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ASMR (Autonomous Sensory Meridian Response) has grown to immense popularity on YouTube and drawn HCI designers' attention to its effects and applications in design. YouTube ASMR creators incorporate visual elements, sounds, motifs of…
The popularity of Autonomous Sensory Meridian Response (ASMR) has skyrockted over the past decade, but scientific studies on what exactly triggered ASMR effect remain few and immature, one most commonly acknowledged trigger is that ASMR…
Due to the extensive use of information technology and the recent developments in multimedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains…
Precise video retrieval requires multi-modal correlations to handle unseen vocabulary and scenes, becoming more complex for lengthy videos where models must perform effectively without prior training on a specific dataset. We introduce a…
Video moment retrieval targets at retrieving a moment in a video for a given language query. The challenges of this task include 1) the requirement of localizing the relevant moment in an untrimmed video, and 2) bridging the semantic gap…
The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge. Human-generated queries for video datasets `in the wild' vary a lot in terms of degree of specificity,…
Watching Autonomous Sensory Meridian Response (ASMR) videos is a popular approach to support mental well-being, as the triggered ASMR tingling sensation supports de-stressing and regulating emotions. Therefore, there is increasing research…
Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low…
Known-item video search is effective with human-in-the-loop to interactively investigate the search result and refine the initial query. Nevertheless, when the first few pages of results are swamped with visually similar items, or the…
With AI-generated videos increasingly indistinguishable from reality, current benchmarks primarily focus on broad semantic alignment and basic physical consistency, offering limited discriminative power for evaluating them. To address this,…
The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular…
Video moment search, the process of finding relevant moments in a video corpus to match a user's query, is crucial for various applications. Existing solutions, however, often assume a single perfect matching moment, struggle with…
Partially relevant video retrieval (PRVR) is a practical yet challenging task in text-to-video retrieval, where videos are untrimmed and contain much background content. The pursuit here is of both effective and efficient solutions to…
Video Corpus Moment Retrieval (VCMR) is a new video retrieval task aimed at retrieving a relevant moment from a large corpus of untrimmed videos using a text query. The relevance between the video and query is partial, mainly evident in two…
Video Corpus Moment Retrieval (VCMR) is a practical video retrieval task focused on identifying a specific moment within a vast corpus of untrimmed videos using the natural language query. Existing methods for VCMR typically rely on…
Generating videos for visual storytelling can be a tedious and complex process that typically requires either live-action filming or graphics animation rendering. To bypass these challenges, our key idea is to utilize the abundance of…
Online video web content is richly multimodal: a single video blends vision, speech, ambient audio, and on-screen text. Retrieval systems typically treat these modalities as independent retrieval sources, which can lead to noisy and subpar…
Affective video indexing is the area of research that develops techniques to automatically generate descriptions of video content that encode the emotional reactions which the video content evokes in viewers. This paper provides a set of…
Now that everyone can easily record videos, the quantity of which is continuously increasing, research on methods for improved video retrieval is important in the contemporary world. In cases where target videos are to be identified within…
We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use…