29 citations · 154 across the 36 of their papers we have counts for
36 papers
SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation
Zhiyu Qu, Tao Xiang, Yi-Zhe Song
Artificial Intelligence Generated Content (AIGC) has shown remarkable progress in generating realistic images. However, in this paper, we take a step "backward" and address AIGC fo…
A Generalized Unbiased Risk Estimator for Learning with Augmented Classes
Senlin Shu, Shuo He, Haobo Wang +3
In contrast to the standard learning paradigm where all classes can be observed in training data, learning with augmented classes (LAC) tackles the problem where augmented classes…
3D VR Sketch Guided 3D Shape Prototyping and Exploration
Ling Luo, Pinaki Nath Chowdhury, Tao Xiang +2
3D shape modeling is labor-intensive, time-consuming, and requires years of expertise. To facilitate 3D shape modeling, we propose a 3D shape generation network that takes a 3D VR…
HeadSculpt: Crafting 3D Head Avatars with Text
Xiao Han, Yukang Cao, Kai Han +5
Recently, text-guided 3D generative methods have made remarkable advancements in producing high-quality textures and geometry, capitalizing on the proliferation of large vision-lan…
SketchXAI: A First Look at Explainability for Human Sketches
Zhiyu Qu, Yulia Gryaditskaya, Ke Li +3
This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a ``human-centred'' data form,…
ChiroDiff: Modelling chirographic data with Diffusion Models
Ayan Das, Yongxin Yang, Timothy Hospedales +2
Generative modelling over continuous-time geometric constructs, a.k.a such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Suc…
Learning Garment DensePose for Robust Warping in Virtual Try-On
Aiyu Cui, Sen He, Tao Xiang +1
Virtual try-on, i.e making people virtually try new garments, is an active research area in computer vision with great commercial applications. Current virtual try-on methods usual…
Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings
Ayan Kumar Bhunia, Subhadeep Koley, Amandeep Kumar +4
Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sket…
Picture that Sketch: Photorealistic Image Generation from Abstract Sketches
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
Given an abstract, deformed, ordinary sketch from untrained amateurs like you and me, this paper turns it into a photorealistic image - just like those shown in Fig. 1(a), all non-…
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not
Aneeshan Sain, Ayan Kumar Bhunia, Pinaki Nath Chowdhury +3
In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalis…
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
Aneeshan Sain, Ayan Kumar Bhunia, Subhadeep Koley +4
This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%. This is…
Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation
Zhongying Deng, Da Li, Junjun He +2
Target domain pseudo-labelling has shown effectiveness in unsupervised domain adaptation (UDA). However, pseudo-labels of unlabeled target domain data are inevitably noisy due to t…
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
Xiao Han, Xiatian Zhu, Licheng Yu +3
In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and imag…
What Can Human Sketches Do for Object Detection?
Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain +3
Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited…
DiffTAD: Temporal Action Detection with Proposal Denoising Diffusion
Sauradip Nag, Xiatian Zhu, Jiankang Deng +2
We propose a new formulation of temporal action detection (TAD) with denoising diffusion, DiffTAD in short. Taking as input random temporal proposals, it can yield action proposals…
Unsupervised Hashing with Similarity Distribution Calibration
Kam Woh Ng, Xiatian Zhu, Jiun Tian Hoe +4
Unsupervised hashing methods typically aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes. However, these methods often over…
Single Stage Multi-Pose Virtual Try-On
Sen He, Yi-Zhe Song, Tao Xiang
Multi-pose virtual try-on (MPVTON) aims to fit a target garment onto a person at a target pose. Compared to traditional virtual try-on (VTON) that fits the garment but keeps the po…
Post-Processing Temporal Action Detection
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song +1
Existing Temporal Action Detection (TAD) methods typically take a pre-processing step in converting an input varying-length video into a fixed-length snippet representation sequenc…
Multi-Modal Few-Shot Temporal Action Detection
Sauradip Nag, Mengmeng Xu, Xiatian Zhu +4
Few-shot (FS) and zero-shot (ZS) learning are two different approaches for scaling temporal action detection (TAD) to new classes. The former adapts a pretrained vision model to a…
Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization
Mengmeng Xu, Yanghao Li, Cheng-Yang Fu +3
This paper deals with the problem of localizing objects in image and video datasets from visual exemplars. In particular, we focus on the challenging problem of egocentric visual q…
FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning
Suvir Mirchandani, Licheng Yu, Mengjiao Wang +4
Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems - e.g., retrieving a fashion item gi…
Learning to Augment via Implicit Differentiation for Domain Generalization
Tingwei Wang, Da Li, Kaiyang Zhou +2
Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains. Domain generalization (DG) a…
Prediction Calibration for Generalized Few-shot Semantic Segmentation
Zhihe Lu, Sen He, Da Li +2
Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of…
Robust Target Training for Multi-Source Domain Adaptation
Zhongying Deng, Da Li, Yi-Zhe Song +1
Given multiple labeled source domains and a single target domain, most existing multi-source domain adaptation (MSDA) models are trained on data from all domains jointly in one ste…