Improving Image Encoders for General-Purpose Nearest Neighbor Search and Classification

Schall, Konstantin; Barthel, Kai Uwe; Hezel, Nico; Jung, Kyeong-Hoon

doi:10.1145/3591106.3592266

Cited by 3 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, vibro [99] employs a CLIP ViT-L [15], [86] network that has been pre-trained on the LAION-2B dataset and fine-tuned in publicly available image datasets [98]. This enables the system to extract feature vectors useful for content-based image retrieval [97].…”

Section: Query By Examplementioning

confidence: 99%

Evaluating Performance and Trends in Interactive Video Retrieval: Insights From the 12th VBS Competition

Vadicamo,

Arnold,

Bailer

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This paper conducts a thorough examination of the 12th Video Browser Showdown (VBS) competition, which is a well-established international benchmarking campaign for interactive video search systems. The annual VBS competition has witnessed a steep rise in the popularity of multimodal embedding-based approaches in interactive video retrieval. The majority of the thirteen systems participating in VBS 2023 utilized a CLIP-based cross-modal search model, allowing the specification of free-form text queries to search visual content. This shared emphasis on joint embedding models contributed to balanced performance across various teams. However, the distinguishing factors of the top-performing teams included the adept combination of multiple models and search modes, along with the capabilities of interactive interfaces to facilitate and refine the search process. Our work provides an overview of the state-of-the-art approaches employed by the participating systems and conducts a thorough analysis of their search logs, which record user interactions and results of their queries for each task. Our comprehensive examination of the VBS competition offers assessments of the effectiveness of the retrieval models employed, the browsing efficiency, and user query patterns. Additionally, it provides valuable insights into the evolving landscape of interactive video retrieval and its future challenges.

show abstract

Section: Query By Examplementioning

confidence: 99%