convnextv2.py convnextv2_sparse.py fcmae.py utils.py .gitmodules CODE_OF_CONDUCT.md CONTRIBUTING.md INSTALL.md LICENSE README.md TRAINING.md datasets.py engine_finetune.py engine_pretrain.py main_finetune.py main_pretrain.py optim_factory.py ...
Compared to the most recent work [46], which predicts future trajectories by stacking Transformer block (computation and memory expensive [15]), our method is parameter-efficient and achieves better perfor- mance. 3. Our Method Pedestrian trajectory prediction aims to p...
createBlock(&whileOp.getBefore(), {}, noTypes); rewriter.setInsertionPointToEnd(before); Value cond = genGetNextCall(rewriter, loc, iter, dimCoords, elemPtr); rewriter.create<scf::ConditionOp>(loc, cond, before->getArguments()); Block *after = rewriter.createBlock(&whileOp.getAfter(),...
PointBeV: A Sparse Approach to BeV Predictions Loick Chambon1,2, E´ loi Zablocki1, Mickae¨l Chen1, Florent Bartoccioni1, Patrick Pe´rez*3, Matthieu Cord1,2 1 Valeo.ai, Paris, France 2 Sorbonne Universite´, Paris, France 3 Kyutai, Paris, France Abstract Bird's-eye View...
The last feature map FL was obtained using the deformable feature extraction block. Finally, a feature map pyramid was utilized as the input for the sparse-to-dense matching module. 3.3 Sparse-to-dense matching Given an image pair (IA,IB)(IA,IB) to be matched, we first applied an off-...
Each thread block has 3 threads. 3.3. Implicit GEMM Dataflow Overview. A very recent advance in sparse convolution inference, SpConv v2 [39, 40], takes an alternative approach of implicit GEMM computation, which is originally de- signed for dense convolution on images in CUTLASS [18], cu...
input conv conv block conv-s2 dconv-s2 upsample linear softmax Figure 4: Illustrations of our submanifold sparse FCN (a) and U-Net (b) architectures. Dark blue boxes represents one or more "pre-activated" SSC(·, ·, 3) convolutions, which may have residual connections. Red boxes ...
The CA_Conv block within the CAFE module uses the following workflow: first, the input image undergoes convolution through the initial convolutional layer, where the kernel size of this convolutional layer can be adjusted according to the task requirements; second, the output of the convolutional la...