8 citations · 20 across the 39 of their papers we have counts for
10 papers · 1 filter
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Dmitry Vatolin +30
This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provid…
LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
Henghui Ding, Lingyi Hong, Chang Liu +30
Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes. In this paper, we introduce the 6th…
UNINEXT-Cutie: The 1st Solution for LSVOS Challenge RVOS Track
Hao Fang, Feiyu Pan, Xiankai Lu +2
Referring video object segmentation (RVOS) relies on natural language expressions to segment target objects in video. In this year, LSVOS Challenge RVOS Track replaced the origin Y…
Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track
Feiyu Pan, Hao Fang, Runmin Cong +2
Video Object Segmentation (VOS) task aims to segmenting a particular object instance throughout the entire video sequence given only the object mask of the first frame. Recently, S…
TDS-CLIP: Temporal Difference Side Network for Efficient VideoAction Recognition
Bin Wang, Wentong Li, Wenqian Wang +3
Recently, large-scale pre-trained vision-language models (e.g., CLIP), have garnered significant attention thanks to their powerful representative capabilities. This inspires resea…
Strike a Balance in Continual Panoptic Segmentation
Jinpeng Chen, Runmin Cong, Yuxuan Luo +2
This study explores the emerging area of continual panoptic segmentation, highlighting three key balances. First, we introduce past-class backtrace distillation to balance the stab…
Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset
Shijie Lian, Ziyi Zhang, Hua Li +4
With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance…
Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation
Anqi Li, Feng Li, Yuxi Liu +3
Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical cha…
Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection
Feiran Li, Qianqian Xu, Shilong Bao +4
This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We ob…
Query-guided Prototype Evolution Network for Few-Shot Segmentation
Runmin Cong, Hang Xiong, Jinpeng Chen +3
Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, w…