Search Results for author:
Found 19 papers, 8 papers with code
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution
For cross-modal tasks such as text-to-image generation, this same mapping from noise to image is learnt whilst including a conditioning mechanism in the model.
Automatic programming via large language models with population self-evolution for dynamic job shop scheduling problem
To enhance the capabilities of LLMs in automatic HDRs design, this paper proposes a novel population self-evolutionary (SeEvo) method, a general search framework inspired by the self-reflective design strategies of human experts.
Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data
In order to narrow the gap between video-text models and human performance on RCAD, we identify a key limitation of current contrastive approaches on video-text data and introduce LLM-teacher, a more effective approach to learn action semantics by leveraging knowledge obtained from a pretrained large language model.
ImageNet3D: Towards General-Purpose Object-Level 3D Understanding
A vision model with general-purpose object-level 3D understanding should be capable of inferring both 2D (e. g., class name and bounding box) and 3D information (e. g., 3D location and 3D viewpoint) for arbitrary rigid objects in natural images.
Image Captioning
Linear Probing Object-Level 3D Awareness
+2
Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization
This paper presents innovative enhancements to diffusion models by integrating a novel multi-resolution network and time-dependent layer normalization.
Ranked #12 on
Image Generation
on ImageNet 256x256
DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data
Unlike recent 3D generative models that rely on clean and well-aligned 3D data, limiting them to single or few-class generation, our model is directly trained on extensive noisy and unaligned `in-the-wild' 3D assets, mitigating the key challenge (i. e., data scarcity) in large-scale 3D generation.
Continual Adversarial Defense
In response to the rapidly evolving nature of adversarial attacks against visual classifiers on a monthly basis, numerous defenses have been proposed to generalize against as many known attacks as possible.
General Object Foundation Model for Images and Videos at Scale
We present GLEE in this work, an object-level foundation model for locating and identifying objects in images and videos.
Ranked #1 on
Referring Video Object Segmentation
on Refer-YouTube-VOS
(using extra training data)
Long-tail Video Object Segmentation
Multi-Object Tracking
+8
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model.
Ranked #1 on
Animal Pose Estimation
on Animal3D
Generating Images with 3D Annotations Using Diffusion Models
With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically.
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search
(2) We find regions in the latent space that lead to distorted images independent of the text prompt, suggesting that parts of the latent space are not well-structured.
InstMove: Instance Motion for Object-centric Video Segmentation
A common solution is to use optical flow to provide motion information, but essentially it only considers pixel-level motion, which still relies on appearance similarity and hence is often inaccurate under occlusion and fast movement.
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation
We introduce a learning-based testing method, termed PoseExaminer, that automatically diagnoses HPS algorithms by searching over the parameter space of human pose images to find the failure modes.
The Runner-up Solution for YouTube-VIS Long Video Challenge 2022
This technical report describes our 2nd-place solution for the ECCV 2022 YouTube-VIS Long Video Challenge.
Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation
Inspired by the remarkable ability of humans to infer occluded joints from visible cues, we develop a method to explicitly model this process that significantly improves bottom-up multi-person human pose estimation with or without occlusions.
Ranked #10 on
3D Multi-Person Pose Estimation (absolute)
on MuPoTS-3D
3D Human Pose Estimation
3D Multi-Person Pose Estimation (absolute)
+2
In Defense of Online Models for Video Instance Segmentation
In recent years, video instance segmentation (VIS) has been largely advanced by offline models, while online models gradually attracted less attention possibly due to their inferior performance.
Ranked #14 on
Video Instance Segmentation
on YouTube-VIS 2021
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
We believe our dataset provides a rich testbed to study UDA for part segmentation and will help to significantly push forward research in this area.
Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation
We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent articulated object pose estimation.
PNS: Population-Guided Novelty Search for Reinforcement Learning in Hard Exploration Environments
To balance exploration and exploitation, the Novelty Search (NS) is employed in every chief agent to encourage policies with high novelty while maximizing per-episode performance.
