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Found 12 papers, 4 papers with code
Transition Rate Scheduling for Quantization-Aware Training
It is thus difficult to control the degree of changes for quantized weights by scheduling the LR manually.
AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search
To address this issue, we propose AZ-NAS, a novel approach that leverages the ensemble of various zero-cost proxies to enhance the correlation between a predicted ranking of networks and the ground truth substantially in terms of the performance.
RankMixup: Ranking-Based Mixup Training for Network Calibration
In this paper, we present RankMixup, a novel mixup-based framework alleviating the problem of the mixture of labels for network calibration.
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
We introduce a CISS framework that alleviates the forgetting problem and facilitates learning novel classes effectively.
Class-Incremental Semantic Segmentation
Knowledge Distillation
OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Search
To this end, we introduce a novel normalization layer, dubbed ProtoNorm, that calibrates features from pedestrian proposals, while considering a long-tail distribution of person IDs, enabling L2 normalized person representations to be discriminative.
Video-based Person Re-identification with Spatial and Temporal Memory Networks
Spatial and temporal distractors in person videos, such as background clutter and partial occlusions over frames, respectively, make this task much more challenging than image-based person reID.
Ranked #8 on
Person Re-Identification
on MARS
Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences
We address the problem of visible-infrared person re-identification (VI-reID), that is, retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal setting.
Distance-aware Quantization
This alleviates the gradient mismatch, but causes a quantizer gap problem.
Network Quantization with Element-wise Gradient Scaling
Network quantization aims at reducing bit-widths of weights and/or activations, particularly important for implementing deep neural networks with limited hardware resources.
Learning with Privileged Information for Efficient Image Super-Resolution
The student and the decoder in the teacher, having the same network architecture as FSRCNN, try to reconstruct HR images.
Learning Semantic Correspondence Exploiting an Object-level Prior
We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category.
SFNet: Learning Object-aware Semantic Correspondence
We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category.
