English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Ad-hoc Video Search (AVS) enables users to search for unlabeled video contentusing on-the-fly textual queries. Current deep learning-based models for AVSare trained to optimize holistic similarity between short videos and theirassociated descriptions. However, due to the diversity of ad-hoc queries, evenfor a short video, its truly relevant part w.r.t. a given query can be ofshorter duration. In such a scenario, the holistic similarity becomessuboptimal. To remedy the issue, we propose in this paper CLIPRerank, afine-grained re-scoring method. We compute cross-modal similarities betweenquery and video frames using a pre-trained CLIP model, with multi-frame scoresaggregated by max pooling. The fine-grained score is weightedly added to theinitial score for search result reranking. As such, CLIPRerank is agnostic tothe underlying video retrieval models and extremely simple, making it a handyplug-in for boosting AVS. Experiments on the challenging TRECVID AVS benchmarks(from 2016 to 2021) justify the effectiveness of the proposed strategy.CLIPRerank consistently improves the TRECVID top performers and multipleexisting models including SEA, W2VV++, Dual Encoding, Dual Task, LAFF,CLIP2Video, TS2-Net and X-CLIP. Our method also works when substituting BLIP-2for CLIP.

CLIPRerank: An Extremely Simple Method for Improving Ad-hoc Video Search