Baiyang Song — researcher or builder tracked in the Angestrom contributor network.
<a href="https://news.google.com/rss/articles/CBMitAFBVV95cUxPNnRPMHpnc1BCNTQxTC15TjE2VTdqSG85T1AwS1BwU2ZocEthd0d4VEp0b2RRYzNuWWNLYWdRazlaNWE0RHhSeFR5Q0U4Q1JKNnF5LV82bklOX2JMU05NUTFyWmVzTkRYR1VEWkVMXzRGbm5jN1JtWVJxTVp0TzVpQTdjSmJJMjU4Wm1Hem9ZLUIwZFNCU2xja3RBR3BmcnM2MWZFMWpjdWxvZWxPTzc4RFduclM?oc=5" target="_blank">'Music brings everyone together' - Docker River Band reflects on Guts Touring 2026</a> <font color="#6f6f6f">National Indigenous Times</font>
Video understanding is often plagued by severe temporal redundancy, where processing dense frame sequences is both semantically inefficient and computationally expensive. This challenge is further amplified when only a small subset of frames is truly relevant to the given query. In this paper, we propose a Query- and Content-Aware (QCA) keyframe selection framework that can select a compact yet information-rich set of frames from long videos. QCA first partitions the video into temporal segments and estimates the information contribution of each segment by jointly modeling query relevance and
