Search

Visual question answering(VQA) is a multi-modal task that combines computer vision and natural language proces-sing,which is extremely challenging.However,the current VQA model is often misled by the apparent correlation in the data,and the output of the model is directly guided by language bias.Many previous researches focus on solving language bias and assisting the model via counterfactual sample methods.These studies,however,ignore the prediction information and the difference between key features and non-key features in counterfactual samples.The proposed model can distinguish the difference between the original sample,the factual sample and the counterfactual sample.In view of this,this paper proposes a paradigm of contrastive learning based on counterfactual samples.By comparing these three samples in terms of feature gaps and prediction gaps,the VQA model has been significantly improved in its robustness.Compared with CL-VQA method,the overall precision,average precision and Num index of this method improves by 0.19%,0.89% and 2.6% respectively.Compared with the CSSVQA method,the Gap of the proposed method decrease to 0.45 from 0.96.

Object detection based on RGB and infrared images has emerged as a crucial research area in computer vision, and the synergy of RGB-Infrared ensures the robustness of object-detection algorithms under varying lighting conditions. However, the RGB-IR image pairs captured typically exhibit spatial misalignment due to sensor discrepancies, leading to compromised localization performance. Furthermore, since the inconsistent distribution of deep features from the two modalities, directly fusing multi-modal features will weaken the feature difference between the object and the background, therefore interfering with the RGB-Infrared object-detection performance. To address these issues, we propose an adaptive dual-discrepancy calibration network (ADCNet) for misaligned RGB-Infrared object detection, including spatial discrepancy and domain-discrepancy calibration. Specifically, the spatial discrepancy calibration module conducts an adaptive affine transformation to achieve spatial alignment of features. Then, the domain-discrepancy calibration module separately aligns object and background features from different modalities, making the distribution of the object and background of the fusion feature easier to distinguish, therefore enhancing the effectiveness of RGB-Infrared object detection. Our ADCNet outperforms the baseline by 3.3% and 2.5% in m A P 50 on the FLIR and misaligned M3FD datasets, respectively. Experimental results demonstrate the superiorities of our proposed method over the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

To answer questions, visual question answering systems (VQA) rely on language bias but ignore the information of the images, which has negative information on its generalization. The mainstream debiased methods focus on removing language prior to inferring. However, the image samples are distributed unevenly in the dataset, so the feature sets acquired by the model often cannot cover the features (views) of the tail samples. Therefore, language bias occurs. This paper proposes a language bias-driven self-knowledge distillation framework to implicitly learn the feature sets of multi-views so as to reduce language bias. Moreover, to measure the performance of student models, the authors of this paper use a generalization uncertainty index to help student models learn unbiased visual knowledge and force them to focus more on the questions that cannot be answered based on language bias alone. In addition, the authors of this paper analyze the theory of the proposed method and verify the positive correlation between generalization uncertainty and expected test error. The authors of this paper validate the method's effectiveness on the VQA-CP v2, VQA-CP v1 and VQA v2 datasets through extensive ablation experiments. [ABSTRACT FROM AUTHOR]

Video caption aims to generate descriptive sentences about the video, and the most critical problem is how to achieve accurate word prediction with standardized and coherent syntax structure, which requires the model to thoroughly understand video content and precisely map them into corresponding sentence components. Many existing methods usually fuse different video features into a single visual feature for generating sentences. However, they ignore the word dataset prior information in the annotations (such as Part-Of-Speech) and they also ignore the association between sentence components and types of visual features. To solve these problems, we propose a POS-trends dynamic-aware model (PDA) to fully exploit the word dataset prior information in the captions to predict POS tag, so as to assist generating captions. We propose a POS feature extraction (PFE) module to use different filters to extract different POS-trends features, predict POS tags and fuse visual features. Furthermore, we propose a visual-dynamic-aware (VDA) module to dynamically adjust the mapping way of words and supplement the visual information into the local features. The fusion features provide directional visual information to generate correct words, and the predicted POS tags to guide the decoding process to generate a more standardized and coherent syntax structure. A large number of experiments based on MSVD, MSR-VTT and VATEX demonstrated that our method outperforms the state-of-the-art methods in BLEU-4, ROUGE-L, METEOR, CIDEr. Code can be available at: https://github.com/WangLanxiao/PDA-for-video-caption. [ABSTRACT FROM AUTHOR]

This paper presents a 3-D shape-adaptive directional wavelet coding technique for object-based scalable video coding. This technique includes 3-D object-based directional threading and extensions of weighted adaptive lifting (WAL) scheme. The 3-D object-based directional threading, which unifies the concept of temporal motion threading and 2-D spatial directional threading, provides the opportunities to align a series of video object planes to form a 3-D video object and exploit the spatio-temporal correlation inside the 3-D video object. The WAL scheme, which is extended from 2-D frame-based image coding to 3-D object-based video coding, is employed to decompose the 3-D video object into a 3-D multispatio-temporal resolution video object pyramid for object-based scalable video coding. Experimental results show that the proposed 3-D shape-adaptive directional wavelet coding technique consistently outperforms MPEG-4 and other wavelet-based schemes for coding arbitrarily shaped video objects. Index Terms--Arbitrarily shaped video object, directional wavelet transform, object-based coding, scalable video coding, weighted adaptive lifting (WAL), 3-D directional threading.

Voice over Internet protocol (VoIP) is one of the most important applications for IEEE 802.11 wireless local area networks (WLANs). For network planners who deploy VoIP over WLANs, one of the important issues is the VoIP capacity. Directly implementing VoIP over infrastructure WLANs will create the bottleneck problem at the access point (AP). In this paper, we propose the use of the service differentiation provided by the new IEEE 802.11e standard to solve the bottleneck problem and improve voice capacity. In particular, we propose the allocation of a higher priority access category (AC) to the AP while allocating lower priority AC to mobile stations. We develop a simple Markov chain model, which considers the important enhanced distributed channel access (EDCA) parameters and the channel errors under saturation and nonsaturation conditions. Based on the developed analytical model, we analyze the performance of VoIP over EDCA. By appropriately selecting the EDCA parameters, we are able to differentiate the services for the downlink and uplink. The experimental results are very promising. With the adjustment of only one EDCA parameter, we improve the VoIP capacity by 20%-30%. Index Terms--Enhanced distributed channel access (EDCA), IEEE 802.11e wireless local area networks (WLANs), medium access mechanisms, service differentiation, voice capacity, voice over Internet protocol (VoIP).

A cross-layer design for optimizing 3-D wavelet scalable video transmission over the IEEE 802.11e networks is proposed. A thorough study on the behavior of the IEEE 802.11e protocol is conducted. Based on our findings, all timescales rate control is developed featuring a unique property of soft capacity support for multimedia delivery. The design consists of a macro timescale and a micro timescale rate control schemes residing at the application layer and the network sublayer respectively. The macro rate control uses bandwidth estimation to achieve optimal bit allocation with minimum distortion. The micro rate control employs an adaptive mapping of packets from video classifications to appropriate network priorities which preemptively drops less important video packets to maximize the transmission protection to the important video packets. The performance is investigated by simulations highlighting advantages of our cross-layer design. Index Terms--Multimedia transmission, network performance analysis, scalable video, wireless LAN.

This paper describes a unified framework of bit-rate control for binary and gray-level shape coding. An exponential rate-distortion model is proposed for binary shape and used in the bit-rate control. An approximation method is applied for finer distortion scale adjustment by adjusting the conversion ratio at the binary alpha block level. Since the gray-level shape is encoded as its support information and the alpha plane information, the rate-quantizer relationship of the alpha plane is modeled. The bit-rate control is then able to adjust the quantizer of the alpha plane for the bit-rate control purpose. The extension for video object (VO) coding is also studied. In this bit-rate control approach, a buffer feedback controller is employed to determine the target bit rates for different sources, i.e., binary shape, gray-level shape, and texture. Accordingly, the distortion scales of different sources are adjusted based on their rate-distortion relationships to maintain a stable encoder buffer. The advantages of the proposed method are demonstrated through various tests for different scenarios. Overall, the algorithm can efficiently encode the shape information and shows a good performance for VO coding. Index Terms--Binary shape, gray-level shape, MPEG-4, rate control, rate-distortion, shape coding.

This paper extends the lifting-based motion threading (MTh) technique from the frame-based coding to the object-based coding, attracted by the unique advantages of the object-based coding that do not exist in other coding schemes. The boundary effects, which exist in spatial and temporal wavelet transforms due to the manner of object-based coding, are well suppressed by 3-D shape-adaptive discrete wavelet transform (3D SA-DWT) via the lifting implementation in a unified framework. Based on the proposed object-based MTh technique, a 3-D object-based scalable wavelet video coder is developed. Experimental results show that the proposed coder achieves good performance compared with the existing object-based coders and provides the scalability functionality at the same time. Index Terms--Boundary effects, object-based motion threading, wavelet video object coding, 3-D shape-adaptive discrete wavelet transform (3D SA-DWT).

In this paper, we address the key problem in real-time video coding, the rate-distortion (R-D) tradeoff. As most video coding applications employ color images, we analyze the R-D modeling problem of the color image sequence. We investigate that the R-D characteristics of color video signal in traditional transform-based video coding systems should be modeled for the luminance and chrominance components separately. So we propose separable R-D models for color video coding. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach to avoid buffer overflow and underflow. To achieve smooth video quality and satisfy the delay constraints in real-time applications, a novel R-D tradeoff controller is designed. In the proposed R-D tradeoff framework, both the quality variation and buffer safety are considered. Extensive simulation results demonstrate that the proposed approach can achieve smooth video quality without sacrificing overall quality while efficiently utilize the bandwidth without buffer overflow or underflow. Index Terms--Buffer control, H.264, rate control, rate-distortion (R-D) model, R-D tradeoff, video coding.

The conventional multiview video coding (MVC) schemes, utilizing both neighboring temporal frames and view frames as possible references, have only shown a slight gain over those using temporal frames alone in terms of coding efficiency. The reason for this is that the neighboring temporal frames exhibit stronger correlation with the current frame and the view frames often fail to be selected as references. This paper proposes an elegant MVC framework using high dimensional wavelet, which rightly matches the inherent high dimension property of multiview video. It also makes a better usage of both temporal and view correlations thanks to the hierarchical decomposition. Besides the proposed framework, this paper also investigates MVC coding from the following aspects. First, a disparity-compensated view filter (DCVF) with pixel alignment is proposed, which can accommodate both global and local view disparities among view frames. The proposed DCVF and the existing motion-compensated temporal filter (MCTF) unify the view and temporal decompositions as a generic lifting transform. Second, an adaptive decomposition structure based on the analysis of the temporal and view correlations is proposed. A Lagrangian cost function is derived to determine the optimum decomposition structure. Third, the major components of the proposed MVC coding are figured out, including macroblock type design, subband coefficient coding, and rate allocation. Extensive experiments are carried out on the MPEG 3DAV test sequences and the superior performance of the proposed MVC coding is demonstrated. In addition, the proposed MVC framework can easily support temporal, spatial, SNR, as well as view scalabilities. Index Terms--Global and local motion alignment, high dimensional wavelet, lifting, multiview video coding (MVC).

In this paper, we investigate optimal frame-selection algorithms based on dynamic programming for delivering stored variable bit rate (VBR) video under both bandwidth and buffer size constraints. Our objective is to find a feasible set of frames that can maximize the video's accumulated motion values without violating any constraint. It is well known that dynamic programming has high complexity. In this research, we propose to eliminate nonoptimal intermediate frame states, which can effectively reduce the complexity of dynamic programming. Moreover, we propose a reverse frame selection (RFS) algorithm, where the selection starts from the last frame and ends at the first frame. Compared with the conventional dynamic programming-based forward frame selection, the RFS is able to find all of the optimal results for different preloads in one round. We further extend the RFS scheme to solve the problem of frame selection for VBR channels. In particular, we first perform the RFS algorithm offline, and the complexity is modest and scalable with the aids of frame stuffing and nonoptimal state elimination. During online streaming, we only need to retrieve the optimal frame-selection path from the pregenerated offline results, and it can be applied to any VBR channels as long as the VBR channels can be modeled as piecewise CBR channels. Experimental results show good performance of our proposed algorithms. Index Terms--Bandwidth smoothing, dynamic programming, motion awareness, optimal frame selection, variable bit rate (VBR) channels, VBR video delivery.

In this paper, we present a uniform framework for optimal multiple video object bit allocation in MPEG-4. We combine the rate-distortion (R-D) models for the texture and shape information of arbitrarily shaped video objects to develop the joint texture-shape R-D models. The dynamic programming technique is applied to optimize the bit allocation for the multiple video objects. The simulation results demonstrate that the proposed joint texture-shape optimization algorithm outperforms the MPEG-4 verification model on the decoded picture quality. Index Terms--Bit ailocation, joint texture-shape optimization, MPEG-4, multiple video object, rate control, rate-distortion (R-D).

In this letter, we explore the rate-distortion (R-D) characteristics of binary shape information in the MPEG-4 standard. The shape coding scheme is a block-based context-based arithmetic encoding approach and distortion is introduced by down and up sampling. Generally, the shape bit rate and nonnormalized distortion increase in proportion with the number of border blocks. At any given resolution scale, the more complex an object, the more distortion that is introduced. Consequently, we propose an R-D model based on the parameters derived from the border blocks and a block-based shape complexity for the video object. The computational complexity is much lower than other existing methods. Experimental results show that it can accurately predict the bit rate and distortion of the binary shape for rate control purposes. Index Terms--MPEG-4, rate distortion (R-D), shape coding.

Single image dehazing is the key to enhancing image visibility in outdoor scenes, which facilitates human observation and computer recognition. The existing approaches generally utilize a one-shot strategy that indiscriminately applies the same filters to all local regions. However, due to neglecting inhomogeneous illumination and detail distortion, their dehazed results easily suffer from underfiltering or overfiltering across different regions. To tackle this issue, we propose a region-adaptive two-shot network (RATNet) that follows a coarse-to-fine framework. First, a lightweight subnetwork is applied to execute regular global filtering and obtain an initially restored image. Then, a two-branch subnetwork is put forward whose branches separately refine its illumination and detail. Eventually, we derive the final prediction by adaptively aggregating the results after illumination modification and detail restoration, whose region-variant weights are jointly optimized by maximizing the similarity between our fused result and haze-free counterpart. Extensive experiments validate the superiority of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Object proposals are used in two-stage detectors, such as R-CNN, to generate detection results, including category predictions and refined bounding-boxes. As a result, classification scores are assigned to refined bounding-boxes rather than object proposals. However, this procedure ignores the discrepancy of data distribution between object proposals and refined bounding-boxes. We consider this discrepancy could limit the detection accuracy. Specifically, the foreground/background imbalance on object proposals and inaccurate information from low-IoU proposals could hinder the category prediction. In this paper, we propose a detector called the Multi-Path Detection Calibration Network (PDC-Net) to address this problem. The key idea behind PDC-Net is calibrating detection results from R-CNN by considering the statistical discrepancy between object proposals and refined bounding-boxes. PDC-Net is built on Faster R-CNN. The core component in PDC-Net is the multi-path detection head, in which the base detector (from Faster R-CNN) generates detection results from object proposals and multiple calibration detectors fix incorrect outputs from the base detector using refined bounding-boxes. Experiments reveal that PDC-Net can boost detection results. Our method could reach 83.1% and 43.3% mAP respectively on PASCAL VOC and MSCOCO benchmarks, which is comparable to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pixel-level segmentation has been widely used to improve object detection. Most of the existing methods refine detection features by adding the constraint of the segmentation branch or by simply embedding high-level segmentation features into detection features within the local receptive field. However, noisy segmentation features are unavoidable in real-word applications and can easily cause false positives. To address this problem, we propose a novel hierarchical context embedding module to effectively embed segmentation features into detection features. The idea of this module is to capture hierarchical context information that includes local objects or parts and nonlocal context features by learning multiple attention maps, and subsequently utilize interdependencies between features to recalibrate noisy segmentation features. Furthermore, we use this module in the proposed gated encoder-decoder network that adaptively aggregates feature maps of different resolutions based on the gate mechanism so that we can embed multiscale segmentation feature maps into detection features for more accurate detection of objects of all sizes. Experimental results demonstrate the effectiveness of the proposed method on the Pascal VOC 2012Seg dataset, the Pascal VOC dataset and the MS COCO dataset. [ABSTRACT FROM AUTHOR]

Images acquired by outdoor vision systems easily suffer poor visibility and annoying interference due to the rainy weather, which brings great challenge for accurately understanding and describing the visual contents. Recent researches have devoted great efforts on the task of rain removal for improving the image visibility. However, there is very few exploration about the quality assessment of de-rained image, even it is crucial for accurately measuring the performance of various de-raining algorithms. In this paper, we first create a de-raining quality assessment (DQA) database that collects 206 authentic rain images and their de-rained versions produced by 6 representative single image rain removal algorithms. Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images. To quantitatively measure the quality of de-rained image with non-uniform artifacts, we propose a bi-directional feature embedding network (B-FEN) which integrates the features of global perception and local difference together. Experiments confirm that the proposed method significantly outperforms many existing universal blind image quality assessment models. To help the research towards perceptually preferred de-raining algorithm, we will publicly release our DQA database and B-FEN source code on https://github.com/wqb-uestc. [ABSTRACT FROM AUTHOR]

In High Efficiency Video Coding (HEVC), multiple-QP (quantization parameter) optimization can adapt to a local video content. However, the multiple-QP implementation in the HEVC reference software (HM 16.6) achieves the best QP value for each coding block with a large amount of computational complexity. To address this challenge, we propose a fast rate-constrained multiple-QP optimization approach for the HM platform. We first introduce a template-based transform coefficient selection method which can save the overall complexity of entropy coding. In addition, we model the multiple-QP determination as a new rate-constrained optimization problem, and finally, we get a feasible solution with a lower computation overhead. Experimental results show that our method dramatically reduces the average complexity under the all-intra, low-delay and random-access configuration. [ABSTRACT FROM AUTHOR]

Head detection plays an important role in localizing and identifying persons from visual data. Most existing methods treat head detection as a specific form of object detection. Head detection is nontrivial due to the considerable difficulty in building the local and global information under conditions of unconstrained pose and orientation. To address these issues, this paper presents an effective adaptive relational network to capture context information, which is greatly helpful to suppress missed detection. We show that the fundamental contextual properties, such as the global shape priors from different heads and the local adjacent relationship between the head and shoulders, can be systematically quantified by visual operators. Specifically, we propose a two-step search algorithm to quantify the global intergroup conflict with adaptive scale, pose and viewpoint. Meanwhile, a structured feature module is introduced to capture the local relation of intraindividual stability. Finally, the global priors and local relation are integrated seamlessly into a single-stage head detector that is end-to-end trainable. An extensive ablation analysis demonstrates the effectiveness of our approach. We achieve state-of-the-art results on two challenging datasets, i.e., HollywoodHeads and Brainwash. [ABSTRACT FROM AUTHOR]