Search Results for author:
Found 18 papers, 9 papers with code
Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining
Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars.
InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models
While recent advancements have introduced control over factors such as object localization, posture, and image contours, a crucial gap remains in our ability to control the interactions between objects in the generated content.
POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
Our key idea is to formulate the POAR problem as an image-text search problem.
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger
Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model.
Temporal Coherent Test-Time Optimization for Robust Video Classification
To exploit information in video with self-supervised learning, TeCo uses global content from video clips and optimizes models for entropy minimization.
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Finally, we examine various types of adversarial attacks that are specific to deraining problems and their effects on both human and machine vision tasks, including 1) rain region attacks, adding perturbations only in the rain regions to make the perturbations in the attacked rain images less visible; 2) object-sensitive attacks, adding perturbations only in regions near the given objects.
Learning Transferable Human-Object Interaction Detector With Natural Language Supervision
More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition.
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions
The state-of-the-art deep neural networks are vulnerable to common corruptions (e. g., input data degradations, distortions, and disturbances caused by weather changes, system error, and processing).
Discovering Human Interactions With Large-Vocabulary Objects via Query and Multi-Scale Detection
In this work, we study the problem of human-object interaction (HOI) detection with large vocabulary object categories.
Attention-Aware Noisy Label Learning for Image Classification
In this paper, the attention-aware noisy label learning approach ($A^2NL$) is proposed to improve the discriminative capability of the network trained on datasets with potential label noise.
Scaling Object Detection by Transferring Classification Weights
Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count.
Motion-Guided Cascaded Refinement Network for Video Object Segmentation
Then, the proposed Cascaded Refinement Network(CRN) takes the coarse segmentation as guidance to generate an accurate segmentation of full resolution.
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
We propose a novel approach for cost-adjustable inference in CNNs - Stochastic Downsampling Point (SDPoint).
Cross-Modal Deep Variational Hashing
In this paper, we propose a cross-modal deep variational hashing (CMDVH) method to learn compact binary codes for cross-modality multimedia retrieval.
DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows
Deluge Networks (DelugeNets) are deep neural networks which efficiently facilitate massive cross-layer information inflows from preceding layers to succeeding layers.
From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection
This representative selection problem is formulated as a sparse dictionary selection problem, i. e., choosing a few representatives object proposals to reconstruct the whole proposal pool.
Deep Transfer Metric Learning
Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same.
Discriminative Deep Metric Learning for Face Verification in the Wild
This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild.
