As pre-processing, each image was center-cropped to produce a square image, and convertedto256 256resolution. In Figure 1, we presentxi (Original) andg(wi)(Projected). Figure 1: Visual comparison between original images and projected images. Weuse the SGD optimizer with the cyclic learning rate scheduling strategyin[10](see Figure 2), momentum0.9,andweightdecay5 For the unseen attacks proposed in [11], we consider attack parameters presented in Table 3. We study how different choices affect the robustness of the trained networks against unseen attacks.

This paper introduces deep synoptic Monte Carlo planning (DSMCP) for large imperfect information games.

We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos.

C provide more ablation studies of our FST, including the ablation on SYNTHIA Cityscapes and evaluation of various segmentation decoders. PASCALVOC2012 [6] consists of21 classes with1,464, 1,449, and 1,456 images for the training, validation, and test set,respectively. Ablation on SYNTHIA.We also provide ablation results on SYNTHIA Cityscapes UDA benchmark andtheresults areshowninTab.S2. The MLP head fuses multi-levelfeatures and upsamples the feature map to predict the segmentation mask, which is designed for Transformer-based segmentation model[36]. We compare our FST with previous state-of-the-art semi-supervised semantic segmentation frameworks, including CCT [22], GCT [16]and CPS [3].

To break through the predicament of seeking supervision only from the past states, we propose future-self-training(FST), which allows the model to learn from itsfuture self. Figure 1b illustrates the concept diagram of our FST. Compared to the conventional ST framework in Figure 1a, which employs thet-step teacher (i.e., updated with the student at moments1,2,...,t 1) to guide the t-step student, FST presents a new training manner by urging thet-step student to learn from the (t+1)-stepteacher.