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Found 14 papers, 1 papers with code
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark
Multi-target multi-camera tracking is a crucial task that involves identifying and tracking individuals over time using video streams from multiple cameras.
KOALA: Self-Attention Matters in Knowledge Distillation of Latent Diffusion Models for Memory-Efficient and Fast Image Synthesis
We hope that due to its balanced speed-performance tradeoff, our KOALA models can serve as a cost-effective alternative to SDXL in resource-constrained environments.
Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation
In specific, we achieve domain mixup in two-step: cut and paste.
Mask-Guided Matting in the Wild
Mask-guided matting has shown great practicality compared to traditional trimap-based methods.
Tracking by Associating Clips
The tracking-by-detection paradigm today has become the dominant method for multi-object tracking and works by detecting objects in each frame and then performing data association across frames.
Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection
First, no tracking supervisions are in LVIS, which leads to inconsistent learning of detection (with LVIS and TAO) and tracking (only with TAO).
Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management
In addition, to prevent overfitting of the TTA model, we devise novel regularization which modulates the adaptation rates using domain-similarity between the source and the current target domain.
Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation
To successfully train our framework, we collect the partial, confident target samples that are classified as known or unknown through on our proposed multi-criteria selection.
Per-Clip Video Object Segmentation
In this per-clip inference scheme, we update the memory with an interval and simultaneously process a set of consecutive frames (i. e. clip) between the memory updates.
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
The scheme first clusters compound target data based on style, discovering multiple latent domains (discover).
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
In order to find the uncertain points, we generate an inconsistency mask using the proposed adaptive pixel selector and we label these segment-based regions to achieve near supervised performance with only a small fraction (about 2. 2%) ground truth points, which we call "Segment based Pixel-Labeling (SPL)".
Unsupervised Domain Adaptation for Video Semantic Segmentation
In this work, we present a new video extension of this task, namely Unsupervised Domain Adaptation for Video Semantic Segmentation.
Preserving Semantic and Temporal Consistency for Unpaired Video-to-Video Translation
In this paper, we investigate the problem of unpaired video-to-video translation.
Align-and-Attend Network for Globally and Locally Coherent Video Inpainting
Our video inpainting network consists of two stages.
