mlsanityData to 14 Oct 2026

XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation

arXiv 2208.14655 ↗First posted 31 Aug 2022eess.IV cs.CVcited by 3

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Abstract (arXiv)

We propose a lightweight, single image super-resolution network for mobile devices, named XCAT. XCAT introduces Heterogeneous Group Convolution Blocks with Cross Concatenations (HXBlock). The heterogeneous split of the input channels to the group convolution blocks reduces the number of operations, and cross concatenation allows for information flow between the intermediate input tensors of cascaded HXBlocks. Cross concatenations inside HXBlocks can also avoid using more expensive operations like 1x1 convolutions. To further prev ent expensive tensor copy operations, XCAT utilizes non-trainable convolution kernels to apply up sampling operations. Designed with integer quantization in mind, XCAT also utilizes several techniques on training, like intensity-based data augmentation. Integer quantized XCAT operates in real time on Mali-G71 MP2 GPU with 320ms, and on Synaptics Dolphin NPU with 30ms (NCHW) and 8.8ms (NHWC), suitable for real-time applications.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 30 of its 42 references that are in the map. First posted on arXiv 31 Aug 2022.

Builds on

30 of 42 references.

References · 30

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18 more
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  18. 2022IMDeception: Grouped Information Distilling Super-Resolution Networkcited by 3

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Citing papers per year: 2022: 1; 2023: 2.

3 papers.

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  1. 2026QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
  2. 2026Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training
  3. 2022Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
  4. 2022Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
  5. 2024Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning
  6. 2023QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms
  7. 20242DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
  8. 2024AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution