Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

In-Situ

,Trace Controlled Text to Image Generation,hting loss (TGR) and semantic aligned augmentation (SAA). In addition, we establish a solid benchmark for the trace-controlled text-to-image generation task, and introduce several new metrics to evaluate both the controllability and compositionality of the model. Upon that, we demonstrate TCTIG’s su

MOAT

RENIN

Explicit Image Caption Editing,rmer-based model, consisting of three modules: Tagger., Tagger., and Inserter. Specifically, Tagger. decides whether each word should be preserved or not, Tagger. decides where to add new words, and Inserter predicts the specific word for adding. To further facilitate ECE research, we propose two EC

landfill

,Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Groundinmporal order discrimination task leverages the difference in temporal order to strengthen the understanding of long-term temporal contexts. Extensive experiments on Charades-STA and ActivityNet Captions demonstrate the effectiveness of our method for mitigating the reliance on temporal biases and st

植物学

Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly,ng less than 8% of the questions to achieve a low risk of error (i.e., 1%). This motivates us to utilize a multimodal selection function to directly estimate the correctness of the predicted answers, which we show can increase the coverage by, for example, . from 6.8% to 16.3% at 1% risk. While it i

移植

,GRIT: Faster and Better Image Captioning Transformer Using Dual Visual Features,n consisting only of Transformers enables end-to-end training of the model. This innovative design and the integration of the dual visual features bring about significant performance improvement. The experimental results on several image captioning benchmarks show that GRIT outperforms previous meth

健谈

,Selective Query-Guided Debiasing for Video Corpus Moment Retrieval,d Debiasing network (SQuiDNet), which incorporates the following two main properties: (1) Biased Moment Retrieval that intentionally uncovers the biased moments inherent in objects of the query and (2) Selective Query-guided Debiasing that performs selective debiasing guided by the meaning of the qu

carbohydrate

,Spatial and Visual Perspective-Taking via View Rotation and Relation Reasoning for Embodied Referenanges the orientation of the receiver to the orientation of the sender by encoding the body orientation and gesture of the sender. Relation reasoning models both the nonverbal and verbal relations between the sender and the objects by multi-modal cooperative reasoning in gesture, language, visual co

接合

Object-Centric Unsupervised Image Captioning,f objects. Unlike in the supervised setting, these constructed pairings are however not guaranteed to have fully overlapping set of objects. Our work in this paper overcomes this by harvesting objects corresponding to a given sentence from the training set, even if they don’t belong to the same imag

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