Search Results for author:
Found 19 papers, 7 papers with code
LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization
Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video.
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection
In this paper, we present Rule By Example (RBE): a novel exemplar-based contrastive learning approach for learning from logical rules for the task of textual content moderation.
Rethinking Multimodal Content Moderation from an Asymmetric Angle with Mixed-modality
There is a rapidly growing need for multimodal content moderation (CM) as more and more content on social media is multimodal in nature.
PivoTAL: Prior-Driven Supervision for Weakly-Supervised Temporal Action Localization
To address this, we present PivoTAL, Prior-driven Supervision for Weakly-supervised Temporal Action Localization, to approach WTAL from a localization-by-localization perspective by learning to localize the action snippets directly.
Weakly Supervised Action Localization
Weakly Supervised Temporal Action Localization
ProTeGe: Untrimmed Pretraining for Video Temporal Grounding by Video Temporal Grounding
We present ProTeGe as the first method to perform VTG-based untrimmed pretraining to bridge the gap between trimmed pretrained backbones and downstream VTG tasks.
Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection
To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images.
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation
It captures object motion in the video via a novel optical flow calibration module that fuses the segmentation mask with optical flow estimation to improve within-object optical flow smoothness and reduce noise at object boundaries.
Ranked #1 on
Video Object Segmentation
on DAVIS 2017 (test-dev)
(using extra training data)
A viable framework for semi-supervised learning on realistic dataset
Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.
GateHUB: Gated History Unit with Background Suppression for Online Action Detection
We present GateHUB, Gated History Unit with Background Suppression, that comprises a novel position-guided gated cross-attention mechanism to enhance or suppress parts of the history as per how informative they are for current frame prediction.
Ranked #1 on
Online Action Detection
on TVSeries
MUSE: Feature Self-Distillation with Mutual Information and Self-Information
Importantly, we argue and empirically demonstrate that MUSE, compared to other feature discrepancy functions, is a more functional proxy to introduce dependency and effectively improve the expressivity of all features in the knowledge distillation framework.
Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation
We present MetaUVFS as the first Unsupervised Meta-learning algorithm for Video Few-Shot action recognition.
Self-supervised Outdoor Scene Relighting
Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo.
Revisiting Dynamic Convolution via Matrix Decomposition
It has two limitations: (a) it increases the number of convolutional weights by K-times, and (b) the joint optimization of dynamic attention and static convolution kernels is challenging.
Stronger NAS with Weaker Predictors
We propose a paradigm shift from fitting the whole architecture space using one strong predictor, to progressively fitting a search path towards the high-performance sub-space through a set of weaker predictors.
Outdoor inverse rendering from a single image using multiview self-supervision
In this paper we show how to perform scene-level inverse rendering to recover shape, reflectance and lighting from a single, uncontrolled image using a fully convolutional neural network.
Weak NAS Predictor Is All You Need
Rather than expecting a single strong predictor to model the whole space, we seek a progressive line of weak predictors that can connect a path to the best architecture, thus greatly simplifying the learning task of each predictor.
SPRING: A Sparsity-Aware Reduced-Precision Monolithic 3D CNN Accelerator Architecture for Training and Inference
To take advantage of sparsity, some accelerator designs explore sparsity encoding and evaluation on CNN accelerators.
Hardware Architecture
Software-defined Design Space Exploration for an Efficient DNN Accelerator Architecture
It models the accelerator design task as a multi-dimensional optimization problem.
InverseRenderNet: Learning single image inverse rendering
By incorporating a differentiable renderer, our network can learn from self-supervision.
