Search Results for author:
Found 11 papers, 5 papers with code
Bridge the Points: Graph-based Few-shot Segment Anything Semantically
Another subsequent Point-Mask Clustering module aligns the granularity of masks and selected points as a directed graph, based on mask coverage over points.
Ranked #2 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Background Adaptation with Residual Modeling for Exemplar-Free Class-Incremental Semantic Segmentation
For this special challenge, we designed a novel background adaptation mechanism, which explicitly models the background residual rather than the background itself in each step, and aggregates these residuals to represent the evolving background.
Class-Incremental Semantic Segmentation
Incremental Learning
+2
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation
However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model's plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the model's plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning.
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation
Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages.
Class-Incremental Semantic Segmentation
Continual Learning
+1
Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation
In inference, we directly use the activation masks from the DA layer as pseudo-labels for segmentation.
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition
Finally, the DTG-Net is evaluated in two ways: (i) the self-supervised DTG-Net to pre-train the supervised action recognition models with only unlabeled videos; (ii) the supervised DTG-Net to be jointly trained with the supervised action networks in an end-to-end way.
HRDNet: High-resolution Detection Network for Small Objects
By extracting various features from high to low resolutions, the MD-IPN is able to improve the performance of small object detection as well as maintaining the performance of middle and large objects.
Visual-Textual Association with Hardest and Semi-Hard Negative Pairs Mining for Person Search
In order to evaluate the effectiveness and feasibility of the proposed approach, we conduct extensive experiments on typical person search datasdet: CUHK-PEDES, in which our approach achieves the top1 score of 55. 32% as a new state-of-the-art.
IPG-Net: Image Pyramid Guidance Network for Small Object Detection
In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Novel Evaluation Metrics for Seam Carving based Image Retargeting
Image retargeting effectively resizes images by preserving the recognizability of important image regions.
