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Found 20 papers, 16 papers with code
Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models
Transformers have catalyzed advancements in computer vision and natural language processing (NLP) fields.
Scalable Diffusion Models with State Space Backbone
We endeavor to train diffusion models for image data, wherein the traditional U-Net backbone is supplanted by a state space backbone, functioning on raw patches or latent space.
SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation
Recent real-time semantic segmentation methods usually adopt an additional semantic branch to pursue rich long-range context.
Conditional Boundary Loss for Semantic Segmentation
We conduct extensive experiments on ADE20K, Cityscapes, and Pascal Context, and the results show that applying the CBL to various popular segmentation networks can significantly improve the mIoU and boundary F-score performance.
Ranked #22 on
Semantic Segmentation
on Cityscapes val
PLIP: Language-Image Pre-training for Person Representation Learning
Extensive experiments demonstrate that our model not only significantly improves existing methods on all these tasks, but also shows great ability in the few-shot and domain generalization settings.
Ranked #5 on
Text based Person Retrieval
on ICFG-PEDES
Semantic Segmentation via Pixel-to-Center Similarity Calculation
Under this novel view, we propose a Class Center Similarity layer (CCS layer) to address the above-mentioned challenges by generating adaptive class centers conditioned on different scenes and supervising the similarities between class centers.
GANet: Goal Area Network for Motion Forecasting
In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas rather than exact goal coordinates as preconditions for trajectory prediction, performing more robustly and accurately.
Ranked #15 on
Motion Forecasting
on Argoverse CVPR 2020
CORE: Consistent Representation Learning for Face Forgery Detection
Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency.
Efficient Video Segmentation Models with Per-frame Inference
To this end, we perform inference at each frame.
Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation
Image manipulation with StyleGAN has been an increasing concern in recent years. Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images. However, due to the limited semantic and spatial manipulation precision in these latent spaces, the existing endeavors are defeated in fine-grained StyleGAN image manipulation, i. e., local attribute translation. To address this issue, we discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles.
CondNet: Conditional Classifier for Scene Segmentation
The last layer of FCN is typically a global classifier (1x1 convolution) to recognize each pixel to a semantic label.
Ranked #18 on
Semantic Segmentation
on PASCAL Context
Lite-HRNet: A Lightweight High-Resolution Network
We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1x1) convolutions in shuffle blocks.
Ranked #33 on
Pose Estimation
on COCO test-dev
(using extra training data)
Representative Graph Neural Network
In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy.
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for realtime semantic segmentation.
Ranked #1 on
Real-Time Semantic Segmentation
on COCO-Stuff
Context Prior for Scene Segmentation
Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior.
Ranked #1 on
Scene Understanding
on ADE20K val
Efficient Semantic Video Segmentation with Per-frame Inference
For semantic segmentation, most existing real-time deep models trained with each frame independently may produce inconsistent results for a video sequence.
Ranked #2 on
Video Semantic Segmentation
on CamVid
GTNet: Generative Transfer Network for Zero-Shot Object Detection
FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively.
An End-to-End Network for Panoptic Segmentation
Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic.
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Semantic segmentation requires both rich spatial information and sizeable receptive field.
Ranked #4 on
Semantic Segmentation
on SkyScapes-Dense
Dichotomous Image Segmentation
Real-Time Semantic Segmentation
+2
Learning a Discriminative Feature Network for Semantic Segmentation
Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction.
Ranked #5 on
Semantic Segmentation
on PASCAL VOC 2012 test
