56 research outputs found
Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation
Recently, semi-supervised semantic segmentation has achieved promising
performance with a small fraction of labeled data. However, most existing
studies treat all unlabeled data equally and barely consider the differences
and training difficulties among unlabeled instances. Differentiating unlabeled
instances can promote instance-specific supervision to adapt to the model's
evolution dynamically. In this paper, we emphasize the cruciality of instance
differences and propose an instance-specific and model-adaptive supervision for
semi-supervised semantic segmentation, named iMAS. Relying on the model's
performance, iMAS employs a class-weighted symmetric intersection-over-union to
evaluate quantitative hardness of each unlabeled instance and supervises the
training on unlabeled data in a model-adaptive manner. Specifically, iMAS
learns from unlabeled instances progressively by weighing their corresponding
consistency losses based on the evaluated hardness. Besides, iMAS dynamically
adjusts the augmentation for each instance such that the distortion degree of
augmented instances is adapted to the model's generalization capability across
the training course. Not integrating additional losses and training procedures,
iMAS can obtain remarkable performance gains against current state-of-the-art
approaches on segmentation benchmarks under different semi-supervised partition
protocols
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation
Recent studies on semi-supervised semantic segmentation (SSS) have seen fast
progress. Despite their promising performance, current state-of-the-art methods
tend to increasingly complex designs at the cost of introducing more network
components and additional training procedures. Differently, in this work, we
follow a standard teacher-student framework and propose AugSeg, a simple and
clean approach that focuses mainly on data perturbations to boost the SSS
performance. We argue that various data augmentations should be adjusted to
better adapt to the semi-supervised scenarios instead of directly applying
these techniques from supervised learning. Specifically, we adopt a simplified
intensity-based augmentation that selects a random number of data
transformations with uniformly sampling distortion strengths from a continuous
space. Based on the estimated confidence of the model on different unlabeled
samples, we also randomly inject labelled information to augment the unlabeled
samples in an adaptive manner. Without bells and whistles, our simple AugSeg
can readily achieve new state-of-the-art performance on SSS benchmarks under
different partition protocols.Comment: 10 pages, 8 table
WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More
Large Language Models (LLMs) face significant deployment challenges due to
their substantial memory requirements and the computational demands of
auto-regressive text generation process. This paper addresses these challenges
by focusing on the quantization of LLMs, a technique that reduces memory
consumption by converting model parameters and activations into low-bit
integers. We critically analyze the existing quantization approaches,
identifying their limitations in balancing the accuracy and efficiency of the
quantized LLMs. To advance beyond these limitations, we propose WKVQuant, a PTQ
framework especially designed for quantizing weights and the key/value (KV)
cache of LLMs. Specifically, we incorporates past-only quantization to improve
the computation of attention. Additionally, we introduce two-dimensional
quantization strategy to handle the distribution of KV cache, along with a
cross-block reconstruction regularization for parameter optimization.
Experiments show that WKVQuant achieves almost comparable memory savings to
weight-activation quantization, while also approaching the performance of
weight-only quantization.Comment: Frist work to exclusively quantize weight and Key/Value cache for
large language model
Testing an intervention codesigned with stakeholders for altering wildlife consumption: Health messaging matters
Consumer behavior change is a key priority to address the illegal wildlife trade, but evaluation of these interventions is lacking. We used surveys and randomized controlled trials to assess the effectiveness of three types of messages, which were codesigned with key stakeholders, with 2496 potential consumers and nontarget consumers in China. We found a 23% decrease in intention among potential consumers to use wild animal medicinal products by health‐related messages, and a 14% decrease by legality‐related messages, compared with the control group, though the effect size was small. Furthermore, we revealed that the effect of health‐related messages occurred indirectly by increasing health risk perceptions associated with improper utilization of wild animals. Yet, we did not find a clear effect pathway of the legality‐related messages. Regarding the nontarget consumers, information of whistleblowing platforms and incentives improved willingness to report illegal wildlife use directly, as well as indirectly through adding messages to increase perceived legality risk of using wild products and improve self‐efficacy in identifying legal products. Our findings can inform future larger scale efforts to influence wildlife consumption
Applying a co-design approach with key stakeholders to design interventions to reduce illegal wildlife consumption
1. Co-design, an approach that seeks to incorporate the experiences and perspectives of different stakeholders, is increasingly being used to develop audience-oriented behaviour change interventions.
2. The complexity of wildlife consumption behaviour makes the co-design approach an important potential tool for the design of conservation interventions that aim to reduce illegal wildlife trade. Yet, little is known about how to adapt and apply the co-design approach to the wildlife trade sector.
3. Here, we applied a co-design approach to develop interventions aimed at reducing illegal animal-based medicine consumption in China. We conducted three workshops with key stakeholders: consumers of animal-based medicines, pharmacy workers who sell them and traditional Chinese medicine (TCM) doctors who prescribe them. We then developed a theory of change to ensure the relevance of the co-designed intervention prototypes.
4. Our co-design process identified five main pathways of interventions, including two inclusive solutions that may have been previously overlooked in behaviour change work in this context. These were an intervention to promote the appropriate use of TCM and one to increase consumers' capacity to identify the legality of products. Our prototype interventions also enhanced existing views related to the role of medical practitioners in health-risk communication.
5. We used our co-design process and reflections on its application to this specific market to provide guidelines for future conservation program planning in the broader wildlife trade context. Some intervention prototypes produced during co-design may need wider stakeholder involvement to increase their feasibility for implementation.
6. We show that the co-design process can integrate multiple stakeholders' perspectives in the ideation stage, and has the potential to produce inclusive intervention designs that could drive innovation in conservation efforts to reduce illegal consumption of a range of wild species
FastPillars: A Deployment-friendly Pillar-based 3D Detector
The deployment of 3D detectors strikes one of the major challenges in
real-world self-driving scenarios. Existing BEV-based (i.e., Bird Eye View)
detectors favor sparse convolutions (known as SPConv) to speed up training and
inference, which puts a hard barrier for deployment, especially for on-device
applications. In this paper, to tackle the challenge of efficient 3D object
detection from an industry perspective, we devise a deployment-friendly
pillar-based 3D detector, termed FastPillars. First, we introduce a novel
lightweight Max-and-Attention Pillar Encoding (MAPE) module specially for
enhancing small 3D objects. Second, we propose a simple yet effective principle
for designing a backbone in pillar-based 3D detection. We construct FastPillars
based on these designs, achieving high performance and low latency without
SPConv. Extensive experiments on two large-scale datasets demonstrate the
effectiveness and efficiency of FastPillars for on-device 3D detection
regarding both performance and speed. Specifically, FastPillars delivers
state-of-the-art accuracy on Waymo Open Dataset with 1.8X speed up and 3.8
mAPH/L2 improvement over CenterPoint (SPConv-based). Our code is publicly
available at: https://github.com/StiphyJay/FastPillars.Comment: Submitted to AAAI202
Continuous decision-making in lane changing and overtaking maneuvers for unmanned vehicles: a risk-aware reinforcement learning approach with task decomposition
Reinforcement learning methods have shown the ability to solve challenging scenarios in unmanned systems. However, solving long-time decision-making sequences in a highly complex environment, such as continuous lane change and overtaking in dense scenarios, remains challenging. Although existing unmanned vehicle systems have made considerable progress, minimizing driving risk is the first consideration. Risk-aware reinforcement learning is crucial for addressing potential driving risks. However, the variability of the risks posed by several risk sources is not considered by existing reinforcement learning algorithms applied in unmanned vehicles. Based on the above analysis, this study proposes a risk-aware reinforcement learning method with driving task decomposition to minimize the risk of various sources. Specifically, risk potential fields are constructed and combined with reinforcement learning to decompose the driving task. The proposed reinforcement learning framework uses different risk-branching networks to learn the driving task. Furthermore, a low-risk episodic sampling augmentation method for different risk branches is proposed to solve the shortage of high-quality samples and further improve sampling efficiency. Also, an intervention training strategy is employed wherein the artificial potential field (APF) is combined with reinforcement learning to speed up training and further ensure safety. Finally, the complete intervention risk classification twin delayed deep deterministic policy gradient-task decompose (IDRCTD3-TD) algorithm is proposed. Two scenarios with different difficulties are designed to validate the superiority of this framework. Results show that the proposed framework has remarkable improvements in performance
A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks
Semisupervised learning in synthetic aperture radars (SARs) is one of the research hotspots in the field of radar image automatic target recognition. It can efficiently deal with challenging environments where there are insufficient labeled samples and large unlabeled samples in the SAR dataset. In recent years, consistency regularization methods in semisupervised learning have shown considerable improvement in recognition accuracy and efficiency. Current consistency regularization approaches suffer from two main shortcomings: first, extracting all of the relevant information in the image target is difficult owing to the inability of conventional convolutional neural networks to capture global relational information; second, the standard teacher–student regularization methodology causes confirmation biases due to the high coupling between teacher and student models. This article adopts an innovative trimodel semisupervised method based on attention-augmented convolutional networks to address the aforementioned obstacles. Specifically, we develop an attention mechanism incorporating a novel positional embedding method based on recurrent neural networks and integrate this with a standard convolutional network as a feature extractor, to improve the network's ability to extract global feature information from images. Furthermore, we address the confirmation bias problem by introducing a classmate model to the standard teacher–student structure and utilize the model to impose a weak consistency constraint designed on the student to weaken the strong coupling between the teacher and the student. Comparative experiments on the Moving and Stationary Target Acquisition and Recognition dataset show that our method outperforms state-of-the-art semisupervised methods in terms of recognition accuracy, demonstrating its potential as a new benchmark approach for the deep learning and SAR research community
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