RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth Estimation
We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy consumption. The proposed method is based on repetition-reduction (RR) blocks. The RR blocks consist of the set of repeated convolutions and the residual connection layer that take place of the pointwise reduction layer with linear connection to the decoder. The RRNet help reduce memory usage and power consumption in the residual connections to the decoder layers. RRNet consumes approximately 3.84 times less energy and 3.06 times less meory and is approaximately 2.21 times faster, without increasing the demand on hardware resource relative to the baseline network (Godard et al, CVPR'17), outperforming current state-of-the-art lightweight architectures such as SqueezeNet, ShuffleNet, MobileNet and PyDNet.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderDepth EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as a means of enabling privacy-preserving D…
Privacy PreservingRRNet: Configurable Real-Time Video Enhancement with Arbitrary Local Lighting Variations
With the growing demand for real-time video enhancement in live applications, existing methods often struggle to balance speed and effective exposure control, particularly under uneven lighting. We introduce RRNet (Rende…
Video EnhancementDRRNet: Macro-Micro Feature Fusion and Dual Reverse Refinement for Camouflaged Object Detection
The core challenge in Camouflage Object Detection (COD) lies in the indistinguishable similarity between targets and backgrounds in terms of color, texture, and shape. This causes existing methods to either lose edge det…
object-detectionObject DetectionScene UnderstandingUncertainty-Guided Inference-Time Depth Adaptation for Transformer-Based Visual Tracking
Transformer-based single-object trackers achieve state-of-the-art accuracy but rely on fixed-depth inference, executing the full encoder--decoder stack for every frame regardless of visual complexity, thereby incurring u…
Knowledge DistillationVisual TrackingOvercoming Decoder Inconsistencies in Whisper for Dravidian and Low-Resource Languages
Multilingual ASR models such as Whisper perform well on high-resource languages but exhibit substantially higher Word Error Rates (WER) for Dravidian languages compared to Indo-Aryan ones. Through linguistic and dataset …