19 citations · 65 across the 20 of their papers we have counts for
20 papers
CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance
Anindya Mondal, Ayan Banerjee, Sauradip Nag +3
Diffusion models excel at photorealistic synthesis but struggle with object count fidelity, especially in high-density settings. We introduce COUNTLOOP, a training-free framework t…
Chirpy3D: Part-Aware Multi-View Diffusion for Creative Fine-Grained Object Generation
Kam Woh Ng, Jing Yang, Jia Wei Sii +5
Understanding and generating the fine-grained structure of objects -- such as birds with species-specific beaks, wings, and tails -- is a long-standing challenge in computer vision…
PartCraft: Crafting Creative Objects by Parts
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
This paper propels creative control in generative visual AI by allowing users to "select". Departing from traditional text or sketch-based methods, we for the first time allow user…
ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitat…
OmniCount: Multi-label Object Counting with Semantic-Geometric Priors
Anindya Mondal, Sauradip Nag, Xiatian Zhu +1
Object counting is pivotal for understanding the composition of scenes. Previously, this task was dominated by class-specific methods, which have gradually evolved into more adapta…
DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
Recent text-to-image (T2I) generative models allow for high-quality synthesis following either text instructions or visual examples. Despite their capabilities, these models face l…
Actor-agnostic Multi-label Action Recognition with Multi-modal Query
Anindya Mondal, Sauradip Nag, Joaquin M Prada +2
Existing action recognition methods are typically actor-specific due to the intrinsic topological and apparent differences among the actors. This requires actor-specific pose estim…
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…
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…
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…
PersonalTailor: Personalizing 2D Pattern Design from 3D Garment Point Clouds
Sauradip Nag, Anran Qi, Xiatian Zhu +1
Garment pattern design aims to convert a 3D garment to the corresponding 2D panels and their sewing structure. Existing methods rely either on template fitting with heuristics and…
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…
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…
Proposal-Free Temporal Action Detection via Global Segmentation Mask Learning
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song +1
Existing temporal action detection (TAD) methods rely on generating an overwhelmingly large number of proposals per video. This leads to complex model designs due to proposal gener…
Zero-Shot Temporal Action Detection via Vision-Language Prompting
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song +1
Existing temporal action detection (TAD) methods rely on large training data including segment-level annotations, limited to recognizing previously seen classes alone during infere…
FashionViL: Fashion-Focused Vision-and-Language Representation Learning
Xiao Han, Licheng Yu, Xiatian Zhu +3
Large-scale Vision-and-Language (V+L) pre-training for representation learning has proven to be effective in boosting various downstream V+L tasks. However, when it comes to the fa…
Semi-Supervised Temporal Action Detection with Proposal-Free Masking
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song +1
Existing temporal action detection (TAD) methods rely on a large number of training data with segment-level annotations. Collecting and annotating such a training set is thus highl…
Few-Shot Temporal Action Localization with Query Adaptive Transformer
Sauradip Nag, Xiatian Zhu, Tao Xiang
Existing temporal action localization (TAL) works rely on a large number of training videos with exhaustive segment-level annotation, preventing them from scaling to new classes. A…
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
Zhihe Lu, Sen He, Xiatian Zhu +3
A few-shot semantic segmentation model is typically composed of a CNN encoder, a CNN decoder and a simple classifier (separating foreground and background pixels). Most existing me…