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Found 29 papers, 22 papers with code
Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models
We present in detail the framework design, the data construction, and the training recipe, to develop an MLLM step-by-step to obtain competitive performance.
Data Metabolism: An Efficient Data Design Schema For Vision Language Model
Data curation plays a crucial role in training powerful Visual Language Models (VLMs).
SEGT: A General Spatial Expansion Group Transformer for nuScenes Lidar-based Object Detection Task
Notably, our method ranks the 1st place in the nuScenes lidar-based object detection task.
LTOS: Layout-controllable Text-Object Synthesis via Adaptive Cross-attention Fusions
Controllable text-to-image generation synthesizes visual text and objects in images with certain conditions, which are frequently applied to emoji and poster generation.
Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space
To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short.
Ranked #16 on
3D Point Cloud Classification
on ScanObjectNN
Smooth image-to-image translations with latent space interpolations
Multi-domain image-to-image (I2I) translations can transform a source image according to the style of a target domain.
Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
Generative Neural Radiance Fields (GNeRF)-based 3D-aware GANs have showcased remarkable prowess in crafting high-fidelity images while upholding robust 3D consistency, particularly face generation.
YOLO-FaceV2: A Scale and Occlusion Aware Face Detector
In this paper, we propose a real-time face detector based on the one-stage detector YOLOv5, named YOLO-FaceV2.
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer
In this paper, we explore solving jigsaw puzzle as a self-supervised auxiliary loss in ViT for image classification, named Jigsaw-ViT.
Ranked #1 on
Learning with noisy labels
on ANIMAL
Spatial Entropy as an Inductive Bias for Vision Transformers
In this work, we propose a different and complementary direction, in which a local bias is introduced using an auxiliary self-supervised task, performed jointly with standard supervised training.
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers
In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded.
MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset
1) Based on a frame-free annotation methodology, we avoid writing complex frames for new predicates.
Language Models Can See: Plugging Visual Controls in Text Generation
MAGIC is a flexible framework and is theoretically compatible with any text generation tasks that incorporate image grounding.
Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics
Current practices in metric evaluation focus on one single dataset, e. g., Newstest dataset in each year's WMT Metrics Shared Task.
ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation
The ISF manipulates the semantics of an input latent code to make the image generated from it lying in the desired visual domain.
Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation
In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation.
Efficient Training of Visual Transformers with Small Datasets
This task encourages the VTs to learn spatial relations within an image and makes the VT training much more robust when training data are scarce.
Assessing Dialogue Systems with Distribution Distances
An important aspect of developing dialogue systems is how to evaluate and compare the performance of different systems.
Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation.
Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation
Inspired by recent works on image inpainting, our proposed method leverages the semantic segmentation to model the content and structure of the image, and learn the best shape and location of the object to insert.
Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation
In this paper we propose the use of an image retrieval system to assist the image-to-image translation task.
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
Our proposed model disentangles the image content from the visual attributes, and it learns to modify the latter using the textual description, before generating a new image from the content and the modified attribute representation.
Vanishing Point Guided Natural Image Stitching
Recently, works on improving the naturalness of stitching images gain more and more extensive attention.
Pairwise Interactive Graph Attention Network for Context-Aware Recommendation
PIGAT introduces the attention mechanism to consider the importance of each interacted user/item to both the user and the item, which captures user interests, item attractions and their influence on the recommendation context.
Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps
In this work, we propose a novel GAN architecture that decouples the required annotations into a category label - that specifies the gesture type - and a simple-to-draw category-independent conditional map - that expresses the location, rotation and size of the hand gesture.
Fast 3D Line Segment Detection From Unorganized Point Cloud
This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud.
Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method
We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus.
Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network
We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network.
