WHTMix: ウォルシュ・アダマール変換による効率的なステレオ深度推定
WHTMix: Efficient Stereo Depth Estimation via Walsh-Hadamard Token Mixing
ステレオトランスフォーマーの自己注意機構をデータ非依存のウォルシュ・アダマール変換に置き換え、計算量と遅延を大幅に削減しつつ精度を維持する手法を提案。
詳しい要約
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著者: Prathyush Sajith, Emadeldeen Hamdan, Ahmet Enis Cetin
分類: cs.CV, eess.IV, eess.SP
原文アブストラクト
Stereo depth estimation for driving, robotics and augmented reality must run at high resolution under tight latency budgets, yet in transformer-based matchers the global self-attention that aggregates scene context grows quadratically with the number of pixels and comes to dominate runtime. We show that the joint self-attention stage of a stereo transformer, whose role is to spread context across both views, can be replaced by a data-independent Walsh-Hadamard token mixer that mixes tokens globally in the transform domain at log-linear cost, while the data-dependent cross-attention that performs left-right correspondence is retained. On synthetic driving data the mixer matches the attention baseline in end-point error while reducing model compute by a factor of 2.46 and single-image inference latency by a factor of 2.65. A complexity analysis shows the benefit is governed by the ratio of sequence length to channel width, which explains why high-resolution stereo matching is a particularly favorable setting and why classification transformers are not; we confirm this token-to-channel scaling on non-stereo long-sequence benchmarks. Furthermore, we introduce a hybrid log-disparity loss function designed to up-weight small-disparity pixels corresponding to long-range objects. This approach reduces the error on distant objects without incurring any additional computational overhead.
