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Found 25 papers, 12 papers with code
Music Consistency Models
Consistency models have exhibited remarkable capabilities in facilitating efficient image/video generation, enabling synthesis with minimal sampling steps.
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.
Tuning-Free Inversion-Enhanced Control for Consistent Image Editing
Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e. g., changing postures) to the main objects in the input image without changing their identity or attributes.
A-JEPA: Joint-Embedding Predictive Architecture Can Listen
The target representations of those regions are extracted by the exponential moving average of context encoder, \emph{i. e.}, target encoder, on the whole spectrogram.
Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding
Panoptic narrative grounding (PNG) aims to segment things and stuff objects in an image described by noun phrases of a narrative caption.
DiT: Efficient Vision Transformers with Dynamic Token Routing
The proposed framework generates a data-dependent path per token, adapting to the object scales and visual discrimination of tokens.
Divide and Adapt: Active Domain Adaptation via Customized Learning
To accommodate active learning and domain adaption, the two naturally different tasks, in a collaborative framework, we advocate that a customized learning strategy for the target data is the key to the success of ADA solutions.
Gradient-Free Textual Inversion
Recent works on personalized text-to-image generation usually learn to bind a special token with specific subjects or styles of a few given images by tuning its embedding through gradient descent.
EfficientRep:An Efficient Repvgg-style ConvNets with Hardware-aware Neural Network Design
Thus, how to design a neural network to efficiently use the computing ability and memory bandwidth of hardware is a critical problem.
Masked Auto-Encoders Meet Generative Adversarial Networks and Beyond
In this paper, we introduce a novel Generative Adversarial Networks alike framework, referred to as GAN-MAE, where a generator is used to generate the masked patches according to the remaining visible patches, and a discriminator is employed to predict whether the patch is synthesized by the generator.
Bridging Search Region Interaction With Template for RGB-T Tracking
To alleviate these limitations, we propose a novel Template-Bridged Search region Interaction (TBSI) module which exploits templates as the medium to bridge the cross-modal interaction between RGB and TIR search regions by gathering and distributing target-relevant object and environment contexts.
Ranked #2 on
Rgb-T Tracking
on RGBT210
Uncertainty-Aware Image Captioning
It is well believed that the higher uncertainty in a word of the caption, the more inter-correlated context information is required to determine it.
Progressive Text-to-Image Generation
Recently, Vector Quantized AutoRegressive (VQ-AR) models have shown remarkable results in text-to-image synthesis by equally predicting discrete image tokens from the top left to bottom right in the latent space.
Meta-Ensemble Parameter Learning
Knowledge distillation is an approach that allows a single model to efficiently capture the approximate performance of an ensemble while showing poor scalability as demand for re-training when introducing new teacher models.
PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding
To alleviate these drawbacks, we propose a one-stage end-to-end Pixel-Phrase Matching Network (PPMN), which directly matches each phrase to its corresponding pixels instead of region proposals and outputs panoptic segmentation by simple combination.
Efficient Modeling of Future Context for Image Captioning
Existing approaches to image captioning usually generate the sentence word-by-word from left to right, with the constraint of conditioned on local context including the given image and history generated words.
Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation
Referring video object segmentation aims to predict foreground labels for objects referred by natural language expressions in videos.
Ranked #6 on
Referring Video Object Segmentation
on MeViS
Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation
In inference, we directly use the activation masks from the DA layer as pseudo-labels for segmentation.
Rethinking BiSeNet For Real-time Semantic Segmentation
BiSeNet has been proved to be a popular two-stream network for real-time segmentation.
Ranked #8 on
Real-Time Semantic Segmentation
on Cityscapes test
More is Less: A More Complicated Network with Less Inference Complexity
In this paper, we present a novel and general network structure towards accelerating the inference process of convolutional neural networks, which is more complicated in network structure yet with less inference complexity.
Deep Domain Adaptation for Describing People Based on Fine-Grained Clothing Attributes
We address the problem of describing people based on fine-grained clothing attributes.
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network
To address this problem, we propose a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning.
CNN: Single-label to Multi-label
Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks.
Towards Multi-view and Partially-Occluded Face Alignment
During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks.
