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Frozen pre-trained video features with attentive probes and two-stage fusion achieve second place on the EPIC-KITCHENS-100 action anticipation challenge.

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2026-06-28 18:54 UTC pith:RLY7BCHN

load-bearing objection This is a clean but incremental competition report that gets 2nd on EK-100 by probing frozen V-JEPA features plus simple score fusion, with the main value being the empirical efficiency result.

arxiv 2606.00662 v1 pith:RLY7BCHN submitted 2026-05-30 cs.CV

TAP-JEPA: Frozen Future-Latent Probing and Two-Stage Score Fusion for EPIC-KITCHENS-100 Action Anticipation

Chaoyang Wang , Lexuan Xu This is my paper
classification cs.CV
keywords action anticipationegocentric videofrozen featuresattentive probesscore fusionfuture latent predictionEPIC-KITCHENS-100
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper shows that competitive action anticipation performance can be reached by keeping a large video model completely frozen and training only small task-specific heads on its outputs. It extracts tokens from an observed egocentric clip using a pre-trained encoder, then uses the model's own latent predictor to generate tokens that represent the near future. Separate attentive probes with verb-specific, noun-specific, and action-pair queries read from both the observed and predicted tokens. A two-stage averaging process first combines eight probe replicas trained in each epoch and then merges results from later epochs with field-dependent weights to produce the final predictions.

Core claim

The central claim is that attentive probes applied to frozen V-JEPA 2.1 visible pre-action tokens and estimated near-future tokens, together with two-stage score fusion, produce an anticipation model that reaches 27.91 percent overall action Mean Top-5 Recall on the official open-testing leaderboard, ranking second and 0.04 points behind the leader, all without any fine-tuning of the video backbone.

What carries the argument

Attentive probing of frozen V-JEPA visible and future-latent tokens combined with two-stage score fusion

Load-bearing premise

The pre-trained V-JEPA encoder and latent predictor already capture sufficient information about near-future video content so that task-specific probes and fusion suffice.

What would settle it

An experiment that fine-tunes the V-JEPA backbone on the same anticipation data and obtains substantially higher MT5R scores than the frozen version would show that the frozen features are insufficient.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The pre-trained latent predictor already encodes useful near-future signals for egocentric kitchen activities.
  • Expanding supervised training to include most of the validation split improves the final test performance.
  • Averaging multiple independently initialized probe replicas within epochs and then merging selected later epochs with per-field weights raises the overall score.
  • pith_inferences=[

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

0 major / 2 minor

Summary. The manuscript presents TAP-JEPA, a runner-up entry to the EPIC-KITCHENS-100 Action Anticipation Challenge. It extracts visible pre-action tokens and estimates near-future tokens using a frozen V-JEPA 2.1 ViT-G/384 encoder and latent predictor, feeds both into task-specific attentive probes for verbs, nouns, and actions, and applies two-stage score fusion (intra-epoch averaging of eight probe replicas followed by epoch 12-20 merging with field-dependent weights). Training uses the official training split plus most of validation; the method yields 27.91% overall action Mean Top-5 Recall on the official open-testing leaderboard, placing second.

Significance. If the reported leaderboard result holds, the work provides concrete evidence that competitive egocentric action anticipation is attainable by attentive probing of frozen pre-trained video models that include latent future prediction, without any backbone fine-tuning. The external public leaderboard supplies independent empirical grounding rather than an internally fitted metric, strengthening the demonstration that V-JEPA 2.1 tokens already encode sufficient near-future information for this task.

minor comments (2)
  1. [Abstract] Abstract: the two-stage fusion procedure is described at a high level; a concrete example or pseudocode for how the field-dependent weights are chosen from the validation subset would improve reproducibility.
  2. [Abstract] Abstract: the phrase 'most of the validation split' is imprecise; stating the exact number of reserved validation clips would allow readers to assess the scale of the sanity-check hold-out.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the detailed summary of our submission and for the positive assessment of its significance. The recommendation of minor revision is noted. No major comments were listed in the report, so we have no specific points requiring response or manuscript changes.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper is an empirical challenge submission that reports leaderboard performance (27.91% MT5R) obtained by training task-specific attentive probes on frozen V-JEPA 2.1 encoder and latent-predictor tokens followed by two-stage fusion; the result is measured on an independent public test set rather than any internal fitted quantity or self-referential definition. No equations, derivations, ansatzes, or load-bearing self-citations appear in the provided text that would reduce the reported outcome to its inputs by construction. The central claim therefore remains externally grounded and self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on the transferability of V-JEPA features and the value added by the two-stage fusion; no new physical entities are postulated and the only free parameters are the learned probe weights and the fusion coefficients tuned on validation data.

free parameters (1)
  • field-dependent fusion weights
    Weights used in the second stage of score merging are chosen to optimize the final leaderboard metric.
axioms (1)
  • domain assumption Frozen V-JEPA 2.1 features plus its latent predictor already encode the information needed for verb/noun/action anticipation
    The entire pipeline is built on this transfer assumption without backbone fine-tuning.

pith-pipeline@v0.9.1-grok · 5767 in / 1386 out tokens · 38176 ms · 2026-06-28T18:54:16.879355+00:00 · methodology

0 comments
read the original abstract

This report presents TAP-JEPA, our runner-up submission to the EPIC-KITCHENS-100 (EK-100) Action Anticipation Challenge at EgoVis 2026. The task is to anticipate the next verb, noun, and verb-noun action from an egocentric clip that ends before the target action begins. Instead of fine-tuning a large video backbone, TAP-JEPA builds a compact anticipation model on frozen V-JEPA 2.1 features: a ViT-G/384 encoder extracts visible pre-action tokens, the pre-trained latent predictor estimates near-future tokens from the observed context, and both token groups are fused by attentive probes with task-specific queries for verbs, nouns, and action pairs. For the final submission, we expand supervised training with the official training split and most of the validation split, reserving a small subset for sanity checks and qualitative inspection, and adopt a two-stage score fusion that first averages eight independently initialized probe replicas within each epoch and then merges candidates from epochs 12-20 with field-dependent weights. On the official open-testing leaderboard, our sunshinesky entry achieves 27.91 percent overall action Mean Top-5 Recall (MT5R), ranking second and only 0.04 percentage points behind the top score.

Figures

Figures reproduced from arXiv: 2606.00662 by Chaoyang Wang, Lexuan Xu.

Figure 1
Figure 1. Figure 1: TAP-JEPA inference pipeline. A frozen V-JEPA 2.1 encoder-predictor first turns the observed pre-action clip into visible-context [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Validation failure example. The onion context is visible [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

discussion (0)

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Reference graph

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