4 research outputs found
Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
Mixed-precision quantization (MPQ) suffers from time-consuming policy search
process (i.e., the bit-width assignment for each layer) on large-scale datasets
(e.g., ISLVRC-2012), which heavily limits its practicability in real-world
deployment scenarios. In this paper, we propose to search the effective MPQ
policy by using a small proxy dataset for the model trained on a large-scale
one. It breaks the routine that requires a consistent dataset at model training
and MPQ policy search time, which can improve the MPQ searching efficiency
significantly. However, the discrepant data distributions bring difficulties in
searching for such a transferable MPQ policy. Motivated by the observation that
quantization narrows the class margin and blurs the decision boundary, we
search the policy that guarantees a general and dataset-independent property:
discriminability of feature representations. Namely, we seek the policy that
can robustly keep the intra-class compactness and inter-class separation. Our
method offers several advantages, i.e., high proxy data utilization, no extra
hyper-parameter tuning for approximating the relationship between
full-precision and quantized model and high searching efficiency. We search
high-quality MPQ policies with the proxy dataset that has only 4% of the data
scale compared to the large-scale target dataset, achieving the same accuracy
as searching directly on the latter, and improving the MPQ searching efficiency
by up to 300 times
Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization
Exemplar-based colorization approaches rely on reference image to provide
plausible colors for target gray-scale image. The key and difficulty of
exemplar-based colorization is to establish an accurate correspondence between
these two images. Previous approaches have attempted to construct such a
correspondence but are faced with two obstacles. First, using luminance
channels for the calculation of correspondence is inaccurate. Second, the dense
correspondence they built introduces wrong matching results and increases the
computation burden. To address these two problems, we propose Semantic-Sparse
Colorization Network (SSCN) to transfer both the global image style and
detailed semantic-related colors to the gray-scale image in a coarse-to-fine
manner. Our network can perfectly balance the global and local colors while
alleviating the ambiguous matching problem. Experiments show that our method
outperforms existing methods in both quantitative and qualitative evaluation
and achieves state-of-the-art performance.Comment: Accepted by ECCV2022; 14 pages, 10 figure
REALY: Rethinking the Evaluation of 3D Face Reconstruction
The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and improving a 3D face reconstruction method. In this paper, we propose a novel evaluation approach with a new benchmark REALY, consists of 100 globally aligned face scans with accurate facial keypoints, high-quality region masks, and topology-consistent meshes. Our approach performs region-wise shape alignment and leads to more accurate, bidirectional correspondences during computing the shape errors. The fine-grained, region-wise evaluation results provide us detailed understandings about the performance of state-of-the-art 3D face reconstruction methods. For example, our experiments on single-image based reconstruction methods reveal that DECA performs the best on nose regions, while GANFit performs better on cheek regions. Besides, a new and high-quality 3DMM basis, HIFI3D ++, is further derived using the same procedure as we construct REALY to align and retopologize several 3D face datasets. We will release REALY, HIFI3D ++, and our new evaluation pipeline at https://realy3dface.com.</p