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Found 8 papers, 6 papers with code
An Experimental Study on Exploring Strong Lightweight Vision Transformers via Masked Image Modeling Pre-Training
In this paper, we question if the \textit{extremely simple} lightweight ViTs' fine-tuning performance can also benefit from this pre-training paradigm, which is considerably less studied yet in contrast to the well-established lightweight architecture design methodology.
DSPNet: Towards Slimmable Pretrained Networks based on Discriminative Self-supervised Learning
However, when facing various resource budgets in real-world applications, it costs a huge computation burden to pretrain multiple networks of various sizes one by one.
Narrowing the Gap: Improved Detector Training with Noisy Location Annotations
Experiments for both synthesized and real-world scenarios consistently demonstrate the effectiveness of our approach, e. g., our method increases the degraded performance of the FCOS detector from 33. 6% AP to 35. 6% AP on COCO.
A Closer Look at Self-Supervised Lightweight Vision Transformers
We also point out some defects of such pre-training, e. g., failing to benefit from large-scale pre-training data and showing inferior performance on data-insufficient downstream tasks.
Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation
Open-vocabulary object detection aims to detect novel object categories beyond the training set.
Ranked #30 on
Open Vocabulary Object Detection
on MSCOCO
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking
In this paper, we show the existence of universal perturbations that can enable the targeted attack, e. g., forcing a tracker to follow the ground-truth trajectory with specified offsets, to be video-agnostic and free from inference in a network.
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling
However, the most suitable positions for inferring different targets, i. e., the object category and boundaries, are generally different.
RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
To reciprocate these two tasks, we design a two-stream structure to learn features on both the object level (i. e., bounding boxes) and the pixel level (i. e., instance masks) jointly.
Ranked #95 on
Instance Segmentation
on COCO test-dev
