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Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos
Gautam Singh, Yi-Fu Wu, Sungjin Ahn
TL;DR
Unsupervised object-centric learning has struggled to handle complex and naturalistic videos, limiting its application beyond simple or synthetic scenes. STEVE combines recurrent temporal slot updates with a transformer-based slot decoder and simple reconstruction-based learning, and it significantly outperforms prior state-of-the-art baselines on complex naturalistic videos. The method retains a minimal architecture but requires more memory as image resolution increases.
Problem
Unsupervised object-centric learning lacks methods that work well on complex and naturalistic videos, despite the importance of applying it to large-scale visual data.
Method
STEVE combines a CNN image encoder, recurrent temporal slot encoder, and transformer-based slot decoder, trained without weak supervision using reconstruction objectives.
Results
STEVE significantly outperforms previous state-of-the-art baselines on various complex and naturalistic videos, while temporal information is essential alongside the slot-transformer decoder.
Takeaways & Limitations
The results support using powerful transformer-based reconstruction together with temporal information for unsupervised object-centric video learning.
Takeaways & Limitations
STEVE has higher memory demand at larger image resolutions because transformer memory complexity is quadratic.
Abstract
from arXiv · showhide
Unsupervised object-centric learning aims to represent the modular, compositional, and causal structure of a scene as a set of object representations and thereby promises to resolve many critical limitations of traditional single-vector representations such as poor systematic generalization. Although there have been many remarkable advances in recent years, one of the most critical problems in this direction has been that previous methods work only with simple and synthetic scenes but not with complex and naturalistic images or videos. In this paper, we propose STEVE, an unsupervised model for object-centric learning in videos. Our proposed model makes a significant advancement by demonstrating its effectiveness on various complex and naturalistic videos unprecedented in this line of research. Interestingly, this is achieved by neither adding complexity to the model architecture nor introducing a new objective or weak supervision. Rather, it is achieved by a surprisingly simple architecture that uses a transformer-based image decoder conditioned on slots and the learning objective is simply to reconstruct the observation. Our experiment results on various complex and naturalistic videos show significant improvements compared to the previous state-of-the-art.
1 Introduction
Object-centric learning seeks modular representations of scenes, but unsupervised methods had not worked well on complex naturalistic videos. STEVE addresses this gap with a simple transformer-decoder architecture and temporal modeling, achieving strong results on such videos.
- Object-centric learning represents conceptual entities such as objects with separate vectors and their relations.
- Applying unsupervised object-centric learning to complex natural images and videos remains a critical challenge.
- Previous methods generally worked only on toy or synthetic scenes or required supervision such as optical flow or first-frame annotations.
- STEVE demonstrates unsupervised object-centric learning on various complex and naturalistic videos without substantially increasing architectural complexity or introducing a new objective or weak supervision.
- STEVE combines the SLATE transformer-based slot decoder with a standard slot-level recurrence model, and significantly outperforms previous state-of-the-art baselines.
- SLATE alone can handle some complex images, but STEVE improves frame segmentation while also maintaining consistent slot tracking across videos.
2 Preliminaries
Object-centric auto-encoding extracts slots from an image and reconstructs it, using competition among slots to encourage object-like representations. Traditional mixture-based decoders reconstruct images by combining slot-specific outputs but struggle with visually complex natural scenes, motivating transformer-based decoding.
- A slot encoder extracts a set of object representations from an image, and a decoder reconstructs the image from those slots.
- Competition among slots creates an information bottleneck that encourages each slot to attend to a meaningful conceptual entity.
- Mixture-based Decoder: Mixture-based decoders independently decode each slot into an object image and alpha mask, then combine the outputs into a full reconstruction.
- Mixture-based Decoder: The traditional mixture-based reconstruction objective uses squared error between the input and reconstructed image.
- Mixture-based Decoder: Mixture-based decoders have not been successful on scenes with high visual complexity such as natural images.
- Autoregressive Slot-Transformer Decoder: SLATE replaces mixture decoding with an autoregressive transformer conditioned on slots to improve slot interaction and reconstruction quality for complex images.
3 STEVE: Slot Transformer for Videos
STEVE extends slot-transformer decoding to videos through a minimal architecture combining image encoding, recurrent temporal slot updates, and transformer reconstruction. It predicts discrete image tokens autoregressively from temporally updated slots.
- STEVE combines a CNN-based image encoder, a recurrent slot encoder, and the SLATE slot-transformer decoder.
- Recurrent Slot Encoder: The recurrent slot encoder updates slot representations at each time step using the previous slots and the current input frame.
- Recurrent Slot Encoder: The model maintains N slots per frame, with slots expected to represent and track objects consistently over time.
- Slot-Transformer Reconstruction: Each frame is converted into discrete tokens by a discrete VAE, and the slot-transformer decoder predicts those tokens autoregressively from the slots.
- Slot-Transformer Reconstruction: The transformer is trained by minimizing cross-entropy on its token predictions, while the complete model combines this loss with the discrete-VAE reconstruction loss.
4 Related Work
Prior unsupervised object-centric work studies static images and videos, commonly using reconstruction with slot-based encoders and mixture-based decoders. Video methods additionally address tracking, localization, and temporal scene decomposition.
- Images: Unsupervised object-centric image models commonly learn through reconstruction and adopt mixture-based decoders.
- Videos: Unsupervised video methods combine recurrent slot-based encoders with reconstruction objectives for video segmentation and tracking.
- Videos: Several video approaches use bounding boxes for tracking, while related methods learn to localize objects through other mechanisms.
5 Experiments
STEVE is evaluated across procedurally generated and natural datasets for image and video segmentation, generalization, decoder design, computational requirements, camera motion, and static objects. It generally outperforms mixture-based and prior temporal baselines, especially on textured and natural scenes, while incurring higher memory demands at larger image sizes.
- Evaluation Setup: STEVE is evaluated on eight datasets, including six procedurally generated datasets and two natural datasets, Traffic and Aquarium.Five of the datasets are introduced in this work.
- Image Segmentation: STEVE significantly outperforms mixture-based baselines on textured datasets, achieving higher FG-ARI in 5 of 6 datasets.The baselines often split complex images into fixed patches rather than meaningful object regions.
- Image Segmentation: Training on videos improves image segmentation over SLATE, with STEVE performing consistently better across all datasets.The gap is especially large on MOVi-Tex.
- Video Segmentation: STEVE significantly outperforms baselines on video segmentation in 5 of 6 datasets, while performance declines with longer test videos but deteriorates less from length 6 to 24 on CATERTex, MOVi-D, and MOVi-E.CATER is the exception where baselines perform comparably or slightly better.
- Generalization and Natural Scenes: STEVE generalizes well to more objects and unseen objects and materials, except that novel textures reduce performance on MOVi-Tex.The authors associate object-count generalization with recurrent slot modularity and texture generalization with the backbone CNN.
- Generalization and Natural Scenes: On natural videos, STEVE approximately doubles FG-ARI on Traffic and triples it on Aquarium relative to the baselines.The Aquarium dataset contains strongly camouflaged fish, yet STEVE handles the segmentation challenge effectively.
- Analysis: Larger discrete-VAE patches worsen segmentation, whereas increasing CNN decoder capacity produces only similar or slightly higher baseline performance and remains below STEVE.The results support the importance of an expressive autoregressive transformer and favor smaller patches, although smaller than 4 × 4 patches were not tested because of memory costs.
- Analysis: STEVE achieves FG-ARI scores of 47.67 and 52.15 on MOVi-D and MOVi-E, respectively, compared with SAVi scores of 19.4 and 2.7 using optical-flow supervision.The model remains effective when static objects are present and performs slightly better with camera motion in MOVi-E than in MOVi-D.
6 Conclusion
The paper presents STEVE as a simple unsupervised model that handles complex and naturalistic videos without supervision, while highlighting temporal information as essential for video learning. It also identifies open questions about whether reconstruction alone captures objectness and how more advanced architectures or larger datasets might improve results.
- STEVE handles various complex and naturalistic videos without supervision while significantly outperforming previous state-of-the-art baselines.
- Temporal information is essential alongside the slot-transformer decoder for dealing with complex videos.
- The results jointly support using powerful reconstruction decoders for unsupervised object-centric learning rather than traditional weak mixture decoders.
- The paper leaves open whether accurate reconstruction from slots is sufficient to capture objectness.
- Future work could explore more advanced architectures and apply the method to large-scale datasets.
A Datasets
The evaluation uses video datasets generated or assembled to include more complex visual conditions, including textured backgrounds, complex shapes, and out-of-distribution object counts.
- MOVi-Solid was generated with Kubric using textured backgrounds and more complex shapes than the original MOVi dataset.
- The evaluation includes test sets with either 3-10 training-range objects or 11-12 out-of-distribution objects.
B STEVE: Architecture Details
STEVE combines a recurrent slot encoder with a discrete VAE and transformer decoder. Slots attend to frame features, interact recurrently, and reconstruct discretized image tokens.
- Recurrent Slot Encoder: The recurrent slot encoder initializes Gaussian slots, encodes each frame with a CNN, and updates slots through attention to spatially embedded features.The feature map is flattened into spatial features, augmented with positional embeddings, and processed by a two-layer MLP.
- Recurrent Slot Encoder: Attention-weighted feature summaries update each slot through a GRU, followed by an MLP, LayerNorm, and slot interaction transformer.The model performs two attention-and-update iterations per frame and uses pre-interaction slots for reconstruction.
- Discrete VAE: The discrete VAE divides each image into 4 × 4 patches and encodes them as tokens from a vocabulary of 4096 entries.The encoder produces a discrete feature map whose cells contain sampled categorical tokens.
- Discrete VAE: The discrete VAE decoder reconstructs an H × W × 3 image from one-hot token features, trained with squared-error image reconstruction.The decoder is implemented as a convolutional neural network.
- Transformer Decoder: A slot-transformer decoder predicts the frame’s discrete token sequence autoregressively from slots, using learned positional embeddings and cross-entropy training.Gumbel-Softmax enables differentiable discrete sampling during training, while hard sampling generates transformer targets.
B.1 Computational Requirements
The paper compares STEVE and SAVi under matched batch size and episode length to assess their computational requirements. The comparison isolates the decoder difference between the models.
- Computational Comparison: STEVE and SAVi are compared using the same batch size and episode length, with STEVE using transformer reconstruction and SAVi using mixture-based reconstruction.The matched settings use batch size 24 and episode length 3.
C Additional Results.
Additional results evaluate STEVE’s unsupervised video segmentation qualitatively across synthetic and naturalistic datasets. The supplementary figures include baseline comparisons, out-of-distribution evaluation, and extra samples.
- Qualitative Segmentation: Qualitative MOVi-D results compare STEVE’s predicted segments with SAVi and OP3 against the input video and true segmentation.The figure organizes rows as input, ground-truth segmentation, STEVE predictions, and baseline predictions.
- Out-of-Distribution Evaluation: SAVi is evaluated for out-of-distribution generalization to unseen object counts and materials using FG-ARI on videos of length 6.The reported metric is FG-ARI in percent.
- Additional Samples: Additional unsupervised video-segmentation samples are shown for MOVi-E and MOVi-Tex.These figures provide supplementary qualitative examples.
- Additional Samples: Additional unsupervised video-segmentation samples are shown for CATERTex, MOVi-D, MOVi-Solid, YouTube Traffic, and YouTube Aquarium.The figures extend qualitative coverage across these datasets and naturalistic videos.
D Additional Related Work
Related work addresses unsupervised video object segmentation through motion cues, RGB-only models trained with flow, and mask propagation. These approaches differ in their deployment requirements and supervision assumptions.
- Motion Cues: Motion-based methods use optical flow as an unsupervised signal for identifying independently moving objects.Such methods include approaches that estimate optical flow without supervision.
- Motion Cues: Some RGB-only methods handle naturalistic videos but rely primarily on optical flow during training, which can be problematic when flow is absent, such as with static objects.STEVE is trained using only RGB video frames.
- Segmentation Propagation: Unsupervised mask-propagation methods can handle natural videos but still require ground-truth segmentation or bounding boxes in the first frame for tracking.They learn to propagate masks rather than fully discovering objects without first-frame annotations.
E Additional Experiment Details
The experiments examine how temporal context and slot-count adjustment are handled when evaluating segmentation and out-of-distribution generalization. They also state societal considerations for future applications of the work.
- Temporal context: The segmentation of the final frame is evaluated as a function of the number of preceding frames, using Image FG-ARI across k ∈ {0, . . . , T−1}.Videos have length T = 7, and k ranges from zero to six preceding frames.
- More-object generalization: Out-of-distribution testing increases the number of slots by the difference between the test and training sets’ maximum object counts.
- Societal considerations: Future applications should avoid malicious surveillance uses and minimize the environmental impact of training larger transformers.