Search Results for author:
Found 11 papers, 9 papers with code
Video Object Segmentation using Tracked Object Proposals
We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 challenge.
ASAP: Architecture Search, Anneal and Prune
In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations.
XNAS: Neural Architecture Search with Expert Advice
This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice.
Compact Network Training for Person ReID
Furthermore, we show the representation power of our ReID network via SotA results on a different task of multi-object tracking.
Ranked #16 on
Person Re-Identification
on Market-1501
(Rank-1 metric)
Graph Embedded Pose Clustering for Anomaly Detection
We propose a new method for anomaly detection of human actions.
Ranked #7 on
Video Anomaly Detection
on HR-UBnormal
Knapsack Pruning with Inner Distillation
Through this we produce compact architectures with the same FLOPs as EfficientNet-B0 and MobileNetV3 but with higher accuracy, by $1\%$ and $0. 3\%$ respectively on ImageNet, and faster runtime on GPU.
Ranked #3 on
Network Pruning
on ImageNet
TResNet: High Performance GPU-Dedicated Architecture
In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
Ranked #7 on
Fine-Grained Image Classification
on Oxford 102 Flowers
(using extra training data)
Asymmetric Loss For Multi-Label Classification
In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples.
Ranked #4 on
Multi-Label Classification
on NUS-WIDE
Semantic Diversity Learning for Zero-Shot Multi-label Classification
We argue that using a single embedding vector to represent an image, as commonly practiced, is not sufficient to rank both relevant seen and unseen labels accurately.
Ranked #3 on
Multi-label zero-shot learning
on Open Images V4
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
We propose to estimate the class distribution using a dedicated temporary model, and we show its improved efficiency over a naive estimation computed using the dataset's partial annotations.
Ranked #1 on
Multi-Label Classification
on OpenImages-v6
Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
Hence, many of the optimizations and tricks that have been successful in natural language generation may not be effective for code tasks.
