning effectively.

In summary, the CATER dataset surfaces critical challenges in achieving holistic video understanding, steering future developments towards integrating complex temporal dynamics into model architectures.