All experiments conducted in the paper have corresponding scripts for reproducability inside the repository.
All experiment scripts are located in tools/experiments/*
, with scripts being separated by the different shifts and VideoDA techniques.
Table 2: ImageDA methods on VideoDA benchmarks:
Segformer Backbone:
Experiment | Training Script |
---|---|
HRDA + MIC | ./tools/experiments/viper_csSeq/baselines/viper_csseq_mic_hrda.sh |
HRDA | ./tools/experiments/viper_csSeq/baselines/viper_csseq_hrda.sh |
Target Only | ./tools/experiments/csSeq/supervised/csSeq_supervised_hrda.sh |
Source Only | ./tools/experiments/viper_csSeq/baselines/viper_source_hrda.sh |
DLV2 Backbone:
Experiment | Training Script |
---|---|
HRDA + MIC | ./tools/experiments/viper_csSeq/baselines/viper_csseq_mic_hrda_dlv2.sh |
HRDA | ./tools/experiments/viper_csSeq/pl_refinement/consis/viper_csseq_hrda_dlv2_consis.sh |
Target Only | ./tools/experiments/csSeq/supervised/csSeq_supervised_hrda_dlv2.sh |
Source Only | ./tools/experiments/viper_csSeq/baselines/viper_source_hrda_dlv2.sh |
Table 3: HRDA + MIC Ablation Study
DLV2 Backbone
Experiment | Training Script |
---|---|
HRDA - MRFusion | ./tools/experiments/viper_csSeq/mic_hrda_component_ablation/viper_csseq_hrda_dlv2_no_MRFusion.sh |
HRDA - MRFusion - Rare class sampling | ./tools/experiments/viper_csSeq/mic_hrda_component_ablation/viper_csseq_hrda_dlv2_no_MRFusion_no_rcs.sh |
HRDA - MRFusion - Rare class sampling - ImgNet feature distance reg | ./tools/experiments/viper_csSeq/mic_hrda_component_ablation/viper_csseq_hrda_dlv2_no_MRFusion_no_rcs_no_imnet.sh |
Table 4: Combining existing Video-DA methods with HRDA
DLV2 Backbone:
Experiment | Training Script |
---|---|
Source Only | ./tools/experiments/viper_csSeq/baselines/viper_source_hrda_dlv2.sh |
HRDA | ./tools/experiments/viper_csSeq/baselines/viper_csseq_mic_hrda_dlv2.sh |
(TPS) HRDA + Accel + Consis Mixup + PL Refine Warp Frame | tools/experiments/viper_csSeq/accel/viper_csseq_hrda_dlv2_accel_consis_mixup_warp_frame.sh |
(DAVSN) HRDA + Accel + Consis Mixup + Video Discrim + PL Refine Max confidence | tools/experiments/viper_csSeq/accel/viper_csseq_hrda_dlv2_accel_consis_mixup_video_discrim_consis_filter.sh |
(UDA-VSS) HRDA + Accel + Video Discrim + PL Refine Consis Filter | tools/experiments/viper_csSeq/video_discrim/viper_csseq_hrda_dlv2_video_discrim_consis.sh |
(MOM) HRDA + Accel + Consis Mixup + PL Refine Consis Filter | tools/experiments/viper_csSeq/accel/viper_csseq_hrda_dlv2_accel_consis_mixup_consis_filter.sh |
HRDA + Video Discrim. | ./tools/experiments/viper_csSeq/video_discrim/viper_csseq_hrda_dlv2_video_discrim.sh |
HRDA + Accel + Consis Mixup | tools/experiments/viper_csSeq/accel/viper_csseq_hrda_dlv2_accel_consis_mixup.sh |
HRDA + PL refine Consis Filter | tools/experiments/viper_csSeq/pl_refinement/consis/viper_csseq_mic_hrda_dlv2_consis.sh |
HRDA + Accel + Consis Mixup + Video Discrim | tools/experiments/viper_csSeq/accel/viper_csseq_hrda_dlv2_accel_consis_mixup_video_discrim.sh |
HRDA + Accel + Consis Mixup + Video Discrim + PL Refine Consis Filter | tools/experiments/viper_csSeq/accel/viper_csseq_hrda_dlv2_accel_consis_mixup_video_discrim_consis_filter.sh |
Target Only | ./tools/experiments/csSeq/supervised/csSeq_supervised_hrda_dlv2.sh |
Note: [For Tables 5-8] To train with forward or backwards flow, edit FRAME_OFFSET
(positive values = forward, negative values = backwards) in configs/_base_/datasets/uda_viper_CSSeq.py
along with cs_train_flow_dir
and cs_val_flow_dir
.
Table 5: Psuedo-label Refinement on HRDA + MIC, Segformer Backbone
Segformer Backbone:
Experiment | Training Script |
---|---|
HRDA + MIC + PL Refine Consis Filter | ./tools/experiments/viper_csSeq/pl_refinement/max_conf/viper_csseq_mic_hrda_max_conf.sh |
HRDA + MIC + PL Refine Max Confidence | ./tools/experiments/viper_csSeq/pl_refinement/rare_class_filter/viper_csseq_mic_hrda_rare_class_filter.sh |
HRDA + MIC + PL Refine Warp Frame | ./tools/experiments/viper_csSeq/pl_refinement/warp_frame/viper_csseq_mic_hrda_warp_frame.sh |
HRDA + MIC + PL Refine Oracle | ./tools/experiments/viper_csSeq/pl_refinement/oracle/viper_csseq_mic_hrda_oracle.sh |
Table 6: Psuedo-label Refinement on HRDA, Segformer Backbone
Segformer Backbone:
Experiment | Training Script |
---|---|
HRDA + PL Refine Consis Filter | ./tools/experiments/viper_csSeq/pl_refinement/consis/viper_csseq_hrda_consis.sh |
HRDA + PL Refine Max Confidence | ./tools/experiments/viper_csSeq/pl_refinement/max_conf/viper_csseq_hrda_max_conf.sh |
HRDA + PL Refine Warp Frame | ./tools/experiments/viper_csSeq/pl_refinement/warp_frame/viper_csseq_hrda_warp_frame.sh |
HRDA + PL Refine Oracle | ./tools/experiments/viper_csSeq/pl_refinement/oracle/viper_csseq_hrda_oracle.sh |
Table 7: Psuedo-label Refinement on HRDA + MIC, DLV2 Backbone
DLV2 Backbone:
Experiment | Training Script |
---|---|
HRDA + MIC + PL Refine Consis Filter | ../tools/experiments/viper_csSeq/pl_refinement/consis/viper_csseq_mic_hrda_dlv2_consis.sh |
HRDA + MIC + PL Refine Max Confidence | ./tools/experiments/viper_csSeq/pl_refinement/max_conf/viper_csseq_mic_hrda_dlv2_max_conf.sh |
HRDA + MIC + PL Refine Warp Frame | ./tools/experiments/viper_csSeq/pl_refinement/warp_frame/viper_csseq_mic_hrda_dlv2_warp_frame.sh |
HRDA + MIC + PL Refine Oracle | ./tools/experiments/viper_csSeq/pl_refinement/oracle/viper_csseq_mic_hrda_dlv2_oracle.sh |
Table 8: Psuedo-label Refinement on HRDA, DLV2 Backbone
Segformer Backbone:
Experiment | Training Script |
---|---|
HRDA + PL Refine Consis Filter | ./tools/experiments/viper_csSeq/pl_refinement/consis/viper_csseq_hrda_dlv2_consis.sh |
HRDA + PL Refine Max Confidence | ./tools/experiments/viper_csSeq/pl_refinement/max_conf/viper_csseq_hrda_dlv2_max_conf.sh |
HRDA + PL Refine Warp Frame | ./tools/experiments/viper_csSeq/pl_refinement/warp_frame/viper_csseq_hrda_dlv2_warp_frame.sh |
HRDA + PL Refine Oracle | ./tools/experiments/viper_csSeq/pl_refinement/oracle/viper_csseq_hrda_dlv2_oracle.sh |
SynthiaSeq -> CityscapesSeq, SynthiaSeq -> BDDVid, ViperSeq --> BDDVid experiment scripts follow directory structure as the Viper Experiments. You can find all relevant experiments reported in the paper at tools/experiments/*
.