11 accepted papers
Video Unsupervised Domain Adaptation (VUDA) poses a significant challenge in action recognition, requiring the adaptation of a model from a labeled source domain to an unlabeled target domain. Despite recent advances, existing VUDA methods often fall short of fully supervised performance, a key reas
Recent advances in Large Multimodal Models (LMMs) have showcased impressive visual understanding and vision-language reasoning capabilities, yet their computational cost hinders practical deployment, especially in resource-constrained settings. A key bottleneck is the large number of visual tokens g
Video highlight detection can greatly simplify video browsing, potentially paving the way for a wide range of applications. Existing efforts are mostly fully-supervised, requiring humans to manually identify and label the interesting moments (called highlights) in a video. Recent weakly supervised m…
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance of an existing segmentation model when training (source domain) and testing (target domain) data originate from different…
In video highlight detection, the goal is to identify the interesting moments within an unedited video. Although the audio component of the video provides important cues for highlight detection, the majority of existing efforts focus almost exclusively on the visual component. In this paper, we argu…
Recently, there is an increasing interest in highlight detection research where the goal is to create a short duration video from a longer video by extracting its interesting moments. However, most existing methods ignore the fact that the definition of video highlight is highly subjective. Differen…
We consider the problem of referring image segmentation. Given an input image and a natural language expression, the goal is to segment the object referred by the language expression in the image. Existing works in this area treat the language expression and the input image separately in their repre…
We consider the problem of video summarization. Given an input raw video, the goal is to select a small subset of key frames from the input video to create a shorter summary video that best describes the content of the original video. Most of the current state-of-the-art video summarization approach…
This paper addresses the problem of video summarization. Given an input video, the goal is to select a subset of the frames to create a summary video that optimally captures the important information of the input video. With the large amount of videos available online, video summarization provides a…
Effective integration of local and global contextual information is crucial for dense labeling problems. Most existing methods based on an encoder-decoder architecture simply concatenate features from earlier layers to obtain higher-frequency details in the refinement stages. However, there are limi…
We consider the problem of localizing unseen objects in weakly labeled image collections. Given a set of images annotated at the image level, our goal is to localize the object in each image. The novelty of our proposed work is in addition to building object appearance model from the weakly labeled…