SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts
In this paper, we present a novel architecture for interactive segmentation in winter sports contexts. The field of interactive segmentation deals with the prediction of high-quality segmentation masks by informing the network about the objects position with the help of user guidance. In our case the guidance consists of click prompts. For this task, we first present a baseline architecture which is specifically geared towards quickly responding after each click. Afterwards, we motivate and describe a number of architectural modifications which improve the performance when tasked with segmenting winter sports equipment on the WSESeg dataset. With regards to the average NoC@85 metric on the WSESeg classes, we outperform SAM and HQ-SAM by 2.336 and 7.946 clicks, respectively. When applied to the HQSeg-44k dataset, our system delivers state-of-the-art results with a NoC@90 of 6.00 and NoC@95 of 9.89. In addition to that, we test our model on a novel dataset containing masks for humans during skiing.
Code (1)
Tasks
Interactive SegmentationSegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Saliency-Enhanced Robust Visual Tracking
Discrete correlation filter (DCF) based trackers have shown considerable success in visual object tracking. These trackers often make use of low to mid level features such as histogram of gradients (HoG) and mid-layer ac…
object-detectionObject DetectionObject TrackingRGB Salient Object Detection+3Exploiting complex pattern features for interactive pattern mining
Recent years have seen a shift from a pattern mining process that has users define constraints before-hand, and sift through the results afterwards, to an interactive one. This new framework depends on exploiting user fe…
Intent Detection at Scale: Tuning a Generic Model using Relevant Intents
Accurately predicting the intent of customer support requests is vital for efficient support systems, enabling agents to quickly understand messages and prioritize responses accordingly. While different approaches exist …
Intent DetectionDistill Where You Fail: Recovering Learning Signals of Negative RL-Groups from Adaptive Teacher Guidance
Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for post-training large language models (LLMs). While Group Relative Policy Optimization (GRPO) is widely adopted, it suffers from spar…
Reinforcement LearningQuickMerge++: Fast Token Merging with Autoregressive Prior
As generative models scale to larger inputs across language, vision, and video domains, the cost of token-level computation has become a key bottleneck. While prior work suggests that only a subset of tokens significantl…