16 citations · 29 across the 16 of their papers we have counts for
17 papers · 1 filter
OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory
Zhaochong An, Menglin Jia, Haonan Qiu +12
Storytelling in real-world videos often unfolds through multiple shots -- discontinuous yet semantically connected clips that together convey a coherent narrative. However, existin…
Adaptive Caching for Faster Video Generation with Diffusion Transformers
Kumara Kahatapitiya, Haozhe Liu, Sen He +5
Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans. More-recent Diffusion Transformers (DiTs) -- despite…
MarDini: Masked Autoregressive Diffusion for Video Generation at Scale
Haozhe Liu, Shikun Liu, Zijian Zhou +12
We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR…
Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA
Jongwoo Park, Kanchana Ranasinghe, Kumara Kahatapitiya +3
Long-form videos that span across wide temporal intervals are highly information redundant and contain multiple distinct events or entities that are often loosely related. Therefor…
Understanding Long Videos with Multimodal Language Models
Kanchana Ranasinghe, Xiang Li, Kumara Kahatapitiya +1
Large Language Models (LLMs) have allowed recent LLM-based approaches to achieve excellent performance on long-video understanding benchmarks. We investigate how extensive world kn…
Language Repository for Long Video Understanding
Kumara Kahatapitiya, Kanchana Ranasinghe, Jongwoo Park +1
Language has become a prominent modality in computer vision with the rise of LLMs. Despite supporting long context-lengths, their effectiveness in handling long-term information gr…
Object-Centric Diffusion for Efficient Video Editing
Kumara Kahatapitiya, Adil Karjauv, Davide Abati +3
Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual ed…
VicTR: Video-conditioned Text Representations for Activity Recognition
Kumara Kahatapitiya, Anurag Arnab, Arsha Nagrani +1
Vision-Language models (VLMs) have excelled in the image-domain -- especially in zero-shot settings -- thanks to the availability of vast pretraining data (i.e., paired image-text…
Grafting Vision Transformers
Jongwoo Park, Kumara Kahatapitiya, Donghyun Kim +3
Vision Transformers (ViTs) have recently become the state-of-the-art across many computer vision tasks. In contrast to convolutional networks (CNNs), ViTs enable global information…
MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection
Rui Dai, Srijan Das, Kumara Kahatapitiya +2
Action detection is an essential and challenging task, especially for densely labelled datasets of untrimmed videos. The temporal relation is complex in those datasets, including c…
SWAT: Spatial Structure Within and Among Tokens
Kumara Kahatapitiya, Michael S. Ryoo
Modeling visual data as tokens (i.e., image patches) using attention mechanisms, feed-forward networks or convolutions has been highly effective in recent years. Such methods usual…
Weakly-guided Self-supervised Pretraining for Temporal Activity Detection
Kumara Kahatapitiya, Zhou Ren, Haoxiang Li +3
Temporal Activity Detection aims to predict activity classes per frame, in contrast to video-level predictions in Activity Classification (i.e., Activity Recognition). Due to the e…
Coarse-Fine Networks for Temporal Activity Detection in Videos
Kumara Kahatapitiya, Michael S. Ryoo
In this paper, we introduce Coarse-Fine Networks, a two-stream architecture which benefits from different abstractions of temporal resolution to learn better video representations…
Feature-Dependent Cross-Connections in Multi-Path Neural Networks
Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe +3
Learning a particular task from a dataset, samples in which originate from diverse contexts, is challenging, and usually addressed by deepening or widening standard neural networks…
Context-Aware Multipath Networks
Dumindu Tissera, Kumara Kahatapitiya, Rukshan Wijesinghe +2
Making a single network effectively address diverse contexts---learning the variations within a dataset or multiple datasets---is an intriguing step towards achieving generalized i…
Exploiting the Redundancy in Convolutional Filters for Parameter Reduction
Kumara Kahatapitiya, Ranga Rodrigo
Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks over the years. However, this comes at the cost of heavy computation a…
Context-Aware Automatic Occlusion Removal
Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo
Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal. No work tries to ad…