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Found 20 papers, 5 papers with code
Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation
Translating images from a source domain to a target domain for learning target models is one of the most common strategies in domain adaptive semantic segmentation (DASS).
DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition with Limited Annotations
Previous works have focused on learning the alignment between textual and visual spaces to compensate for limited image labels, yet may suffer from reduced accuracy due to the scarcity of high-quality multi-label annotations.
Non-Asymptotic Performance of Social Machine Learning Under Limited Data
This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph.
Token Boosting for Robust Self-Supervised Visual Transformer Pre-training
Pre-training VTs on such corrupted data can be challenging, especially when we pre-train via the masked autoencoding approach, where both the inputs and masked ``ground truth" targets can potentially be unreliable in this case.
GAIT: Generating Aesthetic Indoor Tours with Deep Reinforcement Learning
Placing and orienting a camera to compose aesthetically meaningful shots of a scene is not only a key objective in real-world photography and cinematography but also for virtual content creation.
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations
Solving multi-label recognition (MLR) for images in the low-label regime is a challenging task with many real-world applications.
Many-to-many Splatting for Efficient Video Frame Interpolation
Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant.
Ranked #1 on
Video Frame Interpolation
on Xiph-4K (Crop)
Optimal Aggregation Strategies for Social Learning over Graphs
Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs.
Splatting-based Synthesis for Video Frame Interpolation
Frame interpolation is an essential video processing technique that adjusts the temporal resolution of an image sequence.
Learning to Detect Every Thing in an Open World
LDET leads to significant improvements on many datasets in the open-world instance segmentation task, outperforming baselines on cross-category generalization on COCO, as well as cross-dataset evaluation on UVO and Cityscapes.
Conterfactual Generative Zero-Shot Semantic Segmentation
In this work, we consider counterfactual methods to avoid the confounder in the original model.
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes
Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets.
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation
Recently, most ZSS methods focus on learning the visual-semantic correspondence to transfer knowledge from seen classes to unseen classes at the pixel level.
Real-time Semantic Segmentation with Fast Attention
The proposed architecture relies on our fast spatial attention, which is a simple yet efficient modification of the popular self-attention mechanism and captures the same rich spatial context at a small fraction of the computational cost, by changing the order of operations.
Ranked #32 on
Semantic Segmentation
on DensePASS
Temporally Distributed Networks for Fast Video Semantic Segmentation
We present TDNet, a temporally distributed network designed for fast and accurate video semantic segmentation.
Ranked #2 on
Video Semantic Segmentation
on Cityscapes val
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition
Learning from a few examples is a challenging task for machine learning.
Motion-Guided Cascaded Refinement Network for Video Object Segmentation
Then, the proposed Cascaded Refinement Network(CRN) takes the coarse segmentation as guidance to generate an accurate segmentation of full resolution.
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
Hence we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information.
Ranked #7 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
Deep Level Sets for Salient Object Detection
Our method drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency.
Tilings in graphons
We introduce a counterpart to the notion of vertex disjoint tilings by copy of a fixed graph F to the setting of graphons.
Combinatorics
