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arxiv: 2604.16240 · v1 · submitted 2026-04-17 · 💻 cs.CV

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CollideNet: Hierarchical Multi-scale Video Representation Learning with Disentanglement for Time-To-Collision Forecasting

Ali Etemad, Michael Greenspan, Nishq Poorav Desai

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Pith reviewed 2026-05-10 08:32 UTC · model grok-4.3

classification 💻 cs.CV
keywords collidenetvideoforecastingmulti-scalecomponentsdisentanglementhierarchicalmethod
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The pith

CollideNet achieves state-of-the-art time-to-collision forecasting on three public datasets by combining multi-scale spatial aggregation with temporal disentanglement of trend and seasonality in a hierarchical transformer.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents CollideNet, a new neural network design for predicting how soon a collision might happen based on video input. It handles video by looking at each frame at several different levels of detail at once, rather than just one resolution. For the time aspect, it separates the video signal into parts that change over time in different ways: the overall trend, repeating seasonal patterns, and irregular non-stationary changes. This separation is intended to help the model focus on the relevant motion cues for collision risk. The authors test the system on standard video datasets used for this task and report better accuracy than previous methods. They also check how well the model works when trained on one dataset and tested on another to see if it generalizes.

Core claim

Our method achieves state-of-the-art performance in comparison to prior works on three commonly used public datasets, setting a new state-of-the-art by a considerable margin.

Load-bearing premise

The assumption that the proposed hierarchical multi-scale spatial aggregation combined with temporal disentanglement of non-stationarity, trend, and seasonality will generalize beyond the three evaluated datasets and produce reliable TTC forecasts in unseen real-world conditions.

Figures

Figures reproduced from arXiv: 2604.16240 by Ali Etemad, Michael Greenspan, Nishq Poorav Desai.

Figure 1
Figure 1. Figure 1: TTC forecasting task using multi-scale spatial and temporal information. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: The correlation score cj quantifies the relationship between Q + i and Kj . Output Yi at the current segment i is determined by using the similarity between Qi−1 from the previous segment i − 1 and the key Kj as the weight for the value Vj+1 in the aggregation process. 4 Experiments Dataset and evaluation metrics. We use three publicly available datasets, namely DAD [9], CCD [6], and DoTA [63]. These datas… view at source ↗
read the original abstract

Time-to-Collision (TTC) forecasting is a critical task in collision prevention, requiring precise temporal prediction and comprehending both local and global patterns encapsulated in a video, both spatially and temporally. To address the multi-scale nature of video, we introduce a novel spatiotemporal hierarchical transformer-based architecture called CollideNet, specifically catered for effective TTC forecasting. In the spatial stream, CollideNet aggregates information for each video frame simultaneously at multiple resolutions. In the temporal stream, along with multi-scale feature encoding, CollideNet also disentangles the non-stationarity, trend, and seasonality components. Our method achieves state-of-the-art performance in comparison to prior works on three commonly used public datasets, setting a new state-of-the-art by a considerable margin. We conduct cross-dataset evaluations to analyze the generalization capabilities of our method, and visualize the effects of disentanglement of the trend and seasonality components of the video data. We release our code at https://github.com/DeSinister/CollideNet/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper proposes CollideNet, a hierarchical multi-scale spatiotemporal transformer architecture for time-to-collision (TTC) forecasting. The spatial stream aggregates multi-resolution information per video frame, while the temporal stream encodes multi-scale features and disentangles non-stationarity, trend, and seasonality components. The central claim is state-of-the-art performance on three public datasets, supported by cross-dataset evaluations, visualizations of the disentanglement, and public code release.

Significance. If the reported margins hold under the full experimental protocol, the work advances video representation learning for safety-critical applications such as collision avoidance. The combination of hierarchical spatial aggregation and explicit temporal disentanglement offers a structured approach to multi-scale spatiotemporal modeling, and the cross-dataset tests plus code release provide concrete support for generalization and reproducibility.

minor comments (2)
  1. The abstract states results on three public datasets but does not name them; adding the dataset names would improve immediate readability.
  2. Figure captions describing the disentanglement visualizations should explicitly reference the non-stationarity, trend, and seasonality components shown.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents CollideNet as an empirical neural architecture for TTC forecasting, with claims resting on experimental SOTA results across three public datasets plus cross-dataset generalization tests. No equations, derivations, or first-principles predictions appear in the provided text; the architecture (hierarchical spatial aggregation and temporal disentanglement of non-stationarity/trend/seasonality) is described as a design choice motivated by video properties rather than any self-referential definition or fitted input relabeled as output. The central performance margins are therefore not forced by construction and remain independently verifiable via the released code.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities beyond the standard assumption that video data contains separable spatial scales and temporal components amenable to transformer processing and decomposition.

pith-pipeline@v0.9.0 · 5481 in / 1175 out tokens · 21195 ms · 2026-05-10T08:32:23.075493+00:00 · methodology

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Reference graph

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    Zhong, Y., Chen, X., Hu, Y., Tang, P., Ren, F.: Bidirectional Spatio-Temporal Feature Learning With Multiscale Evaluation for Video Anomaly Detection. IEEE Transactions on Circuits and Systems for Video Technology32, 8285–8296 (2022) CollideNet: Hierarchical Multi-scale Video Learning for TTC Forecasting 17 Appendix A.1. Release details The code to implem...

    2022