Search Results for author:
Found 21 papers, 13 papers with code
Mind the Time: Temporally-Controlled Multi-Event Video Generation
Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragraph of text as input.
SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation
Methods for image-to-video generation have achieved impressive, photo-realistic quality.
Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models
By fine-tuning a pre-trained text-to-image diffusion model with this information, our approach enables fine-grained 3D pose and placement control of individual objects in a scene.
SPAD : Spatially Aware Multiview Diffusers
We present SPAD, a novel approach for creating consistent multi-view images from text prompts or single images.
SPAD: Spatially Aware Multi-View Diffusers
We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images.
LEOD: Label-Efficient Object Detection for Event Cameras
On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels.
EventCLIP: Adapting CLIP for Event-based Object Recognition
Recent advances in zero-shot and few-shot classification heavily rely on the success of pre-trained vision-language models (VLMs) such as CLIP.
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models
Finally, we demonstrate the scalability of SlotDiffusion to unconstrained real-world datasets such as PASCAL VOC and COCO, when integrated with self-supervised pre-trained image encoders.
An Incremental Learning Approach for Sustainable Region Isolation and Integration
In this paper, we mimic partitioned learning and memory replay in the human brain and propose an incremental learning approach named SRII, short for sustainable regional isolation and integration.
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
We introduce Breaking Bad, a large-scale dataset of fractured objects.
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning
To relieve the burden of labor-intensive labeling, deep unsupervised SOD methods have been proposed to exploit noisy labels generated by handcrafted saliency methods.
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network
It results in a serious issue of lagged gradient, making the learned attack at the current step ineffective due to the architecture changes afterward.
Road Network Guided Fine-Grained Urban Traffic Flow Inference
Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture.
Instance Similarity Learning for Unsupervised Feature Representation
In this paper, we propose an instance similarity learning (ISL) method for unsupervised feature representation.
Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation
In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e. g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership.
RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge
Question: I have five fingers but I am not alive.
IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
The former aims to recover the surface of point cloud through implicit function, while the latter encourages evenly-distributed points.
BiDet: An Efficient Binarized Object Detector
Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly.
CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation
In this paper, we propose a novel heart segmentation pipeline Combining Faster R-CNN and U-net Network (CFUN).
