Wearable action anticipation systems must remain reliable despite missing frames, masking, and sensor noise, yet existing egocentric anticipation methods largely assume clean observations. We identify two complementary failure modes under temporal corruption: unreliable temporal evidence during encoding and implausible, low-support verb-noun compositions during decoding. We address them with a lightweight framework combining Temporal Reliability Suppression (TRS) and Robust Verb-Noun Graph (RVG) decoding. TRS predicts a per-frame suppression score from the projected input embedding and uses it as a learned key-side attention penalty at every encoder block and to derive reliability-weighted temporal pooling. RVG re-ranks verb-noun pairs using a PMI-based compatibility graph constructed from training labels. Under corruption-augmented training, TRS+CA+RVG reaches 29.1% average corrupted accuracy and 88.2% relative robustness across six corruptions, including three mechanisms absent during training, while reducing rare verb-noun predictions from 15.4% to 1.1%. Multi-seed and diagnostic experiments show that TRS responds to synthetic masking; shuffled-graph and frequency-only controls further indicate that RVG gains depend on genuine pairwise compatibility rather than marginal-frequency effects alone.
Figures & tables
Figure 1: Overview and complementarity of TRS+RVG. Temporal corruption affects egocentric anticipation at two levels: TRS suppresses unreliable temporal evidence during encoding and pooling, whereas RVG corrects low-support verb–noun compositions at decision time.
Method
Cl
Mask
Noise
Comb
Drop
Jitter
Freeze
AvgC
RR
Clean baseline
33.6
9.4
23.4
6.8
24.4
27.0
32.0
20.5
61.0
FrameDrop
32.7
9.6
23.4
7.6
23.7
26.1
30.5
20.1
61.6
FrameDrop + RVG
36.5
12.7
30.6
11.3
28.5
29.4
35.2
24.6
67.5
Corruption-Augmented (CA)
28.4
22.1
19.7
20.7
22.0
25.5
25.5
22.6
79.6
CA + RVG
31.3
25.2
25.1
25.5
24.4
28.1
29.7
26.3
84.0
TRS + CA
29.5
23.6
22.9
24.0
24.3
26.9
27.5
24.9
84.4
Table 1: EGTEA top-1 action accuracy (%), high severity. Drop, Jitter, and Freeze use mechanisms absent from training. AvgC averages the six corruptions; RR =100× AvgC/Cl. FrameDrop is a temporal-augmentation baseline [ 26 ] . Control rows were run on Mask, Noise, and Combined only. Bold : best per column among the full methods.
Figure 2: EGTEA robustness profile (top-1 action accuracy %; red: best per column). TRS+CA+RVG leads on Mask, Combined, and Jitter; FrameDrop+RVG leads on Noise, Drop, and Freeze.
Method
Cl
Mask
Noise
Comb
Avg
Clean baseline
8.9
0.5
5.8
0.5
2.3
Corruption-Augmented (CA)
8.2
6.9
6.2
6.9
6.7
CA + RVG
9.4
9.0
8.3
8.7
8.7
TRS + CA
9.4
8.4
6.3
6.7
7.1
TRS + CA + RVG
10.2
8.1
8.5
8.0
8.2
TRS + CA + Shuffled
9.4
7.7
5.9
6.7
6.8
Table 2: EK100 top-1 action accuracy (%). Avg averages Mask, Noise, and Combined. λ=2 is transferred from EGTEA without re-tuning. Bold : best per column.
Figure 3: TRS suppression scores on an EGTEA clip. Scores rise on masked frames t3 – t5 and remain low elsewhere.
Figure 4: Qualitative RVG correction: graph re-scoring replaces the low-support open knife prediction with cut knife .
Per-stream
By GT rarity
Method
Verb
Noun
Action
All
Seen-GT
Rare-GT
TRS + CA
36.6
37.8
22.5
22.2
22.8
0 6.4
TRS + CA + RVG
37.9
37.1
27.4
28.3
28.9
15.4
Table 4: Left: verb, noun, and joint accuracy under EGTEA Combined corruption. Right: accuracy on seen versus rare ground-truth pairs (rare = training count <30 , n=78 of 2022).